diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index b588d2e6e..35afbf1c7 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-24T10:12:37","documenter_version":"1.7.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.5","generation_timestamp":"2024-09-25T11:51:34","documenter_version":"1.7.0"}} \ No newline at end of file diff --git a/dev/API/functions/index.html b/dev/API/functions/index.html index 7627739dc..c82d30568 100644 --- a/dev/API/functions/index.html +++ b/dev/API/functions/index.html @@ -15,7 +15,7 @@ - `footnotefont` - `footnotecolor` - `footnotealign` -- `footnotelineheight`source
draw(p::PaginatedLayers; kws...)

Draw each element of PaginatedLayers p and return a Vector{FigureGrid}. Keywords kws are passed to the underlying draw calls.

source
draw(p::PaginatedLayers, i::Int; kws...)

Draw the ith element of PaginatedLayers p and return a FigureGrid. Keywords kws are passed to the underlying draw call.

You can retrieve the number of elements using length(p).

source
AlgebraOfGraphics.draw! — Function
draw!(fig, d::AbstractDrawable, scales::Scales = scales(); [axis, facet])

Draw a AlgebraOfGraphics.AbstractDrawable object d on fig. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. fig can be a figure, a position in a layout, or an axis if d has no facet specification. The output can be customized by passing named tuples or dictionaries with settings via the axis or facet keywords.

source
AlgebraOfGraphics.colorbar! — Function
colorbar!(figpos, grid; kwargs...)

Compute colorbar for grid (which should be the output of draw!) and draw it in position figpos. Attributes allowed in kwargs are the same as MakieLayout.Colorbar.

source
AlgebraOfGraphics.legend! — Function
legend!(figpos, grid; order = nothing, kwargs...)

Compute legend for grid (which should be the output of draw!) and draw it in position figpos. All kwargs are forwarded to Makie's Legend constructor.

The order of scales represented in the legend can be changed with the order keyword. By default, legend sections are ordered the same as they appear in the plot specification. Assuming three scales Color, MarkerSize and custom exist in a spec, you can pass a vector to reorder them like [:MarkerSize, :custom, :Color], or merge multiple entries together with a nested vector like [[:MarkerSize, :custom], :Color], or give merged sections a title with the pair syntax [[:MarkerSize, :custom] => "Merged group", :Color].

source
AlgebraOfGraphics.paginate — Function
paginate(l; layout=nothing, row=nothing, col=nothing)

Paginate l, the Layer or Layers object created by an AlgebraOfGraphics spec, to create a PaginatedLayers object. This contains a vector of layers where each layer operates on a subset of the input data.

The PaginatedLayers object can be passed to draw which will return a Vector{FigureGrid} rather than a single figure.

The keywords that limit the number of subplots on each page are the same that are used to specify facets in mapping:

  • layout: Maximum number of subplots in a wrapped linear layout.
  • row: Maximum number of rows in a 2D layout.
  • col: Maximum number of columns in a 2D layout.

Example

d = data((
+- `footnotelineheight`
source
draw(p::PaginatedLayers; kws...)

Draw each element of PaginatedLayers p and return a Vector{FigureGrid}. Keywords kws are passed to the underlying draw calls.

source
draw(p::PaginatedLayers, i::Int; kws...)

Draw the ith element of PaginatedLayers p and return a FigureGrid. Keywords kws are passed to the underlying draw call.

You can retrieve the number of elements using length(p).

source
AlgebraOfGraphics.draw! — Function
draw!(fig, d::AbstractDrawable, scales::Scales = scales(); [axis, facet])

Draw a AlgebraOfGraphics.AbstractDrawable object d on fig. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. fig can be a figure, a position in a layout, or an axis if d has no facet specification. The output can be customized by passing named tuples or dictionaries with settings via the axis or facet keywords.

source
AlgebraOfGraphics.colorbar! — Function
colorbar!(figpos, grid; kwargs...)

Compute colorbar for grid (which should be the output of draw!) and draw it in position figpos. Attributes allowed in kwargs are the same as MakieLayout.Colorbar.

source
AlgebraOfGraphics.legend! — Function
legend!(figpos, grid; order = nothing, kwargs...)

Compute legend for grid (which should be the output of draw!) and draw it in position figpos. All kwargs are forwarded to Makie's Legend constructor.

The order of scales represented in the legend can be changed with the order keyword. By default, legend sections are ordered the same as they appear in the plot specification. Assuming three scales Color, MarkerSize and custom exist in a spec, you can pass a vector to reorder them like [:MarkerSize, :custom, :Color], or merge multiple entries together with a nested vector like [[:MarkerSize, :custom], :Color], or give merged sections a title with the pair syntax [[:MarkerSize, :custom] => "Merged group", :Color].

source
AlgebraOfGraphics.paginate — Function
paginate(l; layout=nothing, row=nothing, col=nothing)

Paginate l, the Layer or Layers object created by an AlgebraOfGraphics spec, to create a PaginatedLayers object. This contains a vector of layers where each layer operates on a subset of the input data.

The PaginatedLayers object can be passed to draw which will return a Vector{FigureGrid} rather than a single figure.

The keywords that limit the number of subplots on each page are the same that are used to specify facets in mapping:

  • layout: Maximum number of subplots in a wrapped linear layout.
  • row: Maximum number of rows in a 2D layout.
  • col: Maximum number of columns in a 2D layout.

Example

d = data((
     x = rand(1000),
     y = rand(1000),
     group1 = rand(string.('a':'i'), 1000),
@@ -28,7 +28,7 @@
 
 layer_2 = d * mapping(:x, :y, row = :group1, col = :group2) * visual(Scatter)
 paginated_2 = paginate(layer_2, row = 4, col = 3)
-figuregrid = draw(paginated_2, 1) # draw only the first grid
source
AlgebraOfGraphics.scales — Function
scales(; kwargs...)

Create a Scales object containing properties for aesthetic scales that can be passed to draw and draw!. Each keyword should be the name of a scale in the spec that is being drawn. That can either be a default one like Color, Marker or LineStyle, or a custom scale name defined in a mapping using the scale function.

The values attached to the keywords must be dict-like, with Symbols as keys (such as NamedTuples).

source

Mapping helpers

AlgebraOfGraphics.pregrouped — Function
pregrouped(positional...; named...)

Equivalent to data(Pregrouped()) * mapping(positional...; named...). Refer to mapping for more information.

source
AlgebraOfGraphics.direct — Function
direct(x)

Return DirectData(x) which marks x for direct use in a mapping that's used with a table-like data source. As a result, x will be used directly as data, without lookup in the table. If x is not an AbstractArray, it will be expanded like fill(x, n) where n is the number of rows in the data source.

source
AlgebraOfGraphics.renamer — Function
renamer(arr::Union{AbstractArray, Tuple})

Utility to rename a categorical variable, as in renamer([value1 => label1, value2 => label2]). The keys of all pairs should be all the unique values of the categorical variable and the values should be the corresponding labels. The order of arr is respected in the legend.

Examples

julia> r = renamer(["class 1" => "Class One", "class 2" => "Class Two"])
+figuregrid = draw(paginated_2, 1) # draw only the first grid
source
AlgebraOfGraphics.scales — Function
scales(; kwargs...)

Create a Scales object containing properties for aesthetic scales that can be passed to draw and draw!. Each keyword should be the name of a scale in the spec that is being drawn. That can either be a default one like Color, Marker or LineStyle, or a custom scale name defined in a mapping using the scale function.

The values attached to the keywords must be dict-like, with Symbols as keys (such as NamedTuples).

source

Mapping helpers

AlgebraOfGraphics.pregrouped — Function
pregrouped(positional...; named...)

Equivalent to data(Pregrouped()) * mapping(positional...; named...). Refer to mapping for more information.

source
AlgebraOfGraphics.direct — Function
direct(x)

Return DirectData(x) which marks x for direct use in a mapping that's used with a table-like data source. As a result, x will be used directly as data, without lookup in the table. If x is not an AbstractArray, it will be expanded like fill(x, n) where n is the number of rows in the data source.

source
AlgebraOfGraphics.renamer — Function
renamer(arr::Union{AbstractArray, Tuple})

Utility to rename a categorical variable, as in renamer([value1 => label1, value2 => label2]). The keys of all pairs should be all the unique values of the categorical variable and the values should be the corresponding labels. The order of arr is respected in the legend.

Examples

julia> r = renamer(["class 1" => "Class One", "class 2" => "Class Two"])
 AlgebraOfGraphics.Renamer{Vector{String}, Vector{String}}(["class 1", "class 2"], ["Class One", "Class Two"])
 
 julia> println(r("class 1"))
@@ -40,10 +40,10 @@
 AlgebraOfGraphics.Renamer{Nothing, Vector{String}}(nothing, ["Class One", "Class Two"])
 
 julia> println(r(2))
-Class Two
source
AlgebraOfGraphics.sorter — Function
sorter(ks)

Utility to reorder a categorical variable, as in sorter(["low", "medium", "high"]). A vararg method sorter("low", "medium", "high") is also supported. ks should include all the unique values of the categorical variable. The order of ks is respected in the legend.

source
AlgebraOfGraphics.nonnumeric — Function
nonnumeric(x)

Transform x into a non numeric type that is printed and sorted in the same way.

source
AlgebraOfGraphics.verbatim — Function
verbatim(x)

Signal that x should not be rescaled, but used in the plot as is.

source
AlgebraOfGraphics.scale — Function
scale(id::Symbol)

Create a ScaleID object that can be used in a mapping to assign a custom id to the mapped variable. This variable will then not be merged into the default scale for its aesthetic type, but instead be handled separately, leading to a separate legend entry.

source
AlgebraOfGraphics.presorted — Function
presorted(x)

Use within a pair expression in mapping to signal that a categorical column from the data source should be used in the original order and not automatically sorted.

Example:

# normally, categories would be sorted a, b, c but with `presorted`
+Class Two
source
AlgebraOfGraphics.sorter — Function
sorter(ks)

Utility to reorder a categorical variable, as in sorter(["low", "medium", "high"]). A vararg method sorter("low", "medium", "high") is also supported. ks should include all the unique values of the categorical variable. The order of ks is respected in the legend.

source
AlgebraOfGraphics.nonnumeric — Function
nonnumeric(x)

Transform x into a non numeric type that is printed and sorted in the same way.

source
AlgebraOfGraphics.verbatim — Function
verbatim(x)

Signal that x should not be rescaled, but used in the plot as is.

source
AlgebraOfGraphics.scale — Function
scale(id::Symbol)

Create a ScaleID object that can be used in a mapping to assign a custom id to the mapped variable. This variable will then not be merged into the default scale for its aesthetic type, but instead be handled separately, leading to a separate legend entry.

source
AlgebraOfGraphics.presorted — Function
presorted(x)

Use within a pair expression in mapping to signal that a categorical column from the data source should be used in the original order and not automatically sorted.

Example:

# normally, categories would be sorted a, b, c but with `presorted`
 # they stay in the order b, c, a
 
-data((; some_column = ["b", "c", "a"])) * mapping(:some_column => presorted)
source

Theming

AlgebraOfGraphics.set_aog_theme! — Function
set_aog_theme!(; kwargs...)

Set the current theme to a predefined and opinionated theme, as defined by the unexported internal function AlgebraOfGraphics.aog_theme.

To tweak the predefined theme, use the function Makie.update_theme!. See the example below on how to change, e.g., default fontsize, title, and markersize.

For more information about setting themes, see the Theming section of the Makie.jl docs.

Examples

julia> using CairoMakie, AlgebraOfGraphics
+data((; some_column = ["b", "c", "a"])) * mapping(:some_column => presorted)
source

Theming

AlgebraOfGraphics.set_aog_theme! — Function
set_aog_theme!(; kwargs...)

Set the current theme to a predefined and opinionated theme, as defined by the unexported internal function AlgebraOfGraphics.aog_theme.

To tweak the predefined theme, use the function Makie.update_theme!. See the example below on how to change, e.g., default fontsize, title, and markersize.

For more information about setting themes, see the Theming section of the Makie.jl docs.

Examples

julia> using CairoMakie, AlgebraOfGraphics
 
 julia> set_aog_theme!()                # Sets a prefedined theme
 
@@ -51,4 +51,4 @@
            fontsize=30,
            markersize=40,
            Axis=(title="MyDefaultTitle",)
-       )
source
AlgebraOfGraphics.aog_theme — Function
aog_theme(; fonts=[firasans("Medium"), firasans("Light")])

Return a NamedTuple of theme settings. Intended for internal use. The provided functionality is exposed to the user by the function set_aog_theme!.

source
AlgebraOfGraphics.from_continuous — Function
from_continuous(x)

Mark a colormap as continuous such that AlgebraOfGraphics will sample a categorical palette from start to end in n steps, and not by using the first n colors.

You could also use cgrad(colormap, n; categorical = true), however, this requires you to specify how many levels there are, which from_continuous detects automatically.

Example:

draw(scales(Color = (; palette = from_continuous(:viridis))))
source

Ticks helpers

AlgebraOfGraphics.datetimeticks — Function
datetimeticks(datetimes::AbstractVector{<:TimeType}, labels::AbstractVector{<:AbstractString})

Generate ticks matching datetimes to the corresponding labels. The result can be passed to xticks, yticks, or zticks.

source
datetimeticks(f, datetimes::AbstractVector{<:TimeType})

Compute ticks for the given datetimes using a formatting function f. The result can be passed to xticks, yticks, or zticks.

source

Internal functions

AlgebraOfGraphics.scientific_eltype — Function
scientific_eltype(v)

Determine whether v should be treated as a continuous, geometrical, or categorical array.

source
AlgebraOfGraphics.scientific_type — Function
scientific_type(T::Type)

Determine whether T represents a continuous, geometrical, or categorical variable.

source
AlgebraOfGraphics.plottypes_attributes — Function
plottypes_attributes(entries)

Return plottypes and relative attributes, as two vectors of the same length, for the given entries.

source
AlgebraOfGraphics.compute_attributes — Function
compute_attributes(pl::ProcessedLayer, categoricalscales, continuousscales_grid, continuousscales)

Process attributes of a ProcessedLayer. In particular,

  • remove AlgebraOfGraphics-specific layout attributes,
  • opt out of Makie cycling mechanism,
  • customize behavior of color (implementing alpha transparency),
  • customize behavior of bar width (default to one unit when not specified),
  • set correct colorrange.

Return computed attributes.

source
+ )source
AlgebraOfGraphics.aog_theme — Function
aog_theme(; fonts=[firasans("Medium"), firasans("Light")])

Return a NamedTuple of theme settings. Intended for internal use. The provided functionality is exposed to the user by the function set_aog_theme!.

source
AlgebraOfGraphics.from_continuous — Function
from_continuous(x)

Mark a colormap as continuous such that AlgebraOfGraphics will sample a categorical palette from start to end in n steps, and not by using the first n colors.

You could also use cgrad(colormap, n; categorical = true), however, this requires you to specify how many levels there are, which from_continuous detects automatically.

Example:

draw(scales(Color = (; palette = from_continuous(:viridis))))
source

Ticks helpers

AlgebraOfGraphics.datetimeticks — Function
datetimeticks(datetimes::AbstractVector{<:TimeType}, labels::AbstractVector{<:AbstractString})

Generate ticks matching datetimes to the corresponding labels. The result can be passed to xticks, yticks, or zticks.

source
datetimeticks(f, datetimes::AbstractVector{<:TimeType})

Compute ticks for the given datetimes using a formatting function f. The result can be passed to xticks, yticks, or zticks.

source

Internal functions

AlgebraOfGraphics.scientific_eltype — Function
scientific_eltype(v)

Determine whether v should be treated as a continuous, geometrical, or categorical array.

source
AlgebraOfGraphics.scientific_type — Function
scientific_type(T::Type)

Determine whether T represents a continuous, geometrical, or categorical variable.

source
AlgebraOfGraphics.plottypes_attributes — Function
plottypes_attributes(entries)

Return plottypes and relative attributes, as two vectors of the same length, for the given entries.

source
AlgebraOfGraphics.compute_attributes — Function
compute_attributes(pl::ProcessedLayer, categoricalscales, continuousscales_grid, continuousscales)

Process attributes of a ProcessedLayer. In particular,

  • remove AlgebraOfGraphics-specific layout attributes,
  • opt out of Makie cycling mechanism,
  • customize behavior of color (implementing alpha transparency),
  • customize behavior of bar width (default to one unit when not specified),
  • set correct colorrange.

Return computed attributes.

source
diff --git a/dev/API/recipes/index.html b/dev/API/recipes/index.html index d59e22d8f..8be7a8bd7 100644 --- a/dev/API/recipes/index.html +++ b/dev/API/recipes/index.html @@ -27,11 +27,11 @@ strokecolormap :batlow strokewidth 0 transparency false - visible truesource
AlgebraOfGraphics.linesfill — Function
linesfill(xs, ys; lower, upper, attributes...)

Line plot with a shaded area between lower and upper. If lower and upper are not given, shaded area is between 0 and ys.

Attributes

Available attributes and their defaults for Plot{AlgebraOfGraphics.linesfill} are: 

  color       :gray25
+  visible             true
source
AlgebraOfGraphics.linesfill — Function
linesfill(xs, ys; lower, upper, attributes...)

Line plot with a shaded area between lower and upper. If lower and upper are not given, shaded area is between 0 and ys.

Attributes

Available attributes and their defaults for Plot{AlgebraOfGraphics.linesfill} are: 

  color       :gray25
   colormap    :batlow
   colorrange  MakieCore.Automatic()
   fillalpha   0.15
   linestyle   "nothing"
   linewidth   1.5
   lower       MakieCore.Automatic()
-  upper       MakieCore.Automatic()
source
+ upper MakieCore.Automatic()source diff --git a/dev/API/types/index.html b/dev/API/types/index.html index 7138ce913..88d389236 100644 --- a/dev/API/types/index.html +++ b/dev/API/types/index.html @@ -1,2 +1,2 @@ -Types · Algebra of Graphics
GitHub

Types

AlgebraOfGraphics.Layer — Type
Layer(transformation, data, positional::AbstractVector, named::AbstractDictionary)

Algebraic object encoding a single layer of a visualization. It is composed of a dataset, positional and named arguments, as well as a transformation to be applied to those. Layer objects can be multiplied, yielding a novel Layer object, or added, yielding a AlgebraOfGraphics.Layers object.

source
AlgebraOfGraphics.ProcessedLayer — Type
ProcessedLayer(l::Layer)

Process a Layer and return the resulting ProcessedLayer.

Note that this method should not be used anymore as processing a Layer can now potentially return multiple ProcessedLayer objects. Therefore, you should use the plural form ProcessedLayers(layer).

source
AlgebraOfGraphics.Entry — Type
Entry(plottype::PlotType, positional::Arguments, named::NamedArguments)

Define plottype as well as positional and named arguments for a single plot.

source
AlgebraOfGraphics.AxisEntries — Type
AxisEntries(axis::Union{Axis, Nothing}, entries::Vector{Entry}, categoricalscales, continuousscales)

Define all ingredients to make plots on an axis. Each categorical scale should be a CategoricalScale, and each continuous scale should be a ContinuousScale.

source
+Types · Algebra of Graphics
GitHub

Types

AlgebraOfGraphics.Layer — Type
Layer(transformation, data, positional::AbstractVector, named::AbstractDictionary)

Algebraic object encoding a single layer of a visualization. It is composed of a dataset, positional and named arguments, as well as a transformation to be applied to those. Layer objects can be multiplied, yielding a novel Layer object, or added, yielding a AlgebraOfGraphics.Layers object.

source
AlgebraOfGraphics.ProcessedLayer — Type
ProcessedLayer(l::Layer)

Process a Layer and return the resulting ProcessedLayer.

Note that this method should not be used anymore as processing a Layer can now potentially return multiple ProcessedLayer objects. Therefore, you should use the plural form ProcessedLayers(layer).

source
AlgebraOfGraphics.Entry — Type
Entry(plottype::PlotType, positional::Arguments, named::NamedArguments)

Define plottype as well as positional and named arguments for a single plot.

source
AlgebraOfGraphics.AxisEntries — Type
AxisEntries(axis::Union{Axis, Nothing}, entries::Vector{Entry}, categoricalscales, continuousscales)

Define all ingredients to make plots on an axis. Each categorical scale should be a CategoricalScale, and each continuous scale should be a ContinuousScale.

source
diff --git a/dev/FAQs/a7f403b1.svg b/dev/FAQs/76271201.svg similarity index 84% rename from dev/FAQs/a7f403b1.svg rename to dev/FAQs/76271201.svg index 425769c6f..272bf9227 100644 --- a/dev/FAQs/a7f403b1.svg +++ b/dev/FAQs/76271201.svg @@ -2,170 +2,173 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - + + + + + + + + + + - - + + diff --git a/dev/FAQs/3cef2c52.svg b/dev/FAQs/a024ef18.svg similarity index 84% rename from dev/FAQs/3cef2c52.svg rename to dev/FAQs/a024ef18.svg index 500a8bde5..b99cd0574 100644 --- a/dev/FAQs/3cef2c52.svg +++ b/dev/FAQs/a024ef18.svg @@ -2,124 +2,118 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - + @@ -128,166 +122,166 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - + + + + + + + + + + @@ -304,8 +298,8 @@ - - + + diff --git a/dev/FAQs/a9834f9c.svg b/dev/FAQs/a9834f9c.svg deleted file mode 100644 index a021dd67c..000000000 --- a/dev/FAQs/a9834f9c.svg +++ /dev/null @@ -1,169 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/FAQs/f2bb89f3.svg b/dev/FAQs/b106064b.svg similarity index 84% rename from dev/FAQs/f2bb89f3.svg rename to dev/FAQs/b106064b.svg index 3996badd7..2923bbeb8 100644 --- a/dev/FAQs/f2bb89f3.svg +++ b/dev/FAQs/b106064b.svg @@ -2,79 +2,82 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + - + - + - + - + - + @@ -82,102 +85,102 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - + + + + + + + + - - - + + + diff --git a/dev/FAQs/c5f7601a.svg b/dev/FAQs/c5f7601a.svg new file mode 100644 index 000000000..f3d986d73 --- /dev/null +++ b/dev/FAQs/c5f7601a.svg @@ -0,0 +1,124 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/FAQs/5cac156c.svg b/dev/FAQs/c91067ee.svg similarity index 60% rename from dev/FAQs/5cac156c.svg rename to dev/FAQs/c91067ee.svg index 925a1e30e..56ae2ed93 100644 --- a/dev/FAQs/5cac156c.svg +++ b/dev/FAQs/c91067ee.svg @@ -2,73 +2,73 @@ - + - + - + - + - + - + - + - - - - + - + + + + - - + + - - + + - + - + - + - + - + - + - + - - + + - + - + - + @@ -76,99 +76,99 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - + + + + + - - - - + + + + diff --git a/dev/FAQs/5b443135.svg b/dev/FAQs/ee7f3e14.svg similarity index 88% rename from dev/FAQs/5b443135.svg rename to dev/FAQs/ee7f3e14.svg index a042afed4..f2184ec6a 100644 --- a/dev/FAQs/5b443135.svg +++ b/dev/FAQs/ee7f3e14.svg @@ -2,127 +2,127 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -131,172 +131,172 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + diff --git a/dev/FAQs/index.html b/dev/FAQs/index.html index 481c7ff60..33b45e02e 100644 --- a/dev/FAQs/index.html +++ b/dev/FAQs/index.html @@ -3,19 +3,19 @@ using AlgebraOfGraphics: density df = (x = exp.(randn(1000)),) kde1 = data(df) * mapping(:x) * density() -draw(kde1, axis=(width=225, height=225, xscale=log,))Example block output
df = (x = exp.(randn(1000)),)
+draw(kde1, axis=(width=225, height=225, xscale=log,))
Example block output
df = (x = exp.(randn(1000)),)
 kde2 = data(df) * mapping(:x => log => "log(x)") * density()
-draw(kde2, axis=(width=225, height=225))
Example block output

The two plots look different. The first represents the pdf of x in a log scale, while the second represents the pdf of log(x) in a linear scale. The two curves differ by a factor 1 / x, the derivative of log(x). See e.g. this post for some mathematical background on the topic.

In general, the second approach (plotting the density of log(x)) could be considered more principled, as it preserves the proportionality between area and probability mass. On the contrary, the first approach (plotting the density of x in a log scale) breaks this proportionality relationship.

A similar reasoning applies to histograms:

using AlgebraOfGraphics
+draw(kde2, axis=(width=225, height=225))
Example block output

The two plots look different. The first represents the pdf of x in a log scale, while the second represents the pdf of log(x) in a linear scale. The two curves differ by a factor 1 / x, the derivative of log(x). See e.g. this post for some mathematical background on the topic.

In general, the second approach (plotting the density of log(x)) could be considered more principled, as it preserves the proportionality between area and probability mass. On the contrary, the first approach (plotting the density of x in a log scale) breaks this proportionality relationship.

A similar reasoning applies to histograms:

using AlgebraOfGraphics
 df = (x = exp.(rand(1000)),)
 hist1 = data(df) * mapping(:x) * histogram()
-draw(hist1, axis=(width=225, height=225, xscale=log))
Example block output
df = (x = exp.(rand(1000)),)
+draw(hist1, axis=(width=225, height=225, xscale=log))
Example block output
df = (x = exp.(rand(1000)),)
 hist2 = data(df) * mapping(:x => log => "log(x)") * histogram()
-draw(hist2, axis=(width=225, height=225))
Example block output

The data transformation approach is preferable as it produces uniform bins, which are easier to interpret.

How to combine AlgebraOfGraphics with plain Makie plots?

Since AlgebraOfGraphics is built upon the Makie ecosystem we can easily combine plots from both packages. Two approaches can be taken. Firstly, by using draw! you can pass a Figure or FigurePosition created by Makie to be used by AlgebraOfGraphics, e.g.

f, a, p = lines(0..2pi, sin; figure = (size = (600, 400),))
+draw(hist2, axis=(width=225, height=225))
Example block output

The data transformation approach is preferable as it produces uniform bins, which are easier to interpret.

How to combine AlgebraOfGraphics with plain Makie plots?

Since AlgebraOfGraphics is built upon the Makie ecosystem we can easily combine plots from both packages. Two approaches can be taken. Firstly, by using draw! you can pass a Figure or FigurePosition created by Makie to be used by AlgebraOfGraphics, e.g.

f, a, p = lines(0..2pi, sin; figure = (size = (600, 400),))
 
 df = (x = exp.(rand(1000)),)
 hist1 = data(df) * mapping(:x => log => "log(x)") * histogram()
-draw!(f[1, 2], hist1)
Example block output

Alternatively, we can create the AlgebraOfGraphics figure first and then add in additional plain Makie axes alongside the result by accessing the .figure field of fg, e.g.

df = (x = exp.(rand(1000)),)
+draw!(f[1, 2], hist1)
Example block output

Alternatively, we can create the AlgebraOfGraphics figure first and then add in additional plain Makie axes alongside the result by accessing the .figure field of fg, e.g.

df = (x = exp.(rand(1000)),)
 hist2 = data(df) * mapping(:x => log => "log(x)") * histogram()
 fg = draw(hist2; figure = (size = (600, 400),))
 
-lines(fg.figure[1, 2], 0..2pi, cos)
Example block output
Note

When setting the width and height dimensions of each axis manually you will need to call resize_to_layout!(fg) before displaying the figure such that each axis is sized correctly.

+lines(fg.figure[1, 2], 0..2pi, cos)Example block output
Note

When setting the width and height dimensions of each axis manually you will need to call resize_to_layout!(fg) before displaying the figure such that each axis is sized correctly.

diff --git a/dev/changelog/index.html b/dev/changelog/index.html index a4fc910e5..06ead3371 100644 --- a/dev/changelog/index.html +++ b/dev/changelog/index.html @@ -1,2 +1,2 @@ -Changelog · Algebra of Graphics
GitHub

Changelog

Unreleased

v0.8.10 -2024-09-24

  • Fixed markercolor in ScatterLines legends when it did not match color #567.

v0.8.9 - 2024-09-24

  • Added ability to include layers in the legend without using scales by adding visual(label = "some label") #565.

v0.8.8 - 2024-09-17

  • Fixed aesthetics of errorbar so that x and y stay labelled correctly when using direction = :x #560.
  • Added ability to specify title, subtitle and footnotes plus settings in the draw function #556.
  • Added dodge_x and dodge_y keywords to mapping that allow to dodge any plot types that have AesX or AesY data #558.

v0.8.7 - 2024-09-06

  • Added ability to return ProcessedLayers from transformations, thereby enabling multi-layer transformations, such as scatter plus errorbars #549.
  • Fixed bug where mergesorted applied on string vectors used isless instead of natural sort #553.

v0.8.6 - 2024-09-02

  • Added bar_labels to BarPlot's aesthetic mapping #544.
  • Added ability to hide legend or colorbar by passing, e.g., legend = (; show = false) to draw #547.

v0.8.5 - 2024-08-27

  • Added presorted helper function to keep categorical data in the order encountered in the source table, instead of sorting it alphabetically #529.
  • Added from_continuous helper function which allows to sample continuous colormaps evenly to use them as categorical palettes without having to specify how many categories there are #541.

v0.8.4 - 2024-08-26

  • Added fillto to BarPlot aesthetics #535.
  • Fixed bug when giving datalimits of density as a (low, high) tuple #536.
  • Fixed bug where facet-local continuous scale limits were used instead of the globally merged ones, possibly leading to mismatches between data and legend #539.

v0.8.3 - 2024-08-23

  • Fixed incorrect x/y axis assignment for the violin plot type #528.

v0.8.2 - 2024-08-21

  • Enable use of LaTeXStrings and rich text in renamer #525.
  • Fixed widths of boxplots with color groupings #524.

v0.8.1 - 2024-08-20

  • Added back support for Hist, CrossBar, ECDFPlot and Density #522.

v0.8.0 - 2024-07-26

  • Breaking: Columns with element types of Union{Missing,T} are not treated as categorical by default anymore, instead T decides if data is seen as categorical, continuous or geometrical. If you relied on numerical vectors with missings being treated as categorical, you can use :columnname => nonnumeric in the mapping instead.
  • Breaking: AbstractString categories are now sorted with natural sort order by default. This means that where you got ["1", "10", "2"] before, you now get ["1", "2", "10"]. You can use sorter, the categories keyword or categorical arrays to sort your data differently if needed.

v0.7.0 - 2024-07-16

  • Breaking: The palette keyword of draw linking palettes to keyword arguments was removed. Instead, palettes need to be passed to specific scales like draw(..., scales(Color = (; palette = :Set1_3)))
  • Breaking: All recipes need to have the new function aesthetic_mapping defined for all sets of positional arguments that should be supported, as can be seen in src/aesthetics.jl. This breaks usage of all custom recipes. Additionally, not all Makie plots have been ported to the new system yet. If you encounter missing plots, or missing attributes of already ported plots, please open an issue.
  • Breaking: All custom recipes that should be displayed in a legend, need to have legend_elements(P, attributes, scale_args) defined as can be seen in src/guides/legend.jl. AlgebraOfGraphics cannot use the same default mechanism as Makie, which can create a legend from an existing plot, because AlgebraOfGraphics needs to create the legend before the plot is instantiated.
  • Breaking: Pregrouped data cannot be passed anymore to the plain mapping(...) without any data(tabular). Instead, you should use pregrouped(...) which is a shortcut for data(Pregrouped()) * mapping(...).
  • Breaking: Contour and Contourf generally do not work anymore with visual(). Instead, the contours() and filled_contours() analyses should be used. Contour can still be used with categorical colors, but not with continuous ones.
  • Breaking: All colormap properties for continuous color scales need to be passed via scales now, and not through visual. This is to have central control over the scale as it can be used by multiple visuals simultaneously.
  • Horizontal barplots, violins, errorbars, rangebars and other plot types that have two different orientations work correctly now. Axis labels switch accordingly when the orientation is changed.
  • Plotting functions whose positional arguments don't correspond to X, Y, Z work correctly now. For example, HLines (1 => Y) or rangebars (1 => X, 2 => Y, 3 => Y).
  • It is possible to add categories beyond those present in the data with the categories keyword within a scale's settings. It is also possible to reorder or otherwise transform the existing categories by passing a function to categories.
  • The supported attributes are not limited anymore to a specific set of names, for example, strokecolor can work the same as color did before, and the two can share a scale via their shared aesthetic type.
  • There can be multiple scales of the same aesthetic now. This allows to have separate legends for different plot types using the same aesthetics. Scale separation works by pairing a variable in mapping with a scale(id_symbol).
  • Legend entries can be reordered using the legend = (; order = ...) option in draw. Specific scales can opt out of the legend by passing legend = false in scales.
  • Labels can now be anything that Makie supports, primarily Strings, LaTeXStrings or rich text.
  • Legend elements now usually reflect all attributes set in their corresponding visual.
  • Simple column vectors of data can now be passed directly to mapping without using data first. Additionally, scalar values are accepted as a shortcut for columns with the same repeated value.
  • Columns from outside a table source in data can now be passed to mapping by wrapping them in the direct function. Scalar values are accepted as a shortcut for columns with the same repeated value. For example, to create a label for columns x and y from a dataframe passed to data, one could now do mapping(:x, :y, color = direct("label")) without having to create a column full of "label" strings first.
  • The numbers at which categorical values are plotted on x and y axis can now be changed via scales(X = (; palette = [1, 2, 4])) or similar.
  • Continuous marker size scales can now be shown in the legend. Numerical values are proportional to area and not diameter now, which makes more sense with respect to human perception. The min and max marker size can be set using the sizerange property for the respective scale in scales.

v0.6.11 - 2022-08-08

  • Added paginate for pagination of large facet plots.

v0.6.8 - 2022-06-14

  • Added choropleth recipe to supersede geodata for geographical data.

v0.6.1 - 2022-01-28

  • Support level in linear analysis for confidence interval.
  • Replaced tuples and named tuples in Layer and Entry with dictionaries from Dictionaries.jl.
  • Split internal Entry type into ProcessedLayer (to be used for analyses) and Entry (to be used for plotting).

v0.6.0 - 2021-10-24

  • Breaking: Default axis linking behavior has changed: now only axes corresponding to the same variable are linked. For consistency with row/col, layout will hide decorations of linked axes and span axis labels if appropriate.
  • Customizable legend and colorbar position and look.
  • Customizable axis linking behavior.

v0.5 - 2021-08-05

  • Breaking: Axis(ae) has been replaced by ae.axis.
  • Breaking: Legend(fg) has been replaced by legend!(fg) and colorbar!(fg).
  • legend! and colorbar! API allows for custom legend placement.

v0.4 - 2021-05-21

  • Breaking: Removed deprecations for style and spec (now only mapping and visual are allowed).
  • Breaking: Analyses now require parentheses (i.e. linear() instead of linear).
  • Breaking: Rename layout_x and layout_y to col and row.
  • Breaking: Rename wts keyword argument to weights.
  • Breaking: categorical has been replaced by nonnumeric.
+Changelog · Algebra of Graphics
GitHub

Changelog

Unreleased

v0.8.11 - 2024-09-25

  • Fixed lexicographic natural sorting of tuples (this would fall back to default sort order before) #568.

v0.8.10 - 2024-09-24

  • Fixed markercolor in ScatterLines legends when it did not match color #567.

v0.8.9 - 2024-09-24

  • Added ability to include layers in the legend without using scales by adding visual(label = "some label") #565.

v0.8.8 - 2024-09-17

  • Fixed aesthetics of errorbar so that x and y stay labelled correctly when using direction = :x #560.
  • Added ability to specify title, subtitle and footnotes plus settings in the draw function #556.
  • Added dodge_x and dodge_y keywords to mapping that allow to dodge any plot types that have AesX or AesY data #558.

v0.8.7 - 2024-09-06

  • Added ability to return ProcessedLayers from transformations, thereby enabling multi-layer transformations, such as scatter plus errorbars #549.
  • Fixed bug where mergesorted applied on string vectors used isless instead of natural sort #553.

v0.8.6 - 2024-09-02

  • Added bar_labels to BarPlot's aesthetic mapping #544.
  • Added ability to hide legend or colorbar by passing, e.g., legend = (; show = false) to draw #547.

v0.8.5 - 2024-08-27

  • Added presorted helper function to keep categorical data in the order encountered in the source table, instead of sorting it alphabetically #529.
  • Added from_continuous helper function which allows to sample continuous colormaps evenly to use them as categorical palettes without having to specify how many categories there are #541.

v0.8.4 - 2024-08-26

  • Added fillto to BarPlot aesthetics #535.
  • Fixed bug when giving datalimits of density as a (low, high) tuple #536.
  • Fixed bug where facet-local continuous scale limits were used instead of the globally merged ones, possibly leading to mismatches between data and legend #539.

v0.8.3 - 2024-08-23

  • Fixed incorrect x/y axis assignment for the violin plot type #528.

v0.8.2 - 2024-08-21

  • Enable use of LaTeXStrings and rich text in renamer #525.
  • Fixed widths of boxplots with color groupings #524.

v0.8.1 - 2024-08-20

  • Added back support for Hist, CrossBar, ECDFPlot and Density #522.

v0.8.0 - 2024-07-26

  • Breaking: Columns with element types of Union{Missing,T} are not treated as categorical by default anymore, instead T decides if data is seen as categorical, continuous or geometrical. If you relied on numerical vectors with missings being treated as categorical, you can use :columnname => nonnumeric in the mapping instead.
  • Breaking: AbstractString categories are now sorted with natural sort order by default. This means that where you got ["1", "10", "2"] before, you now get ["1", "2", "10"]. You can use sorter, the categories keyword or categorical arrays to sort your data differently if needed.

v0.7.0 - 2024-07-16

  • Breaking: The palette keyword of draw linking palettes to keyword arguments was removed. Instead, palettes need to be passed to specific scales like draw(..., scales(Color = (; palette = :Set1_3)))
  • Breaking: All recipes need to have the new function aesthetic_mapping defined for all sets of positional arguments that should be supported, as can be seen in src/aesthetics.jl. This breaks usage of all custom recipes. Additionally, not all Makie plots have been ported to the new system yet. If you encounter missing plots, or missing attributes of already ported plots, please open an issue.
  • Breaking: All custom recipes that should be displayed in a legend, need to have legend_elements(P, attributes, scale_args) defined as can be seen in src/guides/legend.jl. AlgebraOfGraphics cannot use the same default mechanism as Makie, which can create a legend from an existing plot, because AlgebraOfGraphics needs to create the legend before the plot is instantiated.
  • Breaking: Pregrouped data cannot be passed anymore to the plain mapping(...) without any data(tabular). Instead, you should use pregrouped(...) which is a shortcut for data(Pregrouped()) * mapping(...).
  • Breaking: Contour and Contourf generally do not work anymore with visual(). Instead, the contours() and filled_contours() analyses should be used. Contour can still be used with categorical colors, but not with continuous ones.
  • Breaking: All colormap properties for continuous color scales need to be passed via scales now, and not through visual. This is to have central control over the scale as it can be used by multiple visuals simultaneously.
  • Horizontal barplots, violins, errorbars, rangebars and other plot types that have two different orientations work correctly now. Axis labels switch accordingly when the orientation is changed.
  • Plotting functions whose positional arguments don't correspond to X, Y, Z work correctly now. For example, HLines (1 => Y) or rangebars (1 => X, 2 => Y, 3 => Y).
  • It is possible to add categories beyond those present in the data with the categories keyword within a scale's settings. It is also possible to reorder or otherwise transform the existing categories by passing a function to categories.
  • The supported attributes are not limited anymore to a specific set of names, for example, strokecolor can work the same as color did before, and the two can share a scale via their shared aesthetic type.
  • There can be multiple scales of the same aesthetic now. This allows to have separate legends for different plot types using the same aesthetics. Scale separation works by pairing a variable in mapping with a scale(id_symbol).
  • Legend entries can be reordered using the legend = (; order = ...) option in draw. Specific scales can opt out of the legend by passing legend = false in scales.
  • Labels can now be anything that Makie supports, primarily Strings, LaTeXStrings or rich text.
  • Legend elements now usually reflect all attributes set in their corresponding visual.
  • Simple column vectors of data can now be passed directly to mapping without using data first. Additionally, scalar values are accepted as a shortcut for columns with the same repeated value.
  • Columns from outside a table source in data can now be passed to mapping by wrapping them in the direct function. Scalar values are accepted as a shortcut for columns with the same repeated value. For example, to create a label for columns x and y from a dataframe passed to data, one could now do mapping(:x, :y, color = direct("label")) without having to create a column full of "label" strings first.
  • The numbers at which categorical values are plotted on x and y axis can now be changed via scales(X = (; palette = [1, 2, 4])) or similar.
  • Continuous marker size scales can now be shown in the legend. Numerical values are proportional to area and not diameter now, which makes more sense with respect to human perception. The min and max marker size can be set using the sizerange property for the respective scale in scales.

v0.6.11 - 2022-08-08

  • Added paginate for pagination of large facet plots.

v0.6.8 - 2022-06-14

  • Added choropleth recipe to supersede geodata for geographical data.

v0.6.1 - 2022-01-28

  • Support level in linear analysis for confidence interval.
  • Replaced tuples and named tuples in Layer and Entry with dictionaries from Dictionaries.jl.
  • Split internal Entry type into ProcessedLayer (to be used for analyses) and Entry (to be used for plotting).

v0.6.0 - 2021-10-24

  • Breaking: Default axis linking behavior has changed: now only axes corresponding to the same variable are linked. For consistency with row/col, layout will hide decorations of linked axes and span axis labels if appropriate.
  • Customizable legend and colorbar position and look.
  • Customizable axis linking behavior.

v0.5 - 2021-08-05

  • Breaking: Axis(ae) has been replaced by ae.axis.
  • Breaking: Legend(fg) has been replaced by legend!(fg) and colorbar!(fg).
  • legend! and colorbar! API allows for custom legend placement.

v0.4 - 2021-05-21

  • Breaking: Removed deprecations for style and spec (now only mapping and visual are allowed).
  • Breaking: Analyses now require parentheses (i.e. linear() instead of linear).
  • Breaking: Rename layout_x and layout_y to col and row.
  • Breaking: Rename wts keyword argument to weights.
  • Breaking: categorical has been replaced by nonnumeric.
diff --git a/dev/gallery/covers/axis.png b/dev/gallery/covers/axis.png index d48dff1f4..100b4cc62 100644 Binary files a/dev/gallery/covers/axis.png and b/dev/gallery/covers/axis.png differ diff --git a/dev/gallery/covers/colorbar.png b/dev/gallery/covers/colorbar.png index 14bf3880f..2bf5e90f3 100644 Binary files a/dev/gallery/covers/colorbar.png and b/dev/gallery/covers/colorbar.png differ diff --git a/dev/gallery/covers/custom_scales.png b/dev/gallery/covers/custom_scales.png index 48943b7d9..e5e214f4b 100644 Binary files a/dev/gallery/covers/custom_scales.png and b/dev/gallery/covers/custom_scales.png differ diff --git a/dev/gallery/covers/density_plots.png b/dev/gallery/covers/density_plots.png index 3caca8165..092f3e321 100644 Binary files a/dev/gallery/covers/density_plots.png and b/dev/gallery/covers/density_plots.png differ diff --git a/dev/gallery/covers/discrete_scales.png b/dev/gallery/covers/discrete_scales.png index 4ccb749c5..240b9bb79 100644 Binary files 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a/dev/gallery/gallery/applications/geographic/index.html +++ b/dev/gallery/gallery/applications/geographic/index.html @@ -15,4 +15,4 @@ # Draw map plt = data(t) * mapping(:geometry, color = :surface) * visual(Choropleth) -fg = draw(plt; axis=(aspect=1,))

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(plt; axis=(aspect=1,))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/applications/geometries/index.html b/dev/gallery/gallery/applications/geometries/index.html index 3f9814287..f08c13ce3 100644 --- a/dev/gallery/gallery/applications/geometries/index.html +++ b/dev/gallery/gallery/applications/geometries/index.html @@ -7,4 +7,4 @@ df = (; geometry, group) plt = data(df) * visual(Poly) * mapping(:geometry, color = :group) -fg = draw(plt; axis=(aspect=1,))

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(plt; axis=(aspect=1,))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/applications/time_series-3.svg b/dev/gallery/gallery/applications/time_series-3.svg index 20ca26f57..d75f7d0c5 100644 --- a/dev/gallery/gallery/applications/time_series-3.svg +++ b/dev/gallery/gallery/applications/time_series-3.svg @@ -2,157 +2,139 @@ - + - + - - + + - - + + - + - - - - - - - + - + - + - + - + - - + + - + - - + + - - + + - + - + - - + + - - + + - + - - - - - + + - + - + + + + - + - + - + - + - + - - - - + - + - + - - - - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -160,260 +142,250 @@ - - - - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/applications/time_series-4.svg b/dev/gallery/gallery/applications/time_series-4.svg index af9a077cc..1d26e0bd0 100644 --- a/dev/gallery/gallery/applications/time_series-4.svg +++ b/dev/gallery/gallery/applications/time_series-4.svg @@ -2,127 +2,130 @@ - + - + - - + + - + + + + + + + - + - + + + + - + - + - + - - - - + - - - - - - - - + + - + - - + + - + - + - + - + - + - + - + - + - + - - + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -130,198 +133,191 @@ - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/applications/time_series-5.svg b/dev/gallery/gallery/applications/time_series-5.svg index 64479ec94..626c98b82 100644 --- a/dev/gallery/gallery/applications/time_series-5.svg +++ b/dev/gallery/gallery/applications/time_series-5.svg @@ -2,142 +2,139 @@ - + - + - + - + - + - + + + + + + + - + + + + - + - + - + - - - - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - + - + - + - + @@ -145,396 +142,396 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + + diff --git a/dev/gallery/gallery/applications/time_series/index.html b/dev/gallery/gallery/applications/time_series/index.html index 6c14a48d2..18918661c 100644 --- a/dev/gallery/gallery/applications/time_series/index.html +++ b/dev/gallery/gallery/applications/time_series/index.html @@ -26,4 +26,4 @@ return (; date, observation) end plt = data(df) * mapping(:date, :observation) * visual(BoxPlot) -draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

+draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/basic visualizations/assets/lines_and_markers.png b/dev/gallery/gallery/basic visualizations/assets/lines_and_markers.png index 0f456d441..3ff4a609d 100644 Binary files a/dev/gallery/gallery/basic visualizations/assets/lines_and_markers.png and b/dev/gallery/gallery/basic visualizations/assets/lines_and_markers.png differ diff --git a/dev/gallery/gallery/basic visualizations/lines_and_markers-4.svg b/dev/gallery/gallery/basic visualizations/lines_and_markers-4.svg index 47efad5b6..5e465f21c 100644 --- a/dev/gallery/gallery/basic visualizations/lines_and_markers-4.svg +++ b/dev/gallery/gallery/basic visualizations/lines_and_markers-4.svg @@ -2,55 +2,58 @@ - + - + - + - - + + - + - + - + - - - - + - - - - + - + + + + - + + + + - + + + + - + - + - + @@ -58,171 +61,171 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/basic visualizations/lines_and_markers-9.svg b/dev/gallery/gallery/basic visualizations/lines_and_markers-9.svg index fb2310c12..57535abbd 100644 --- a/dev/gallery/gallery/basic visualizations/lines_and_markers-9.svg +++ b/dev/gallery/gallery/basic visualizations/lines_and_markers-9.svg @@ -2,70 +2,70 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -73,85 +73,85 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - + + + + + + + + + + diff --git a/dev/gallery/gallery/basic visualizations/lines_and_markers/index.html b/dev/gallery/gallery/basic visualizations/lines_and_markers/index.html index 2f4038f7d..08170057e 100644 --- a/dev/gallery/gallery/basic visualizations/lines_and_markers/index.html +++ b/dev/gallery/gallery/basic visualizations/lines_and_markers/index.html @@ -18,4 +18,4 @@ df1 = (; x, y) df2 = (x=rand(10), y=rand(10)) layers = data(df1) * visual(Lines) + data(df2) * visual(Scatter) -fg = draw(layers * mapping(:x, :y))

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(layers * mapping(:x, :y))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/basic visualizations/statistical_visualizations-5.svg b/dev/gallery/gallery/basic visualizations/statistical_visualizations-5.svg index 157f6a346..c1a1e8566 100644 --- a/dev/gallery/gallery/basic visualizations/statistical_visualizations-5.svg +++ b/dev/gallery/gallery/basic visualizations/statistical_visualizations-5.svg @@ -1,422 +1,422 @@ - + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - - - + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -453,274 +453,274 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - + diff --git a/dev/gallery/gallery/basic visualizations/statistical_visualizations/index.html b/dev/gallery/gallery/basic visualizations/statistical_visualizations/index.html index 3c6b1006b..18e59a638 100644 --- a/dev/gallery/gallery/basic visualizations/statistical_visualizations/index.html +++ b/dev/gallery/gallery/basic visualizations/statistical_visualizations/index.html @@ -9,4 +9,4 @@ fg = draw(plt, axis=(limits=((0.5, 3.5), nothing),))

data(penguins) * visual(BoxPlot, show_notch=true) *
     mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |> draw

data(penguins) *
     mapping(:bill_length_mm, :bill_depth_mm, col=:sex) *
-    visual(QQPlot, qqline=:fit) |> draw

This page was generated using DemoCards.jl and Literate.jl.

+ visual(QQPlot, qqline=:fit) |> draw

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/customization/assets/axis_tweaking.png b/dev/gallery/gallery/customization/assets/axis_tweaking.png index d48dff1f4..100b4cc62 100644 Binary files a/dev/gallery/gallery/customization/assets/axis_tweaking.png and b/dev/gallery/gallery/customization/assets/axis_tweaking.png differ diff --git a/dev/gallery/gallery/customization/assets/colorbar_tweaking.png b/dev/gallery/gallery/customization/assets/colorbar_tweaking.png index 14bf3880f..2bf5e90f3 100644 Binary files a/dev/gallery/gallery/customization/assets/colorbar_tweaking.png and b/dev/gallery/gallery/customization/assets/colorbar_tweaking.png differ diff --git a/dev/gallery/gallery/customization/assets/figure_tweaking.png b/dev/gallery/gallery/customization/assets/figure_tweaking.png index 469b5cf62..adb20abef 100644 Binary files a/dev/gallery/gallery/customization/assets/figure_tweaking.png and b/dev/gallery/gallery/customization/assets/figure_tweaking.png differ diff --git a/dev/gallery/gallery/customization/assets/legend_tweaking.png b/dev/gallery/gallery/customization/assets/legend_tweaking.png index d81e36bac..a31f7c529 100644 Binary files a/dev/gallery/gallery/customization/assets/legend_tweaking.png and b/dev/gallery/gallery/customization/assets/legend_tweaking.png differ diff --git a/dev/gallery/gallery/customization/axis-5.svg b/dev/gallery/gallery/customization/axis-5.svg index b91c58d1e..6c106493b 100644 --- a/dev/gallery/gallery/customization/axis-5.svg +++ b/dev/gallery/gallery/customization/axis-5.svg @@ -2,295 +2,304 @@ - + - + - + - - - - - - - + - + - - - - + - - + + - + - - + + - + - + + + + + + + + + + - + - + - + - - - - + - + - + - + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - + + + + + + + + + diff --git a/dev/gallery/gallery/customization/axis-6.svg b/dev/gallery/gallery/customization/axis-6.svg index 5016f563c..670b74069 100644 --- a/dev/gallery/gallery/customization/axis-6.svg +++ b/dev/gallery/gallery/customization/axis-6.svg @@ -2,604 +2,598 @@ - + - + - + - - - - - - - - - - - - - + - + - + - + - + - - - - - + + - + - - - - - - - - + + - + - + - + + + + + + + - + - + - - - - - - - - - - - + + - + - + - - + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + - + + + + - + - + - + - + - + - + - + - + - + - + - - - - + - + - + + + + + + + - + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/customization/axis/index.html b/dev/gallery/gallery/customization/axis/index.html index 62834e3d2..b6e51075d 100644 --- a/dev/gallery/gallery/customization/axis/index.html +++ b/dev/gallery/gallery/customization/axis/index.html @@ -2,4 +2,4 @@ Axis tweaking · Algebra of Graphics
GitHub

Axis tweaking

Source code Author

using AlgebraOfGraphics, CairoMakie

To tweak one or more axes, simply use the axis keyword when plotting. For example

df = (x=rand(100), y=rand(100), z=rand(100))
 layers = linear() + mapping(color=:z)
 plt = data(df) * layers * mapping(:x, :y)
-draw(plt, axis=(aspect=1,))

fg = draw(plt, axis=(aspect=1, xticks=0:0.1:1, yticks=0:0.1:1, ylabel="custom label"))

This page was generated using DemoCards.jl and Literate.jl.

+draw(plt, axis=(aspect=1,))

fg = draw(plt, axis=(aspect=1, xticks=0:0.1:1, yticks=0:0.1:1, ylabel="custom label"))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/customization/colorbar-11.svg b/dev/gallery/gallery/customization/colorbar-11.svg index 3c04294c7..60eab7e36 100644 --- a/dev/gallery/gallery/customization/colorbar-11.svg +++ b/dev/gallery/gallery/customization/colorbar-11.svg @@ -2,301 +2,304 @@ - + - + - + - - - - - - - + - + - + + + + + + + - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - + + + + + + diff --git a/dev/gallery/gallery/customization/colorbar-5.svg b/dev/gallery/gallery/customization/colorbar-5.svg index dbde2bf53..cdacaa175 100644 --- a/dev/gallery/gallery/customization/colorbar-5.svg +++ b/dev/gallery/gallery/customization/colorbar-5.svg @@ -2,296 +2,299 @@ - + - + - + - - - - - - - + - + - + - + + + + + + + - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - + + + + + + + + + diff --git a/dev/gallery/gallery/customization/colorbar-6.svg b/dev/gallery/gallery/customization/colorbar-6.svg index 6232e5df2..b02506d63 100644 --- a/dev/gallery/gallery/customization/colorbar-6.svg +++ b/dev/gallery/gallery/customization/colorbar-6.svg @@ -2,308 +2,308 @@ - + - + - + - + - - - - - + + - + - + - + + + + + + + - + - - - - + - - - - + - - - - + - + - + - + - + - + - + - - - - + - + + + + - + - + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - + + + + + + + + + diff --git a/dev/gallery/gallery/customization/colorbar-9.svg b/dev/gallery/gallery/customization/colorbar-9.svg index c58883cc9..ee5afb574 100644 --- a/dev/gallery/gallery/customization/colorbar-9.svg +++ b/dev/gallery/gallery/customization/colorbar-9.svg @@ -2,296 +2,299 @@ - + - + - + - - - - - - - + - + - + - + + + + + + + - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - + + + + + + + + + diff --git a/dev/gallery/gallery/customization/colorbar/index.html b/dev/gallery/gallery/customization/colorbar/index.html index 77733ab2d..998c345aa 100644 --- a/dev/gallery/gallery/customization/colorbar/index.html +++ b/dev/gallery/gallery/customization/colorbar/index.html @@ -6,4 +6,4 @@ colorrange = (0.25, 0.75), highclip = :cyan, lowclip = :lime, -)))

This page was generated using DemoCards.jl and Literate.jl.

+)))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/customization/figure-5.svg b/dev/gallery/gallery/customization/figure-5.svg index da2fd298d..02234f94d 100644 --- a/dev/gallery/gallery/customization/figure-5.svg +++ b/dev/gallery/gallery/customization/figure-5.svg @@ -2,349 +2,358 @@ - + - + - + - + - + + + + - + - - + + - + - - - - + - + - - - - + - + - + - + - + - + - + + + + + + + + + + - + + + + - + + + + + + + - + - + - + - + - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - + + + + + + + + + + + + diff --git a/dev/gallery/gallery/customization/figure-7.svg b/dev/gallery/gallery/customization/figure-7.svg index 649201847..69600276b 100644 --- a/dev/gallery/gallery/customization/figure-7.svg +++ b/dev/gallery/gallery/customization/figure-7.svg @@ -2,786 +2,786 @@ - + - + - + - + - + + + + - + - - - - - - - + - + - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + + + + + + + + + + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - + - - - - - - - + - + - + - + - + - + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - + + + + + + + + + + + + diff --git a/dev/gallery/gallery/customization/figure-9.svg b/dev/gallery/gallery/customization/figure-9.svg index 62bf12d36..d17f90034 100644 --- a/dev/gallery/gallery/customization/figure-9.svg +++ b/dev/gallery/gallery/customization/figure-9.svg @@ -2,767 +2,767 @@ - + - + - + - + - + + + + - + - - - - - - - + - + - - - - + - + - + - + - + - + - + + + + - + + + + + + + + + + - + - + - + - + - + - + - + - + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + - + - + - + - + - + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - + + + + + + + + + + + + diff --git a/dev/gallery/gallery/customization/figure/index.html b/dev/gallery/gallery/customization/figure/index.html index b469fa52d..111e0b7c2 100644 --- a/dev/gallery/gallery/customization/figure/index.html +++ b/dev/gallery/gallery/customization/figure/index.html @@ -39,4 +39,4 @@ titlealign = :right, footnotefont = :bold, ) -)

This page was generated using DemoCards.jl and Literate.jl.

+)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/customization/legend-10.svg b/dev/gallery/gallery/customization/legend-10.svg index 20847f91f..47752190e 100644 --- a/dev/gallery/gallery/customization/legend-10.svg +++ b/dev/gallery/gallery/customization/legend-10.svg @@ -2,211 +2,205 @@ - + - + - + - - - - - - - + - + - - + + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -214,385 +208,385 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - 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+ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -181,380 +178,380 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - - - + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/customization/legend-8.svg b/dev/gallery/gallery/customization/legend-8.svg index c9ea4d050..89674ccda 100644 --- a/dev/gallery/gallery/customization/legend-8.svg +++ b/dev/gallery/gallery/customization/legend-8.svg @@ -2,154 +2,154 @@ - + - + - + - + - - - - - - - - + + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - + + - - + + - + - + - + - + - + - + - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + + + + + + + + + + + + - + - + - + - + @@ -157,391 +157,391 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/customization/legend/index.html b/dev/gallery/gallery/customization/legend/index.html index 4b6ed6c73..493db86d4 100644 --- a/dev/gallery/gallery/customization/legend/index.html +++ b/dev/gallery/gallery/customization/legend/index.html @@ -12,4 +12,4 @@ legend!(makie_fig[1, 1], grid; tellheight=false, tellwidth=false, halign=:right, valign=:top) -makie_fig

This page was generated using DemoCards.jl and Literate.jl.

+makie_fig

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/data manipulations/assets/new_columns_on_the_fly.png b/dev/gallery/gallery/data manipulations/assets/new_columns_on_the_fly.png index 1179a1869..b2fb72610 100644 Binary files a/dev/gallery/gallery/data manipulations/assets/new_columns_on_the_fly.png and b/dev/gallery/gallery/data manipulations/assets/new_columns_on_the_fly.png differ diff --git a/dev/gallery/gallery/data manipulations/assets/pregrouped_data.png b/dev/gallery/gallery/data manipulations/assets/pregrouped_data.png index c5666782e..3fbc4ef5b 100644 Binary files a/dev/gallery/gallery/data manipulations/assets/pregrouped_data.png and b/dev/gallery/gallery/data manipulations/assets/pregrouped_data.png differ diff --git a/dev/gallery/gallery/data manipulations/assets/wide_data.png b/dev/gallery/gallery/data manipulations/assets/wide_data.png index 5a969b8f2..b0556e318 100644 Binary files a/dev/gallery/gallery/data manipulations/assets/wide_data.png and b/dev/gallery/gallery/data manipulations/assets/wide_data.png differ diff --git a/dev/gallery/gallery/data manipulations/new_columns_on_the_fly-3.svg b/dev/gallery/gallery/data manipulations/new_columns_on_the_fly-3.svg index 35e8c0d44..fc27699da 100644 --- a/dev/gallery/gallery/data manipulations/new_columns_on_the_fly-3.svg +++ b/dev/gallery/gallery/data manipulations/new_columns_on_the_fly-3.svg @@ -2,435 +2,420 @@ - + - + - + - + - - - - - - - - - - - - - + - - + + - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - + + + + + + + + + diff --git a/dev/gallery/gallery/data manipulations/new_columns_on_the_fly-5.svg b/dev/gallery/gallery/data manipulations/new_columns_on_the_fly-5.svg index b18502ae0..264322860 100644 --- a/dev/gallery/gallery/data manipulations/new_columns_on_the_fly-5.svg +++ b/dev/gallery/gallery/data manipulations/new_columns_on_the_fly-5.svg @@ -2,490 +2,447 @@ - + - - + + - + - - - - + - + - - - - - - - - - - - - - + - - - - + - - - - - - - + - + - - + + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - - + + - + + + + + + + - + - + + + + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/data manipulations/new_columns_on_the_fly/index.html b/dev/gallery/gallery/data manipulations/new_columns_on_the_fly/index.html index 7715b4483..bbd2ddb01 100644 --- a/dev/gallery/gallery/data manipulations/new_columns_on_the_fly/index.html +++ b/dev/gallery/gallery/data manipulations/new_columns_on_the_fly/index.html @@ -6,4 +6,4 @@ plt = data(df) * layers * mapping(:x => (x -> x^2) => L"x^2", :y => L"This variable is called $y$") fg = draw(plt)

Use a Tuple to pass combine several columns into a unique operation.

plt = data(df) * layers * mapping(:x, (:x, :y, :z) => (+) => L"the new variable $x + y + z$", layout=:c)
-fg = draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/data manipulations/no_data/index.html b/dev/gallery/gallery/data manipulations/no_data/index.html index 62d25b851..0b5192e2d 100644 --- a/dev/gallery/gallery/data manipulations/no_data/index.html +++ b/dev/gallery/gallery/data manipulations/no_data/index.html @@ -9,4 +9,4 @@ mapping([20, 28, 51], color = "marker" => scale(:secondary)) * visual(VLines, linestyle = :dash) -fg = draw(plt, scales(secondary = (; palette = [:gray80])))

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(plt, scales(secondary = (; palette = [:gray80])))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/data manipulations/pre_grouped_data-3.svg b/dev/gallery/gallery/data manipulations/pre_grouped_data-3.svg index 166b0ad66..b0871aed0 100644 --- a/dev/gallery/gallery/data manipulations/pre_grouped_data-3.svg +++ b/dev/gallery/gallery/data manipulations/pre_grouped_data-3.svg @@ -2,92 +2,89 @@ - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -95,143 +92,139 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/data manipulations/pre_grouped_data-4.svg b/dev/gallery/gallery/data manipulations/pre_grouped_data-4.svg index 0ea0abae5..e4276b98f 100644 --- a/dev/gallery/gallery/data manipulations/pre_grouped_data-4.svg +++ b/dev/gallery/gallery/data manipulations/pre_grouped_data-4.svg @@ -2,107 +2,104 @@ - - - - + - + - - - - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -110,160 +107,156 @@ - - - - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/data manipulations/pre_grouped_data-5.svg b/dev/gallery/gallery/data manipulations/pre_grouped_data-5.svg index 61f255140..f4ab0d70c 100644 --- a/dev/gallery/gallery/data manipulations/pre_grouped_data-5.svg +++ b/dev/gallery/gallery/data manipulations/pre_grouped_data-5.svg @@ -2,31 +2,31 @@ - - - - + - + - + - + + + + - + - + - + - + @@ -34,104 +34,100 @@ - - - - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + diff --git a/dev/gallery/gallery/data manipulations/pre_grouped_data-6.svg b/dev/gallery/gallery/data manipulations/pre_grouped_data-6.svg index f109317ff..8a78092cd 100644 --- a/dev/gallery/gallery/data manipulations/pre_grouped_data-6.svg +++ b/dev/gallery/gallery/data manipulations/pre_grouped_data-6.svg @@ -2,28 +2,31 @@ - - - - + - + - + + + + - + - + - + - + + + + @@ -31,105 +34,105 @@ - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -137,10 +140,10 @@ - + - + diff --git a/dev/gallery/gallery/data manipulations/pre_grouped_data/index.html b/dev/gallery/gallery/data manipulations/pre_grouped_data/index.html index 84e101a2d..2e058d445 100644 --- a/dev/gallery/gallery/data manipulations/pre_grouped_data/index.html +++ b/dev/gallery/gallery/data manipulations/pre_grouped_data/index.html @@ -12,4 +12,4 @@ draw(m)

m = pregrouped(x, [y z], color=dims(1) => renamer(["a", "b", "c"])) * visual(Scatter)
 draw(m)

m = pregrouped(x, [y z], color=["1" "2"])
 layers = visual(Scatter) + linear()
-fg = draw(m * layers)

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(m * layers)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/data manipulations/presorted_data/index.html b/dev/gallery/gallery/data manipulations/presorted_data/index.html index 62bfd2e27..ebbba80f9 100644 --- a/dev/gallery/gallery/data manipulations/presorted_data/index.html +++ b/dev/gallery/gallery/data manipulations/presorted_data/index.html @@ -15,4 +15,4 @@ fg = draw(spec)

We can also mark multiple variables as presorted, note how the order in the color legend shifts when we do the same for group:

spec = data(df) *
     mapping(:countries => presorted, :some_value, color = :group => presorted) *
     visual(BarPlot, direction = :x)
-draw(spec)

This page was generated using DemoCards.jl and Literate.jl.

+draw(spec)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/data manipulations/wide_data-10.svg b/dev/gallery/gallery/data manipulations/wide_data-10.svg index 349ce645f..eb5342557 100644 --- a/dev/gallery/gallery/data manipulations/wide_data-10.svg +++ b/dev/gallery/gallery/data manipulations/wide_data-10.svg @@ -2,396 +2,411 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - - + + - + - + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - + - + - + + + + - + - + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + - + - - + + + + + - + + + + - + - + - - - - - - - - + + - + - + - + - + - + - + - - - - - + + - + - + - + - + + + + - - + + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - - - - + - + - + + + + - + - - - - + - + - + - + - + - + - - + + + + + + + + - + + + + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - @@ -400,929 +415,929 @@ - + - + - + - + - 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+ - + - + - + - + - + - + - + - + - + - - - - - + + - - + + - - + + - + + - + - - + + - - + + - - + + - - + + - - + - + - + - + - + - + - + - - + + + + + - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -147,115 +150,115 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - + + + + + + + + + - - - - + + + + @@ -263,67 +266,67 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/data manipulations/wide_data-4.svg b/dev/gallery/gallery/data manipulations/wide_data-4.svg index 50c50fca8..1feff1254 100644 --- a/dev/gallery/gallery/data manipulations/wide_data-4.svg +++ b/dev/gallery/gallery/data manipulations/wide_data-4.svg @@ -2,130 +2,139 @@ - + - + - + - - + + + + + + + + - + - + - + - + - - + + - - + + - + - + - + - + - + - + - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -133,389 +142,389 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -526,91 +535,91 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/data manipulations/wide_data-7.svg b/dev/gallery/gallery/data manipulations/wide_data-7.svg index 58f91d5fb..4e350d486 100644 --- a/dev/gallery/gallery/data manipulations/wide_data-7.svg +++ b/dev/gallery/gallery/data manipulations/wide_data-7.svg @@ -2,226 +2,232 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + - + - + + + + - + - + + + + - + - + - + - - + + + + + - + - + - + - + - + - + - + - - - - + - + - + - - - - + - + + + + - + - + - + - + - + - + - + - - + + - - + + - + @@ -232,671 +238,671 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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+ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + + + + - + - + - + - + - - - - - + + - + - - - - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - - - - + - - - - + - - + + - + - - + + - - + + - + - + - + - - - - + - - - - + - + - + - + - + - - - - - - - - - - - - - - - - - - - + - + - + - + - + - + - + @@ -265,751 +235,751 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/data manipulations/wide_data/index.html b/dev/gallery/gallery/data manipulations/wide_data/index.html index 6e9449042..f49b3f971 100644 --- a/dev/gallery/gallery/data manipulations/wide_data/index.html +++ b/dev/gallery/gallery/data manipulations/wide_data/index.html @@ -22,4 +22,4 @@ yvars = ["petal_length" "petal_width"] layers = linear() + visual(Scatter) plt = data(df) * layers * mapping(xvars, yvars, col=dims(1), row=dims(2)) -draw(plt)

Axes can be fully linked or fully unlinked

draw(plt, facet = (; linkxaxes = :all, linkyaxes = :all))

fg = draw(plt, facet = (; linkxaxes = :none, linkyaxes = :none))

This page was generated using DemoCards.jl and Literate.jl.

+draw(plt)

Axes can be fully linked or fully unlinked

draw(plt, facet = (; linkxaxes = :all, linkyaxes = :all))

fg = draw(plt, facet = (; linkxaxes = :none, linkyaxes = :none))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/layout/assets/faceting.png b/dev/gallery/gallery/layout/assets/faceting.png index 802da6947..a0be05796 100644 Binary files a/dev/gallery/gallery/layout/assets/faceting.png and b/dev/gallery/gallery/layout/assets/faceting.png differ diff --git a/dev/gallery/gallery/layout/assets/nested_layouts.png b/dev/gallery/gallery/layout/assets/nested_layouts.png index 175ea4196..5929e9aab 100644 Binary files a/dev/gallery/gallery/layout/assets/nested_layouts.png and b/dev/gallery/gallery/layout/assets/nested_layouts.png differ diff --git a/dev/gallery/gallery/layout/faceting-11.svg b/dev/gallery/gallery/layout/faceting-11.svg index 5c543b38e..b6637daf8 100644 --- a/dev/gallery/gallery/layout/faceting-11.svg +++ b/dev/gallery/gallery/layout/faceting-11.svg @@ -2,127 +2,112 @@ - + - + - - + + - - + + - + - - - - - + + - - + + - - + + - + - - - - + - + - - - - + - + - - + + + + + - + - + - - + + - + - - + + - + - - - - + - - - - - + + - + - - + + - - + + - - + + - + - - - - + - + - + - + - + - + - + - + @@ -134,316 +119,276 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - + - + - + - + - + - + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/layout/faceting-13.svg b/dev/gallery/gallery/layout/faceting-13.svg index a6e43b1b7..778ec82cb 100644 --- a/dev/gallery/gallery/layout/faceting-13.svg +++ b/dev/gallery/gallery/layout/faceting-13.svg @@ -2,870 +2,792 @@ - 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- - - - - - - - - - - - - - + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/layout/faceting-17.svg b/dev/gallery/gallery/layout/faceting-17.svg index ccd6ed4c9..7dd61ac4c 100644 --- a/dev/gallery/gallery/layout/faceting-17.svg +++ b/dev/gallery/gallery/layout/faceting-17.svg @@ -2,97 +2,121 @@ - + - + - + - + - + - + + + + + + + - + + + + + + + - + + + + - + - + - + - + - + - + - + - + - + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -108,304 +132,304 @@ - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - + + + + + + - + - + - + - + - + - + - + - + - + - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - - - - - - + + + + + + - - - + + + diff --git a/dev/gallery/gallery/layout/faceting-19.svg b/dev/gallery/gallery/layout/faceting-19.svg index 08d037317..930b31f02 100644 --- a/dev/gallery/gallery/layout/faceting-19.svg +++ b/dev/gallery/gallery/layout/faceting-19.svg @@ -2,112 +2,112 @@ - + - + - + + + + + + + - + + + + - + - + - + - + - + - - - - + - - + + - + - - + + - - + + - + - + + + + - + - + - + - - - - + - - + + - - + + - + - - - - + - + - - - - + - + - + - + - + - + - + @@ -119,276 +119,276 @@ - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - 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+ - + - - - - + - - - - + - + - + - + - + - + - + - + - + - + - + + + + + + + - + + + + - - + + + + + + + + - + - + - - + + - + - + - + - + - + - + @@ -91,365 +100,386 @@ - - - - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - - - - - - - - - - - - - - - - - - - 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+ - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - - - - + + + + - - - - + + + + diff --git a/dev/gallery/gallery/layout/faceting-7.svg b/dev/gallery/gallery/layout/faceting-7.svg index 496add89b..1b7f0dad7 100644 --- a/dev/gallery/gallery/layout/faceting-7.svg +++ b/dev/gallery/gallery/layout/faceting-7.svg @@ -2,106 +2,106 @@ - + - + - - + + - - + + - + - + - + - + - + - + - - + + + + + + + + - + - + - + - + - + - - - - - - - + - + - - + + - - + + - + - + - + - + - + - + - + - + - + - + - + - + @@ -114,240 +114,240 @@ - + - + - + - + - + - - - - - - - - - - - - + + + + + + + + + + + + - + - + - + - + - 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+ - + - + - + - + - + - + @@ -144,307 +135,317 @@ - + + + + + + + + + + + + + + + + - + - + - + - + + + + + + + + + + + + + - + - + - + - + - + - + - + - - - - - - - - - - - - - + - + - + - + - + + + + + + + + + + + + + + + + + - + - + - + - + - + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - + - + - + - + - + - - - - - - - - - - - - - - - - - + - + - + - + + + + + + + + + + + + + + + + + + + - + - + - - - - - - - - - - - - - - - - - - - - - - - + - + - + - + + + + + + + + + + + + + + + + + + + + - + - + - + - - - - - - - - - - - - - - - - - - - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - + + + + + + + + + + diff --git a/dev/gallery/gallery/layout/faceting/index.html b/dev/gallery/gallery/layout/faceting/index.html index c71be748f..3a2b7ebce 100644 --- a/dev/gallery/gallery/layout/faceting/index.html +++ b/dev/gallery/gallery/layout/faceting/index.html @@ -31,4 +31,4 @@ plt = data(df) * mapping(:x, :y, layout=:l) pag = paginate(plt, layout = 4)
PaginatedLayers with 2 entries (layout = 4)

The object returned from draw will be a Vector{FigureGrid}.

figuregrids = draw(pag)
2-element Vector{AlgebraOfGraphics.FigureGrid}:
  FigureGrid()
- FigureGrid()

You can either extract single figures from this vector...

figuregrids[1]

or use draw with an optional second argument specifying the index of the page to draw.

draw(pag, 2)

This page was generated using DemoCards.jl and Literate.jl.

+ FigureGrid()

You can either extract single figures from this vector...

figuregrids[1]

or use draw with an optional second argument specifying the index of the page to draw.

draw(pag, 2)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/layout/nested_layouts-5.svg b/dev/gallery/gallery/layout/nested_layouts-5.svg index a570d874f..eab71a8ef 100644 --- a/dev/gallery/gallery/layout/nested_layouts-5.svg +++ b/dev/gallery/gallery/layout/nested_layouts-5.svg @@ -2,195 +2,180 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - + - + - + - + - + - + - + - + + + + - + - + - - - - + - + - + - - - - + - - + + - + - - - - + - + - + - + - + - + - + - + - + - + - + @@ -207,741 +192,741 @@ - + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + - + - + - + - + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - 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+ - + - + - + - + - + - + @@ -949,87 +934,87 @@ - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/layout/nested_layouts-7.svg b/dev/gallery/gallery/layout/nested_layouts-7.svg index 6bad0f8b2..d7bee6fb9 100644 --- a/dev/gallery/gallery/layout/nested_layouts-7.svg +++ b/dev/gallery/gallery/layout/nested_layouts-7.svg @@ -2,120 +2,123 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + + + + - + - + - + - + + + + - + + + + - + - - - - - - - + - + - + - + - + - + @@ -125,640 +128,640 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - + + + + + + diff --git a/dev/gallery/gallery/layout/nested_layouts-9.svg b/dev/gallery/gallery/layout/nested_layouts-9.svg index 7c4c3e22f..06e942c4d 100644 --- a/dev/gallery/gallery/layout/nested_layouts-9.svg +++ b/dev/gallery/gallery/layout/nested_layouts-9.svg @@ -2,418 +2,360 @@ - + - - - - + - + - - - - - + + - + - + - - - - - - - - - - + - - - - - - - + - + - + - + - - - - - - - - - - + - - + + - + - + - - - - + - + - + - - - - - + + - + - + - - + + - + - + - - - - - - - - - - - - - - - - - - - - - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + - - + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - - - - + + + + diff --git a/dev/gallery/gallery/layout/nested_layouts/index.html b/dev/gallery/gallery/layout/nested_layouts/index.html index 832ca9a86..afcae5df5 100644 --- a/dev/gallery/gallery/layout/nested_layouts/index.html +++ b/dev/gallery/gallery/layout/nested_layouts/index.html @@ -42,4 +42,4 @@ grid = draw!(ax2, plt) legend!(f[1, 3], grid) -f

This page was generated using DemoCards.jl and Literate.jl.

+f

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/scales/assets/custom_scales.png b/dev/gallery/gallery/scales/assets/custom_scales.png index 48943b7d9..e5e214f4b 100644 Binary files a/dev/gallery/gallery/scales/assets/custom_scales.png and b/dev/gallery/gallery/scales/assets/custom_scales.png differ diff --git a/dev/gallery/gallery/scales/assets/discrete_scales.png b/dev/gallery/gallery/scales/assets/discrete_scales.png index 4ccb749c5..240b9bb79 100644 Binary files a/dev/gallery/gallery/scales/assets/discrete_scales.png and b/dev/gallery/gallery/scales/assets/discrete_scales.png differ diff --git a/dev/gallery/gallery/scales/assets/legend_merging.png b/dev/gallery/gallery/scales/assets/legend_merging.png index 8e80bafb7..f15014356 100644 Binary files a/dev/gallery/gallery/scales/assets/legend_merging.png and b/dev/gallery/gallery/scales/assets/legend_merging.png differ diff --git a/dev/gallery/gallery/scales/assets/multiple_color_scales.png b/dev/gallery/gallery/scales/assets/multiple_color_scales.png index 90f37641f..46854a84f 100644 Binary files a/dev/gallery/gallery/scales/assets/multiple_color_scales.png and b/dev/gallery/gallery/scales/assets/multiple_color_scales.png differ diff --git a/dev/gallery/gallery/scales/assets/prescaled_data.png b/dev/gallery/gallery/scales/assets/prescaled_data.png index 9f99b5aca..a40333665 100644 Binary files a/dev/gallery/gallery/scales/assets/prescaled_data.png and b/dev/gallery/gallery/scales/assets/prescaled_data.png differ diff --git a/dev/gallery/gallery/scales/assets/secondary_scales.png b/dev/gallery/gallery/scales/assets/secondary_scales.png index e42c79c36..e2957ae63 100644 Binary files a/dev/gallery/gallery/scales/assets/secondary_scales.png and b/dev/gallery/gallery/scales/assets/secondary_scales.png differ diff --git a/dev/gallery/gallery/scales/continuous_scales/index.html b/dev/gallery/gallery/scales/continuous_scales/index.html index 61c8674f6..2e1ebd6cd 100644 --- a/dev/gallery/gallery/scales/continuous_scales/index.html +++ b/dev/gallery/gallery/scales/continuous_scales/index.html @@ -25,4 +25,4 @@ :x, :y => "y", ) * visual(Lines) -fg = draw(plt, axis=(yscale=log,))

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(plt, axis=(yscale=log,))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/scales/custom_scales-5.svg b/dev/gallery/gallery/scales/custom_scales-5.svg index f9cf58837..c472cfe96 100644 --- a/dev/gallery/gallery/scales/custom_scales-5.svg +++ b/dev/gallery/gallery/scales/custom_scales-5.svg @@ -2,82 +2,82 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -85,852 +85,852 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - + + + + + + - - - + - - - + - - - - - - - + + + - - - - - + + + + + + + + + + - - - - - - - + + - - - - - - - - - + + + + + + + + + + + + + + + - - - - - - - - - - - - - - + + + + + + + + + - - - - + + + - - - - - - + + + + - + + - - - + + + + + + - - - - - - - - + + + + + + - - - - + + + + - - - + + + + + + - - - + - - - + - - - - - - - + + + - - - - - + + + + + + + + + + - - - - - - - + + - - - - - - - - - + + + + + + + + + + + + + + + - - - - - - - - - - - - - - + + + + + + + + + - - - - + + + - - - - - - + + + + - + + - - - + + + + + + - 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+ - + - + - - + + - + - + - + - + - - - - - - - - + + - + - + - + - + + + + - + + + + - + - + - + - + - + @@ -70,189 +70,189 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + diff --git a/dev/gallery/gallery/scales/custom_scales-9.svg b/dev/gallery/gallery/scales/custom_scales-9.svg index b464b9c2c..6ae826712 100644 --- a/dev/gallery/gallery/scales/custom_scales-9.svg +++ b/dev/gallery/gallery/scales/custom_scales-9.svg @@ -2,79 +2,76 @@ - + - + - + + + + - + - + - + - + - + - + - + - - - - + - + - - - - + - - - - + - + + + + - + - + - + - + - + - + - + @@ -82,271 +79,271 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -356,31 +353,31 @@ - + - + - + - + - + - + - + - + - + diff --git a/dev/gallery/gallery/scales/custom_scales/index.html b/dev/gallery/gallery/scales/custom_scales/index.html index a6f3fc15d..6301caa3a 100644 --- a/dev/gallery/gallery/scales/custom_scales/index.html +++ b/dev/gallery/gallery/scales/custom_scales/index.html @@ -25,4 +25,4 @@ z = rand(["a", "b", "c", "d", "e", "f", "g", "h"], 200) df = (; x, y, z) plt = data(df) * mapping(:x, :y, color=:z) -draw(plt, scales(Color = (; palette = from_continuous(:cividis))))

This page was generated using DemoCards.jl and Literate.jl.

+draw(plt, scales(Color = (; palette = from_continuous(:cividis))))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/scales/discrete_scales-10.svg b/dev/gallery/gallery/scales/discrete_scales-10.svg index d3fa63c08..132bc8be4 100644 --- a/dev/gallery/gallery/scales/discrete_scales-10.svg +++ b/dev/gallery/gallery/scales/discrete_scales-10.svg @@ -2,55 +2,55 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -58,93 +58,93 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - + + + diff --git a/dev/gallery/gallery/scales/discrete_scales-12.svg b/dev/gallery/gallery/scales/discrete_scales-12.svg index 529206aae..f129ecd42 100644 --- a/dev/gallery/gallery/scales/discrete_scales-12.svg +++ b/dev/gallery/gallery/scales/discrete_scales-12.svg @@ -2,73 +2,73 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -76,125 +76,125 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - + + + diff --git a/dev/gallery/gallery/scales/discrete_scales-14.svg b/dev/gallery/gallery/scales/discrete_scales-14.svg index 529206aae..f129ecd42 100644 --- a/dev/gallery/gallery/scales/discrete_scales-14.svg +++ b/dev/gallery/gallery/scales/discrete_scales-14.svg @@ -2,73 +2,73 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -76,125 +76,125 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - + + + diff --git a/dev/gallery/gallery/scales/discrete_scales-16.svg b/dev/gallery/gallery/scales/discrete_scales-16.svg index e4927e965..96972e9fe 100644 --- a/dev/gallery/gallery/scales/discrete_scales-16.svg +++ b/dev/gallery/gallery/scales/discrete_scales-16.svg @@ -2,61 +2,61 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - + - + + + + @@ -64,103 +64,101 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + + diff --git a/dev/gallery/gallery/scales/discrete_scales-17.svg b/dev/gallery/gallery/scales/discrete_scales-17.svg index 8c04a7e0b..e9d3ec1ea 100644 --- a/dev/gallery/gallery/scales/discrete_scales-17.svg +++ b/dev/gallery/gallery/scales/discrete_scales-17.svg @@ -2,61 +2,61 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - + - + + + + @@ -64,103 +64,101 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + - - - + + + diff --git a/dev/gallery/gallery/scales/discrete_scales-5.svg b/dev/gallery/gallery/scales/discrete_scales-5.svg index 5da1b9b9f..975df6ba3 100644 --- a/dev/gallery/gallery/scales/discrete_scales-5.svg +++ b/dev/gallery/gallery/scales/discrete_scales-5.svg @@ -2,40 +2,40 @@ - + - + - + - + - + - + - + - + - + - + - + - + @@ -43,71 +43,71 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + - - - + + + diff --git a/dev/gallery/gallery/scales/discrete_scales-6.svg b/dev/gallery/gallery/scales/discrete_scales-6.svg index 301d96dd2..e000ad54e 100644 --- a/dev/gallery/gallery/scales/discrete_scales-6.svg +++ b/dev/gallery/gallery/scales/discrete_scales-6.svg @@ -2,73 +2,73 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -76,116 +76,116 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - + + + diff --git a/dev/gallery/gallery/scales/discrete_scales-8.svg b/dev/gallery/gallery/scales/discrete_scales-8.svg index 301d96dd2..e000ad54e 100644 --- a/dev/gallery/gallery/scales/discrete_scales-8.svg +++ b/dev/gallery/gallery/scales/discrete_scales-8.svg @@ -2,73 +2,73 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -76,116 +76,116 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + - - - + + + diff --git a/dev/gallery/gallery/scales/discrete_scales/index.html b/dev/gallery/gallery/scales/discrete_scales/index.html index 7aa53a7a1..efb8657ff 100644 --- a/dev/gallery/gallery/scales/discrete_scales/index.html +++ b/dev/gallery/gallery/scales/discrete_scales/index.html @@ -36,4 +36,4 @@ draw(plt, scales(X = (; categories = cats -> initialed.(sort(cats; by = name -> split(name)[2])) -)))

This page was generated using DemoCards.jl and Literate.jl.

+)))

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/scales/dodging/index.html b/dev/gallery/gallery/scales/dodging/index.html index 35cc328bc..fdbd1dea2 100644 --- a/dev/gallery/gallery/scales/dodging/index.html +++ b/dev/gallery/gallery/scales/dodging/index.html @@ -22,4 +22,4 @@ draw!(f[2, 1], plt3, scales(DodgeX = (; width = 0.5)); axis = kw(title = "DodgeX = (; width = 0.5)")) draw!(f[3, 1], plt3, scales(DodgeX = (; width = 1.0)); axis = (; title = "DodgeX = (; width = 1.0)")) -f

This page was generated using DemoCards.jl and Literate.jl.

+f

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/scales/legend_merging-3.svg b/dev/gallery/gallery/scales/legend_merging-3.svg index d76807204..6a03ee2ce 100644 --- a/dev/gallery/gallery/scales/legend_merging-3.svg +++ b/dev/gallery/gallery/scales/legend_merging-3.svg @@ -2,231 +2,248 @@ - + - + - + - + + + + - + - + - + + + + - + - - + + - + - - + + - - + + - + - + - + - + + + + + + + - + - + - + - + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - - + + diff --git a/dev/gallery/gallery/scales/legend_merging/index.html b/dev/gallery/gallery/scales/legend_merging/index.html index 398538b62..b8bf47394 100644 --- a/dev/gallery/gallery/scales/legend_merging/index.html +++ b/dev/gallery/gallery/scales/legend_merging/index.html @@ -12,4 +12,4 @@ layers = visual(Lines) + visual(Scatter) * mapping(marker = :grp) plt = data(df) * layers * mapping(:x, :y, color = :grp) -fg = draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/scales/multiple_color_scales-5.svg b/dev/gallery/gallery/scales/multiple_color_scales-5.svg index 2c32badcd..dafe4e0b4 100644 --- a/dev/gallery/gallery/scales/multiple_color_scales-5.svg +++ b/dev/gallery/gallery/scales/multiple_color_scales-5.svg @@ -2,822 +2,822 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - + + + + + @@ -827,13 +827,13 @@ - + - + - + diff --git a/dev/gallery/gallery/scales/multiple_color_scales/index.html b/dev/gallery/gallery/scales/multiple_color_scales/index.html index 8c0fabc47..520809596 100644 --- a/dev/gallery/gallery/scales/multiple_color_scales/index.html +++ b/dev/gallery/gallery/scales/multiple_color_scales/index.html @@ -7,4 +7,4 @@ df = (; x, y, ŷ, z, c) layers = mapping(:y, color=:z) * visual(Lines) + mapping(:ŷ => "y", color=:c) plt = data(df) * mapping(:x) * layers -fg = draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/scales/prescaled_data-3.svg b/dev/gallery/gallery/scales/prescaled_data-3.svg index ac0e8d6e4..14dd685ce 100644 --- a/dev/gallery/gallery/scales/prescaled_data-3.svg +++ b/dev/gallery/gallery/scales/prescaled_data-3.svg @@ -2,58 +2,55 @@ - + - + - + - + - - - - + - - + + - + - - + + - - + + - + - + - + - + - + - + - + - + @@ -61,171 +58,171 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/scales/prescaled_data-5.svg b/dev/gallery/gallery/scales/prescaled_data-5.svg index 25b3dd34e..94f82ff40 100644 --- a/dev/gallery/gallery/scales/prescaled_data-5.svg +++ b/dev/gallery/gallery/scales/prescaled_data-5.svg @@ -2,547 +2,538 @@ - + - + - - + + - - + + - + - - - - - + + - + - - + + - + - - + + - + - + - + - - - - - - - + - + - - - - - - - - + + - + - + + + + - + - + - + - + - - + + - + - + + + + - - + + - + - + - + - + - + - + - + - + - + - + - - - - - - - + - + - + - + - + - + - + - - - - + - + - + - + - + - - + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - + + + + + + diff --git a/dev/gallery/gallery/scales/prescaled_data/index.html b/dev/gallery/gallery/scales/prescaled_data/index.html index 97b43dc2d..d53fa30c6 100644 --- a/dev/gallery/gallery/scales/prescaled_data/index.html +++ b/dev/gallery/gallery/scales/prescaled_data/index.html @@ -12,4 +12,4 @@ label = rand(["a", "b"], 100) df = (; x, y, label) plt = data(df) * mapping(:x, :y, text=:label => verbatim) * visual(Makie.Text) -fg = draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/scales/secondary_scales-11.svg b/dev/gallery/gallery/scales/secondary_scales-11.svg index d0d9aa860..921439903 100644 --- a/dev/gallery/gallery/scales/secondary_scales-11.svg +++ b/dev/gallery/gallery/scales/secondary_scales-11.svg @@ -2,213 +2,210 @@ - + - + - + - + - + - + - - - - - + + - + - + - + - + - - + + - + - - + + - - + + - - + + - + - - + + + + + + + + - + - + - + - + - + - + - - + + - - + + - - + + - - + + - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - + + + + + - - + + - - + + - - + + - - - - - - + + + + + + - + - + - + - + - + - + - + - + - + - + - + - - + + diff --git a/dev/gallery/gallery/scales/secondary_scales-5.svg b/dev/gallery/gallery/scales/secondary_scales-5.svg index 7dcbabe14..049a024b0 100644 --- a/dev/gallery/gallery/scales/secondary_scales-5.svg +++ b/dev/gallery/gallery/scales/secondary_scales-5.svg @@ -2,163 +2,154 @@ - + - + - + - + - + - + - - + + - + - - - - + - + - - + + - - + + - - + + - - + + - - + + - + - + - + - + - + - - + + - - + + - - + + - - + + - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - + + + + + - - + + - - + + - - + + - - - - - - + + + + + + @@ -170,20 +161,20 @@ - + - + - + - + - + - - + + diff --git a/dev/gallery/gallery/scales/secondary_scales-7.svg b/dev/gallery/gallery/scales/secondary_scales-7.svg index d27c3f7de..967100821 100644 --- a/dev/gallery/gallery/scales/secondary_scales-7.svg +++ b/dev/gallery/gallery/scales/secondary_scales-7.svg @@ -2,240 +2,228 @@ - + - + - + - + - + - - + + + + + - + - + - - + + - + - - + + - + - - - - - - - - - - - + + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - - + + - - + + - - + + - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - + + + + + - - + + - - + + - - + + - - - - - - + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + diff --git a/dev/gallery/gallery/scales/secondary_scales-9.svg b/dev/gallery/gallery/scales/secondary_scales-9.svg index f38062a0d..3b08fc8b0 100644 --- a/dev/gallery/gallery/scales/secondary_scales-9.svg +++ b/dev/gallery/gallery/scales/secondary_scales-9.svg @@ -2,240 +2,228 @@ - + - + - + - + - + - - + + + + + - + - + - - + + - + - - + + - + - - - - - - - - - - - + + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - - + + - - + + - - + + - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - + + + + + - - + + - - + + - - + + - - - - - - + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + diff --git a/dev/gallery/gallery/scales/secondary_scales/index.html b/dev/gallery/gallery/scales/secondary_scales/index.html index b8fd81333..32d2e2975 100644 --- a/dev/gallery/gallery/scales/secondary_scales/index.html +++ b/dev/gallery/gallery/scales/secondary_scales/index.html @@ -19,4 +19,4 @@ secondary = (; palette = [:gray70, :gray30]) ); legend = (; order = [[:Color, :secondary] => "Legend"]) -)

This page was generated using DemoCards.jl and Literate.jl.

+)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/statistical analyses/assets/density_plots.png b/dev/gallery/gallery/statistical analyses/assets/density_plots.png index 3caca8165..092f3e321 100644 Binary files a/dev/gallery/gallery/statistical analyses/assets/density_plots.png and b/dev/gallery/gallery/statistical analyses/assets/density_plots.png differ diff --git a/dev/gallery/gallery/statistical analyses/assets/histograms.png b/dev/gallery/gallery/statistical analyses/assets/histograms.png index 5120995b9..52bf47049 100644 Binary files a/dev/gallery/gallery/statistical analyses/assets/histograms.png and b/dev/gallery/gallery/statistical analyses/assets/histograms.png differ diff --git a/dev/gallery/gallery/statistical analyses/assets/regression_plots.png b/dev/gallery/gallery/statistical analyses/assets/regression_plots.png index 9eb6e9f07..88105e56c 100644 Binary files a/dev/gallery/gallery/statistical analyses/assets/regression_plots.png and b/dev/gallery/gallery/statistical analyses/assets/regression_plots.png differ diff --git a/dev/gallery/gallery/statistical analyses/density_plots-3.svg b/dev/gallery/gallery/statistical analyses/density_plots-3.svg index f43719b96..68a27310d 100644 --- a/dev/gallery/gallery/statistical analyses/density_plots-3.svg +++ b/dev/gallery/gallery/statistical analyses/density_plots-3.svg @@ -2,67 +2,76 @@ - + - + - - + + - + - - + + - + - + - + + + + + + + + + + + + + - + - + - + - + - + - + - + - - - - + - + - + - + - + @@ -70,88 +79,106 @@ - + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - + + + + + + + - - - + + + - + - + - + diff --git a/dev/gallery/gallery/statistical analyses/density_plots/index.html b/dev/gallery/gallery/statistical analyses/density_plots/index.html index 6bc4245b7..d8b5e013b 100644 --- a/dev/gallery/gallery/statistical analyses/density_plots/index.html +++ b/dev/gallery/gallery/statistical analyses/density_plots/index.html @@ -6,4 +6,4 @@ plt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5) fg = draw(plt)

df = (x=randn(1000), c=rand(["a", "b"], 1000))
 plt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5) * visual(orientation=:vertical)
-"Not yet supported" # hide
"Not yet supported"

This page was generated using DemoCards.jl and Literate.jl.

+"Not yet supported" # hide
"Not yet supported"

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/statistical analyses/histograms-3.svg b/dev/gallery/gallery/statistical analyses/histograms-3.svg index 1aee598f6..981796dd9 100644 --- a/dev/gallery/gallery/statistical analyses/histograms-3.svg +++ b/dev/gallery/gallery/statistical analyses/histograms-3.svg @@ -2,178 +2,135 @@ - + - + - - + + - + - + - - - - + - - - - + - + - + - + - + - + - - + + - + - - - - + - - - - - - - + - - - - - - - - - - - - - - - - - - - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/statistical analyses/histograms-4.svg b/dev/gallery/gallery/statistical analyses/histograms-4.svg index 499eda14e..de6d09177 100644 --- a/dev/gallery/gallery/statistical analyses/histograms-4.svg +++ b/dev/gallery/gallery/statistical analyses/histograms-4.svg @@ -2,67 +2,73 @@ - + - + - + + + + - + - + - + - + - + - + - + - + - + - + - + - + + + + + + + - + - - - - + - + - + - + @@ -70,114 +76,114 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - + + + + - + - + - + diff --git a/dev/gallery/gallery/statistical analyses/histograms-5.svg b/dev/gallery/gallery/statistical analyses/histograms-5.svg index a18d4da83..ff44cfea7 100644 --- a/dev/gallery/gallery/statistical analyses/histograms-5.svg +++ b/dev/gallery/gallery/statistical analyses/histograms-5.svg @@ -2,463 +2,506 @@ - + - + - + + + + - + - - - - - + + - - + + - - + + - - + + - + - + + + + - + - + - + - - - - + - - - - + - + + + + - + + + + + + + - + - + - + - + - + - + - - - - - - - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + - + - + - + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - + - + - + - + - + - + - + - + - - + + - - - + + + - - - - - + + diff --git a/dev/gallery/gallery/statistical analyses/histograms/index.html b/dev/gallery/gallery/statistical analyses/histograms/index.html index aaf6ad937..a138f63fc 100644 --- a/dev/gallery/gallery/statistical analyses/histograms/index.html +++ b/dev/gallery/gallery/statistical analyses/histograms/index.html @@ -8,4 +8,4 @@ plt = data(df) * mapping(:x, color=:c, stack=:c) * histogram(bins=20) fg = draw(plt)

df = (x=rand(1000), y=randn(1000))
 plt = data(df) * mapping(:x, :y) * histogram(bins=20)
-draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

+draw(plt)

This page was generated using DemoCards.jl and Literate.jl.

diff --git a/dev/gallery/gallery/statistical analyses/regression_plots-3.svg b/dev/gallery/gallery/statistical analyses/regression_plots-3.svg index 088a487c8..69586bacc 100644 --- a/dev/gallery/gallery/statistical analyses/regression_plots-3.svg +++ b/dev/gallery/gallery/statistical analyses/regression_plots-3.svg @@ -2,52 +2,49 @@ - + - + - - - - + - + - + - - - - + - + + + + + + + - + - + - - - - + - + - + - + @@ -55,166 +52,166 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/statistical analyses/regression_plots-4.svg b/dev/gallery/gallery/statistical analyses/regression_plots-4.svg index fedd4d3cc..819fd23cf 100644 --- a/dev/gallery/gallery/statistical analyses/regression_plots-4.svg +++ b/dev/gallery/gallery/statistical analyses/regression_plots-4.svg @@ -2,234 +2,215 @@ - + - + - + - - - - - + + - + - + - - - - + - - - - + - + + + + - - + + - - + + - + - - - - - - - - + + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/dev/gallery/gallery/statistical analyses/regression_plots/index.html b/dev/gallery/gallery/statistical analyses/regression_plots/index.html index 89b45c294..bc756eed9 100644 --- a/dev/gallery/gallery/statistical analyses/regression_plots/index.html +++ b/dev/gallery/gallery/statistical analyses/regression_plots/index.html @@ -11,4 +11,4 @@ df = (; x, y) xy = data(df) * mapping(:x, :y) layers = smooth() + visual(Scatter) -fg = draw(layers * xy)

This page was generated using DemoCards.jl and Literate.jl.

+fg = draw(layers * xy)

This page was generated using DemoCards.jl and Literate.jl.

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card image

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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/dev/generated/analyses/d760e238.svg b/dev/generated/analyses/d760e238.svg new file mode 100644 index 000000000..cf413e0fa --- /dev/null +++ b/dev/generated/analyses/d760e238.svg @@ -0,0 +1,340 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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dev/generated/analyses/efa5bcfb.svg index c3ef3f787..efdd4e986 100644 --- a/dev/generated/analyses/c7065ba7.svg +++ b/dev/generated/analyses/efa5bcfb.svg @@ -2,121 +2,121 @@ - + - - - - + - + - - - - + - + + + + - + - + - + - - - - - - - + - + - - + + - + - + - - - - + - + - - - - + - + - - + + - + - + - + - + + + + + + + + + + - + - + - + - + + + + + + + - + - + - + - + - + - + - + @@ -126,190 +126,190 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - - + + + - + - + - + - - - + + + - + - + - + - + - + - - - - - - + + + + + + - - - - - - + + + + + + diff --git a/dev/generated/analyses/index.html b/dev/generated/analyses/index.html index f42cebd58..f8d71c0a0 100644 --- a/dev/generated/analyses/index.html +++ b/dev/generated/analyses/index.html @@ -1,54 +1,54 @@ -Analyses · Algebra of Graphics
GitHub

Analyses

Histogram

AlgebraOfGraphics.histogram — Function
histogram(; bins=automatic, datalimits=automatic, closed=:left, normalization=:none)

Compute a histogram.

The attribute bins can be an Integer, an AbstractVector (in particular, a range), or a Tuple of either integers or abstract vectors (useful for 2- or 3-dimensional histograms). When bins is an Integer, it denotes the approximate number of equal-width intervals used to compute the histogram. In that case, the range covered by the intervals is defined by datalimits (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. When bins is an AbstractVector, it denotes the intervals directly.

closed determines whether the the intervals are closed to the left or to the right.

The histogram can be normalized by setting normalization. Possible values are:

  • :pdf: Normalize by sum of weights and bin sizes. Resulting histogram has norm 1 and represents a PDF.
  • :density: Normalize by bin sizes only. Resulting histogram represents count density of input and does not have norm 1.
  • :probability: Normalize by sum of weights only. Resulting histogram represents the fraction of probability mass for each bin and does not have norm 1.
  • :none: Do not normalize.

Weighted data is supported via the keyword weights (passed to mapping).

Note

Normalizations are computed withing groups. For example, in the case of normalization=:pdf, sum of weights within each group will be equal to 1.

source
using AlgebraOfGraphics, CairoMakie
+Analyses · Algebra of Graphics
GitHub
Analyses
Histogram
AlgebraOfGraphics.histogram — Function
histogram(; bins=automatic, datalimits=automatic, closed=:left, normalization=:none)
Compute a histogram.
The attribute bins can be an Integer, an AbstractVector (in particular, a range), or a Tuple of either integers or abstract vectors (useful for 2- or 3-dimensional histograms). When bins is an Integer, it denotes the approximate number of equal-width intervals used to compute the histogram. In that case, the range covered by the intervals is defined by datalimits (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. When bins is an AbstractVector, it denotes the intervals directly.
closed determines whether the the intervals are closed to the left or to the right.
The histogram can be normalized by setting normalization. Possible values are:
  • :pdf: Normalize by sum of weights and bin sizes. Resulting histogram  has norm 1 and represents a PDF.
  • :density: Normalize by bin sizes only. Resulting histogram represents  count density of input and does not have norm 1.
  • :probability: Normalize by sum of weights only. Resulting histogram  represents the fraction of probability mass for each bin and does not have  norm 1.
  • :none: Do not normalize.
Weighted data is supported via the keyword weights (passed to mapping).
Note
Normalizations are computed withing groups. For example, in the case of normalization=:pdf, sum of weights within each group will be equal to 1.
source
using AlgebraOfGraphics, CairoMakie
 set_aog_theme!()
 
 df = (x=randn(5000), y=randn(5000), z=rand(["a", "b", "c"], 5000))
 specs = data(df) * mapping(:x, layout=:z) * histogram(bins=range(-2, 2, length=15))
-draw(specs)
Example block output
specs = data(df) * mapping(:x, dodge=:z, color=:z) * histogram(bins=range(-2, 2, length=15))
-draw(specs)
Example block output
specs = data(df) * mapping(:x, stack=:z, color=:z) * histogram(bins=range(-2, 2, length=15))
-draw(specs)
Example block output
specs = data(df) *
+draw(specs)
Example block output
specs = data(df) * mapping(:x, dodge=:z, color=:z) * histogram(bins=range(-2, 2, length=15))
+draw(specs)
Example block output
specs = data(df) * mapping(:x, stack=:z, color=:z) * histogram(bins=range(-2, 2, length=15))
+draw(specs)
Example block output
specs = data(df) *
     mapping((:x, :z) => ((x, z) -> x + 5 * (z == "b")) => "new x", col=:z) *
     histogram(datalimits=extrema, bins=20)
-draw(specs, facet=(linkxaxes=:minimal,))
Example block output
data(df) * mapping(:x, :y, layout=:z) * histogram(bins=15) |> draw
Example block output
Density
AlgebraOfGraphics.density — Function
density(; datalimits=automatic, kernel=automatic, bandwidth=automatic, npoints=200)
Fit a kernel density estimation of data.
Here, datalimits specifies the range for which the density should be calculated (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. The keyword arguments kernel and bandwidth are forwarded to KernelDensity.kde. npoints is the number of points used by Makie to draw the line
Weighted data is supported via the keyword weights (passed to mapping).
source
df = (x=randn(5000), y=randn(5000), z=rand(["a", "b", "c", "d"], 5000))
+draw(specs, facet=(linkxaxes=:minimal,))
Example block output
data(df) * mapping(:x, :y, layout=:z) * histogram(bins=15) |> draw
Example block output
Density
AlgebraOfGraphics.density — Function
density(; datalimits=automatic, kernel=automatic, bandwidth=automatic, npoints=200)
Fit a kernel density estimation of data.
Here, datalimits specifies the range for which the density should be calculated (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. The keyword arguments kernel and bandwidth are forwarded to KernelDensity.kde. npoints is the number of points used by Makie to draw the line
Weighted data is supported via the keyword weights (passed to mapping).
source
df = (x=randn(5000), y=randn(5000), z=rand(["a", "b", "c", "d"], 5000))
 specs = data(df) * mapping(:x, layout=:z) * AlgebraOfGraphics.density(datalimits=((-2.5, 2.5),))
 
-draw(specs)
Example block output
specs = data(df) *
+draw(specs)
Example block output
specs = data(df) *
     mapping((:x, :z) => ((x, z) -> x + 5 * (z ∈ ["b", "d"])) => "new x", layout=:z) *
     AlgebraOfGraphics.density(datalimits=extrema)
-draw(specs, facet=(linkxaxes=:minimal,))
Example block output
data(df) * mapping(:x, :y, layout=:z) * AlgebraOfGraphics.density(npoints=50) |> draw
Example block output
specs = data(df) * mapping(:x, :y, layout=:z) *
+draw(specs, facet=(linkxaxes=:minimal,))
Example block output
data(df) * mapping(:x, :y, layout=:z) * AlgebraOfGraphics.density(npoints=50) |> draw
Example block output
specs = data(df) * mapping(:x, :y, layout=:z) *
     AlgebraOfGraphics.density(npoints=50) * visual(Surface)
 
-draw(specs, axis=(type=Axis3, zticks=0:0.1:0.2, limits=(nothing, nothing, (0, 0.2))))
Example block output
Frequency
df = (x=rand(["a", "b", "c"], 100), y=rand(["a", "b", "c"], 100), z=rand(["a", "b", "c"], 100))
+draw(specs, axis=(type=Axis3, zticks=0:0.1:0.2, limits=(nothing, nothing, (0, 0.2))))
Example block output
Frequency
df = (x=rand(["a", "b", "c"], 100), y=rand(["a", "b", "c"], 100), z=rand(["a", "b", "c"], 100))
 specs = data(df) * mapping(:x, layout=:z) * frequency()
-draw(specs)
Example block output
specs = data(df) * mapping(:x, layout=:z, color=:y, stack=:y) * frequency()
-draw(specs)
Example block output
specs = data(df) * mapping(:x, :y, layout=:z) * frequency()
-draw(specs)
Example block output
Expectation
df = (x=rand(["a", "b", "c"], 100), y=rand(["a", "b", "c"], 100), z=rand(100), c=rand(["a", "b", "c"], 100))
+draw(specs)
Example block output
specs = data(df) * mapping(:x, layout=:z, color=:y, stack=:y) * frequency()
+draw(specs)
Example block output
specs = data(df) * mapping(:x, :y, layout=:z) * frequency()
+draw(specs)
Example block output
Expectation
df = (x=rand(["a", "b", "c"], 100), y=rand(["a", "b", "c"], 100), z=rand(100), c=rand(["a", "b", "c"], 100))
 specs = data(df) * mapping(:x, :z, layout=:c) * expectation()
-draw(specs)
Example block output
specs = data(df) * mapping(:x, :z, layout=:c, color=:y, dodge=:y) * expectation()
-draw(specs)
Example block output
specs = data(df) * mapping(:x, :y, :z, layout=:c) * expectation()
-draw(specs)
Example block output
Linear
AlgebraOfGraphics.linear — Function
linear(; interval=automatic, level=0.95, dropcollinear=false, npoints=200)
Compute a linear fit of y ~ 1 + x. An optional named mapping weights determines the weights. Use interval to specify what type of interval the shaded band should represent, for a given coverage level (the default 0.95 equates alpha = 0.05). Valid values of interval are :confidence, to delimit the uncertainty of the predicted relationship, and :prediction, to delimit estimated bounds for new data points. Use interval = nothing to only compute the line fit, without any uncertainty estimate.  By default, this analysis errors on singular (collinear) data. To avoid that, it is possible to set dropcollinear=true. npoints is the number of points used by Makie to draw the shaded band.
Weighted data is supported via the keyword weights (passed to mapping).
source
x = 1:0.05:10
+draw(specs)
Example block output
specs = data(df) * mapping(:x, :z, layout=:c, color=:y, dodge=:y) * expectation()
+draw(specs)
Example block output
specs = data(df) * mapping(:x, :y, :z, layout=:c) * expectation()
+draw(specs)
Example block output
Linear
AlgebraOfGraphics.linear — Function
linear(; interval=automatic, level=0.95, dropcollinear=false, npoints=200)
Compute a linear fit of y ~ 1 + x. An optional named mapping weights determines the weights. Use interval to specify what type of interval the shaded band should represent, for a given coverage level (the default 0.95 equates alpha = 0.05). Valid values of interval are :confidence, to delimit the uncertainty of the predicted relationship, and :prediction, to delimit estimated bounds for new data points. Use interval = nothing to only compute the line fit, without any uncertainty estimate.  By default, this analysis errors on singular (collinear) data. To avoid that, it is possible to set dropcollinear=true. npoints is the number of points used by Makie to draw the shaded band.
Weighted data is supported via the keyword weights (passed to mapping).
source
x = 1:0.05:10
 a = rand(1:7, length(x))
 y = 1.2 .* x .+ a .+ 0.5 .* randn.()
 df = (; x, y, a)
 specs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (linear() + visual(Scatter))
-draw(specs)
Example block output
Smoothing
AlgebraOfGraphics.smooth — Function
smooth(; span=0.75, degree=2, npoints=200)
Fit a loess model. span is the degree of smoothing, typically in [0,1]. Smaller values result in smaller local context in fitting. degree is the polynomial degree used in the loess model. npoints is the number of points used by Makie to draw the line
source
x = 1:0.05:10
+draw(specs)
Example block output
Smoothing
AlgebraOfGraphics.smooth — Function
smooth(; span=0.75, degree=2, npoints=200)
Fit a loess model. span is the degree of smoothing, typically in [0,1]. Smaller values result in smaller local context in fitting. degree is the polynomial degree used in the loess model. npoints is the number of points used by Makie to draw the line
source
x = 1:0.05:10
 a = rand(1:7, length(x))
 y = sin.(x) .+ a .+ 0.1 .* randn.()
 df = (; x, y, a)
 specs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (smooth() + visual(Scatter))
-draw(specs)
Example block output
Contours
AlgebraOfGraphics.contours — Function
contours(; levels=5, kwargs...)
Create contour lines over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. 
You can pass the number of levels as an integer or a vector of levels. The levels are calculated across the whole z data if they are specified as an integer.
Note that visual(Contour) only works in a limited way with AlgebraOfGraphics since version 0.7, because the internal calculations it does are not compatible with the scale system. With visual(Contour), you can only have categorically-colored contours (for example to visualize contours of multiple categories). Alternatively, if you set the colormap attribute, you can get continuously-colored contours but the levels will not be known to AlgebraOfGraphics, so they won't be synchronized across facets and there will not be a colorbar.
All other keyword arguments are forwarded as attributes to the underlying Contour plot.
source
x = repeat(1:10, 10)
+draw(specs)
Example block output
Contours
AlgebraOfGraphics.contours — Function
contours(; levels=5, kwargs...)
Create contour lines over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. 
You can pass the number of levels as an integer or a vector of levels. The levels are calculated across the whole z data if they are specified as an integer.
Note that visual(Contour) only works in a limited way with AlgebraOfGraphics since version 0.7, because the internal calculations it does are not compatible with the scale system. With visual(Contour), you can only have categorically-colored contours (for example to visualize contours of multiple categories). Alternatively, if you set the colormap attribute, you can get continuously-colored contours but the levels will not be known to AlgebraOfGraphics, so they won't be synchronized across facets and there will not be a colorbar.
All other keyword arguments are forwarded as attributes to the underlying Contour plot.
source
x = repeat(1:10, 10)
 y = repeat(11:20, inner = 10)
 z = sqrt.(x .* y)
 df = (; x, y, z)
 specs = data(df) * mapping(:x, :y, :z) * contours(levels = 8)
-draw(specs)
Example block output
x = repeat(1:10, 10)
+draw(specs)
Example block output
x = repeat(1:10, 10)
 y = repeat(11:20, inner = 10)
 z = sqrt.(x .* y)
 df = (; x, y, z)
 specs = data(df) * mapping(:x, :y, :z) * contours(levels = 8, labels = true)
-draw(specs)
Example block output
Filled Contours
AlgebraOfGraphics.filled_contours — Function
filled_contours(; bands=automatic, levels=automatic)
Create filled contours over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. 
You can pass either the number of bands to bands or pass a vector of levels (the boundaries of the bands) to levels, but not both. The number of bands when levels is passed is length(levels) - 1. The levels are calculated across the whole z data if the number of bands is specified. If neither levels nor bands are specified, the default is bands = 10.
Note that visual(Contourf) does not work with AlgebraOfGraphics since version 0.7, because the internal binning it does is not compatible with the scale system.
source
x = repeat(1:10, 10)
+draw(specs)
Example block output
Filled Contours
AlgebraOfGraphics.filled_contours — Function
filled_contours(; bands=automatic, levels=automatic)
Create filled contours over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. 
You can pass either the number of bands to bands or pass a vector of levels (the boundaries of the bands) to levels, but not both. The number of bands when levels is passed is length(levels) - 1. The levels are calculated across the whole z data if the number of bands is specified. If neither levels nor bands are specified, the default is bands = 10.
Note that visual(Contourf) does not work with AlgebraOfGraphics since version 0.7, because the internal binning it does is not compatible with the scale system.
source
x = repeat(1:10, 10)
 y = repeat(11:20, inner = 10)
 z = sqrt.(x .* y)
 df = (; x, y, z)
 specs = data(df) * mapping(:x, :y, :z) * filled_contours(levels = 3:2:15)
-draw(specs)
Example block output
This page was generated using Literate.jl.

+draw(specs)
Example block output

This page was generated using Literate.jl.

diff --git a/dev/generated/penguins/f43f7b9f.svg b/dev/generated/penguins/018ebc8e.svg similarity index 99% rename from dev/generated/penguins/f43f7b9f.svg rename to dev/generated/penguins/018ebc8e.svg index 7ea86d775..28c107d7e 100644 --- a/dev/generated/penguins/f43f7b9f.svg +++ b/dev/generated/penguins/018ebc8e.svg @@ -1,244 +1,244 @@ - + - - - + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + @@ -258,225 +258,225 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/generated/penguins/70df9e00.svg b/dev/generated/penguins/1d804339.svg similarity index 96% rename from dev/generated/penguins/70df9e00.svg rename to dev/generated/penguins/1d804339.svg index 2ad9d11f0..65126578e 100644 --- a/dev/generated/penguins/70df9e00.svg +++ b/dev/generated/penguins/1d804339.svg @@ -2,409 +2,409 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -745,61 +745,61 @@ - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -815,88 +815,88 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/generated/penguins/a942f40e.svg b/dev/generated/penguins/24d92674.svg similarity index 99% rename from dev/generated/penguins/a942f40e.svg rename to dev/generated/penguins/24d92674.svg index 77171f71b..78a6f0d0d 100644 --- a/dev/generated/penguins/a942f40e.svg +++ b/dev/generated/penguins/24d92674.svg @@ -2,401 +2,401 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -2899,49 +2899,49 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -5444,49 +5444,49 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -7989,135 +7989,135 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - + - + - + - + diff --git a/dev/generated/penguins/fa05a24f.svg b/dev/generated/penguins/2ac71d18.svg similarity index 99% rename from dev/generated/penguins/fa05a24f.svg rename to dev/generated/penguins/2ac71d18.svg index 8ca5a3828..eed2e26ba 100644 --- a/dev/generated/penguins/fa05a24f.svg +++ b/dev/generated/penguins/2ac71d18.svg @@ -2,410 +2,410 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -2908,49 +2908,49 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -5453,49 +5453,49 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -7998,147 +7998,147 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/generated/penguins/7e60695c.svg b/dev/generated/penguins/589f56b7.svg similarity index 96% rename from dev/generated/penguins/7e60695c.svg rename to dev/generated/penguins/589f56b7.svg index 19ef889a8..2e2e995e9 100644 --- a/dev/generated/penguins/7e60695c.svg +++ b/dev/generated/penguins/589f56b7.svg @@ -1,266 +1,266 @@ - + - - - + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + @@ -277,249 +277,249 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/generated/penguins/6aa2613c.svg b/dev/generated/penguins/645452fc.svg similarity index 95% rename from dev/generated/penguins/6aa2613c.svg rename to dev/generated/penguins/645452fc.svg index 6b4b10cf8..5c237abcf 100644 --- a/dev/generated/penguins/6aa2613c.svg +++ b/dev/generated/penguins/645452fc.svg @@ -1,505 +1,505 @@ - + - - - + + + - - - + + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + @@ -531,568 +531,568 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - - + + - - + + - + - + - + - + - + diff --git a/dev/generated/penguins/index.html b/dev/generated/penguins/index.html index 4c50fa07c..ece90ac27 100644 --- a/dev/generated/penguins/index.html +++ b/dev/generated/penguins/index.html @@ -31,10 +31,10 @@ plt = penguin_bill * layers * mapping(color = :species, col = :sex) draw(plt; axis = axis)Example block output

Smooth density plots

An alternative approach to understanding how two variables interact is to consider their joint probability density distribution (pdf).

using AlgebraOfGraphics: density
 plt = penguin_bill * density(npoints=50) * mapping(col = :species)
-draw(plt; axis = axis)
Example block output

The default colormap is multi-hue, but it is possible to pass single-hue colormaps as well. The color range is inferred from the data by default, but it can also be passed manually. Both settings are passed via scales to draw, because multiple plots can share the same colormap, so visual is not the appropriate place for this setting.

draw(plt, scales(Color = (; colormap = :grayC, colorrange = (0, 6))); axis = axis)
Example block output

A Heatmap (the default visualization for a 2D density) is a bit unfortunate if we want to mark species by color. In that case, one can use visual to change the default visualization and, optionally, fine tune some arguments. In this case, a Wireframe with thin lines looks quite nice. (Note that, for the time being, we must specify explicitly that we require a 3D axis.)

axis = (type = Axis3, width = 300, height = 300)
+draw(plt; axis = axis)
Example block output

The default colormap is multi-hue, but it is possible to pass single-hue colormaps as well. The color range is inferred from the data by default, but it can also be passed manually. Both settings are passed via scales to draw, because multiple plots can share the same colormap, so visual is not the appropriate place for this setting.

draw(plt, scales(Color = (; colormap = :grayC, colorrange = (0, 6))); axis = axis)
Example block output

A Heatmap (the default visualization for a 2D density) is a bit unfortunate if we want to mark species by color. In that case, one can use visual to change the default visualization and, optionally, fine tune some arguments. In this case, a Wireframe with thin lines looks quite nice. (Note that, for the time being, we must specify explicitly that we require a 3D axis.)

axis = (type = Axis3, width = 300, height = 300)
 layer = density() * visual(Wireframe, linewidth=0.05)
 plt = penguin_bill * layer * mapping(color = :species)
-draw(plt; axis = axis)
Example block output

Of course, a more traditional approach would be to use a Contour plot instead:

axis = (width = 225, height = 225)
+draw(plt; axis = axis)
Example block output

Of course, a more traditional approach would be to use a Contour plot instead:

axis = (width = 225, height = 225)
 layer = density() * visual(Contour)
 plt = penguin_bill * layer * mapping(color = :species)
 draw(plt; axis = axis)
Example block output

The data and the linear fit can also be added back to the plot:

layers = density() * visual(Contour) + linear() + mapping()
@@ -44,10 +44,10 @@
 draw(plt; axis = axis)
Example block output

Correlating three variables

We are now mostly up to speed with bill size, but we have not considered how it relates to other penguin features, such as their weight. For that, a possible approach is to use a continuous color on a gradient to denote weight and different marker shapes to denote species. Here we use group to split the data for the linear regression without adding any additional style.

body_mass = :body_mass_g => (t -> t / 1000) => "body mass (kg)"
 layers = linear() * mapping(group = :species) + mapping(color = body_mass, marker = :species)
 plt = penguin_bill * layers
-draw(plt; axis = axis)
Example block output

Naturally, within each species, heavier penguins have bigger bills, but perhaps counter-intuitively the species with the shallowest bills features the heaviest penguins. We could also try and see the interplay of these three variables in a 3D plot.

axis = (type = Axis3, width = 300, height = 300)
+draw(plt; axis = axis)
Example block output

Naturally, within each species, heavier penguins have bigger bills, but perhaps counter-intuitively the species with the shallowest bills features the heaviest penguins. We could also try and see the interplay of these three variables in a 3D plot.

axis = (type = Axis3, width = 300, height = 300)
 plt = penguin_bill * mapping(body_mass, color = :species)
-draw(plt; axis = axis)
Example block output
plt = penguin_bill * mapping(body_mass, color = :species, layout = :sex)
-draw(plt; axis = axis)
Example block output

Note that static 3D plot can be misleading, as they only show one projection of 3D data. They are mostly useful when shown interactively.

Machine Learning

Finally, let us use Machine Learning techniques to build an automated penguin classifier!

We would like to investigate whether it is possible to predict the species of a penguin based on its bill size. To do so, we will use a standard classifier technique called Support-Vector Machine.

The strategy is quite simple. We split the data into training and testing subdatasets. We then train our classifier on the training dataset and use it to make predictions on the whole data. We then add the new columns obtained this way to the dataset and visually inspect how well the classifier performed in both training and testing.

using LIBSVM, Random
+draw(plt; axis = axis)
Example block output
plt = penguin_bill * mapping(body_mass, color = :species, layout = :sex)
+draw(plt; axis = axis)
Example block output

Note that static 3D plot can be misleading, as they only show one projection of 3D data. They are mostly useful when shown interactively.

Machine Learning

Finally, let us use Machine Learning techniques to build an automated penguin classifier!

We would like to investigate whether it is possible to predict the species of a penguin based on its bill size. To do so, we will use a standard classifier technique called Support-Vector Machine.

The strategy is quite simple. We split the data into training and testing subdatasets. We then train our classifier on the training dataset and use it to make predictions on the whole data. We then add the new columns obtained this way to the dataset and visually inspect how well the classifier performed in both training and testing.

using LIBSVM, Random
 
 # use approximately 80% of penguins for training
 Random.seed!(1234) # for reproducibility
@@ -84,4 +84,4 @@
 draw(plt; axis = axis)
Example block output

Um, some of the penguins are indeed being misclassified... Let us try to understand why by adding an extra layer, which describes the density of the distributions of the three species.

pdflayer = density() * visual(Contour, colormap=Reverse(:grays)) * mapping(group = :species)
 layers = pdflayer + datalayer
 plt = penguin_bill * layers
-draw(plt; axis = axis)
Example block output

We can conclude that the classifier is doing a reasonable job: it is mostly making mistakes on outlier penguins.

This page was generated using Literate.jl.

+draw(plt; axis = axis)Example block output

We can conclude that the classifier is doing a reasonable job: it is mostly making mistakes on outlier penguins.

This page was generated using Literate.jl.

diff --git a/dev/generated/visual/56cb1f05.svg b/dev/generated/visual/0142524b.svg similarity index 99% rename from dev/generated/visual/56cb1f05.svg rename to dev/generated/visual/0142524b.svg index 683c55062..21637a6bf 100644 --- a/dev/generated/visual/56cb1f05.svg +++ b/dev/generated/visual/0142524b.svg @@ -2,106 +2,106 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -2603,52 +2603,52 @@ - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/generated/visual/f499c39b.svg b/dev/generated/visual/85951a1d.svg similarity index 99% rename from dev/generated/visual/f499c39b.svg rename to dev/generated/visual/85951a1d.svg index 3f2c91464..b565b3e3f 100644 --- a/dev/generated/visual/f499c39b.svg +++ b/dev/generated/visual/85951a1d.svg @@ -2,106 +2,106 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -2603,52 +2603,52 @@ - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/generated/visual/index.html b/dev/generated/visual/index.html index a0cf8559d..16717d8b6 100644 --- a/dev/generated/visual/index.html +++ b/dev/generated/visual/index.html @@ -1,10 +1,10 @@ -Visual · Algebra of Graphics
GitHub

Visual

AlgebraOfGraphics.visual — Function
visual(plottype; attributes...)

Create a Layer that will cause a plot spec multiplied with it to be visualized with plot type plottype, together with optional attributes.

The available plotting functions are documented here. Refer to plotting functions using upper CamelCase for visual's first argument (e.g. visual(Scatter), visual(BarPlot)). See the documentation of each plotting function to discover the available attributes. These attributes can be passed as additional keyword arguments to visual, or as part of the mapping you define.

The visual function can in principle be used for any plotting function that is defined using the @recipe macro from Makie. AlgebraOfGraphics just needs method definitions for aesthetic_mapping, which define what arguments of the plotting function map to which visual aesthetics. And for legend support, legend_elements must be overloaded for custom recipes as well, as Makie's default legend mechanism relies on instantiated plot objects, while AlgebraOfGraphics must go by the type and attributes alone.

Depending on its aesthetic_mapping, a plot type and its attributes may change certain semantics of a given data(...) * mapping(...) spec. For example, visual(BarPlot) will show mapping 1 on the x axis and 2 on the y axis, while visual(BarPlot, direction = :x) shows mapping 1 on y and 2 on x.

source

Examples

using AlgebraOfGraphics, CairoMakie
+Visual · Algebra of Graphics
GitHub
Visual
AlgebraOfGraphics.visual — Function
visual(plottype; attributes...)
Create a Layer that will cause a plot spec multiplied with it to be visualized with plot type plottype, together with optional attributes.
The available plotting functions are documented here. Refer to plotting functions using upper CamelCase for visual's first argument (e.g. visual(Scatter), visual(BarPlot)). See the documentation of each plotting function to discover the available attributes. These attributes can be passed as additional keyword arguments to visual, or as part of the mapping you define.
The visual function can in principle be used for any plotting function that is defined using the @recipe macro from Makie. AlgebraOfGraphics just needs method definitions for aesthetic_mapping, which define what arguments of the plotting function map to which visual aesthetics. And for legend support, legend_elements must be overloaded for custom recipes as well, as Makie's default legend mechanism relies on instantiated plot objects, while AlgebraOfGraphics must go by the type and attributes alone.
Depending on its aesthetic_mapping, a plot type and its attributes may change certain semantics of a given data(...) * mapping(...) spec. For example, visual(BarPlot) will show mapping 1 on the x axis and 2 on the y axis, while visual(BarPlot, direction = :x) shows mapping 1 on y and 2 on x.
source
Examples
using AlgebraOfGraphics, CairoMakie
 set_aog_theme!()
 
 df = (x=randn(1000), y=randn(1000))
 plt = data(df) * mapping(:x, :y) * AlgebraOfGraphics.density(npoints=50)
-draw(plt * visual(Heatmap)) # plot as heatmap (the default)
Example block output
From AlgebraOfGraphics version 0.7 on, some attributes of the underlying Makie functions will not have an effect if they are controlled by scales instead. For example, continuous colors are completely controlled by color scales, so setting colormap in visual does not have an effect.
Set the colormap in the Scale options instead.
draw(plt, scales(Color = (; colormap = :viridis))) # set a different colormap
Example block output
draw(plt * visual(Contour)) # plot as contour
Example block output
draw(plt * visual(Contour, linewidth=2)) # plot as contour with thicker lines
Example block output
Manual legend entries via label
The legend normally contains entries for all appropriate scales used in the plot. Sometimes, however, you just want to label certain plots such that they appear in the legend without using any scale. You can achieve this by adding the label keyword to all visuals that you want to label. Layers with the same label will be combined within a legend entry.
x = range(0, 4pi, length = 40)
+draw(plt * visual(Heatmap)) # plot as heatmap (the default)
Example block output
From AlgebraOfGraphics version 0.7 on, some attributes of the underlying Makie functions will not have an effect if they are controlled by scales instead. For example, continuous colors are completely controlled by color scales, so setting colormap in visual does not have an effect.
Set the colormap in the Scale options instead.
draw(plt, scales(Color = (; colormap = :viridis))) # set a different colormap
Example block output
draw(plt * visual(Contour)) # plot as contour
Example block output
draw(plt * visual(Contour, linewidth=2)) # plot as contour with thicker lines
Example block output
Manual legend entries via label
The legend normally contains entries for all appropriate scales used in the plot. Sometimes, however, you just want to label certain plots such that they appear in the legend without using any scale. You can achieve this by adding the label keyword to all visuals that you want to label. Layers with the same label will be combined within a legend entry.
x = range(0, 4pi, length = 40)
 layer1 = data((; x = x, y = cos.(x))) * mapping(:x, :y) * visual(Lines, linestyle = :dash, label = "A cosine line")
 layer2 = data((; x = x, y = sin.(x) .+ 2)) * mapping(:x, :y) *
     (visual(Lines, color = (:tomato, 0.4)) + visual(Scatter, color = :tomato)) * visual(label = "A sine line + scatter")
@@ -18,4 +18,4 @@
 legend!(f[1, 2], fg)
 legend!(f[1, 3], fg, order = [:Label, :Color])
 
-f
Example block output
This page was generated using Literate.jl.

+f
Example block output

This page was generated using Literate.jl.

diff --git a/dev/index.html b/dev/index.html index 0427e2f80..af924904a 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · Algebra of Graphics
GitHub

Introduction

AlgebraOfGraphics defines a language for data visualization. It is based on a few simple building blocks that can be combined using + and *.

This package can be installed typing

julia> import Pkg; Pkg.add("AlgebraOfGraphics")

in the Julia REPL.

See the Tutorial to get started.

+Home · Algebra of Graphics
GitHub

Introduction

AlgebraOfGraphics defines a language for data visualization. It is based on a few simple building blocks that can be combined using + and *.

This package can be installed typing

julia> import Pkg; Pkg.add("AlgebraOfGraphics")

in the Julia REPL.

See the Tutorial to get started.

diff --git a/dev/layers/data/index.html b/dev/layers/data/index.html index ae55cb0e4..2d90ddc4e 100644 --- a/dev/layers/data/index.html +++ b/dev/layers/data/index.html @@ -1,9 +1,9 @@ -Data · Algebra of Graphics
GitHub

Data

AlgebraOfGraphics.data — Function
data(table)

Create a Layer with its data field set to a table-like object.

There are no type restrictions on this object, as long as it respects the Tables interface. In particular, any one of these formats should work out of the box.

To create a fully specified layer, the layer created with data needs to be multiplied with the output of mapping.

spec = data(...) * mapping(...)
source
using AlgebraOfGraphics
+Data · Algebra of Graphics
GitHub
Data
AlgebraOfGraphics.data — Function
data(table)
Create a Layer with its data field set to a table-like object.
There are no type restrictions on this object, as long as it respects the Tables interface. In particular, any one of these formats should work out of the box.
To create a fully specified layer, the layer created with data needs to be multiplied with the output of mapping.
spec = data(...) * mapping(...)
source
using AlgebraOfGraphics
 df = (a = rand(10), b = rand(10))
 data(df)
Layer 
   transformation: identity
   data: AlgebraOfGraphics.Columns{@NamedTuple{a::Vector{Float64}, b::Vector{Float64}}}
   positional:
   named:
-

+
diff --git a/dev/layers/draw/61d2196e.svg b/dev/layers/draw/0ba38e8a.svg similarity index 90% rename from dev/layers/draw/61d2196e.svg rename to dev/layers/draw/0ba38e8a.svg index 4df7ba0a7..d99a2acf7 100644 --- a/dev/layers/draw/61d2196e.svg +++ b/dev/layers/draw/0ba38e8a.svg @@ -2,380 +2,380 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -385,34 +385,34 @@ - + - + - + - + - + - + - + - + - + - + @@ -440,49 +440,49 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -535,19 +535,19 @@ - + - + - + - + - + @@ -600,19 +600,19 @@ - + - + - + - + - + @@ -640,354 +640,354 @@ - + - + - + - + - + - + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/dev/layers/draw/c5e33dd9.svg b/dev/layers/draw/50e3bffb.svg similarity index 92% rename from dev/layers/draw/c5e33dd9.svg rename to dev/layers/draw/50e3bffb.svg index f1628d0ee..37f13787c 100644 --- a/dev/layers/draw/c5e33dd9.svg +++ b/dev/layers/draw/50e3bffb.svg @@ -2,121 +2,121 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + @@ -128,36 +128,36 @@ - - + + - + - + - + - + - + - + - + - + - + diff --git a/dev/layers/draw/index.html b/dev/layers/draw/index.html index 01fbffd1b..96f14ff3a 100644 --- a/dev/layers/draw/index.html +++ b/dev/layers/draw/index.html @@ -116,7 +116,7 @@ highclip = :red, colorrange = (2, 9) ) -))Example block output

MarkerSize

The range of marker sizes can be set with the sizerange attribute. Marker sizes are computed such that their area, and not markersize itself, grows linearly with the scale values. 

using AlgebraOfGraphics
+))
Example block output

MarkerSize

The range of marker sizes can be set with the sizerange attribute. Marker sizes are computed such that their area, and not markersize itself, grows linearly with the scale values. 

using AlgebraOfGraphics
 using CairoMakie
 
 spec = data((; x = 1:10, y = 1:10, z = 10:10:100)) *
@@ -185,4 +185,4 @@
         ],
     ),
     axis = (; width = 100, height = 100)
-)
Example block output +)Example block output diff --git a/dev/layers/introduction/index.html b/dev/layers/introduction/index.html index 78b2b2f71..baf5918fc 100644 --- a/dev/layers/introduction/index.html +++ b/dev/layers/introduction/index.html @@ -1,2 +1,2 @@ -Introduction · Algebra of Graphics
GitHub

Introduction

Layers are the key building blocks of AlgebraOfGraphics. Each layer is the product of the following elementary objects.

  • Data (encoding the dataset).
  • Mapping (associating variables to plot attributes).
  • Visual (encoding data-independent plot information).
  • Analyses (encoding transformations that are applied to the data before plotting).

Data, mappings, visuals and analyses can be combined together using Algebraic Operations to form one or more layers.

The output of these algebraic operations can be visualized, as shown in the section Drawing Layers.

+Introduction · Algebra of Graphics
GitHub

Introduction

Layers are the key building blocks of AlgebraOfGraphics. Each layer is the product of the following elementary objects.

  • Data (encoding the dataset).
  • Mapping (associating variables to plot attributes).
  • Visual (encoding data-independent plot information).
  • Analyses (encoding transformations that are applied to the data before plotting).

Data, mappings, visuals and analyses can be combined together using Algebraic Operations to form one or more layers.

The output of these algebraic operations can be visualized, as shown in the section Drawing Layers.

diff --git a/dev/layers/mapping/index.html b/dev/layers/mapping/index.html index 1fe37b3ab..c1a9e6597 100644 --- a/dev/layers/mapping/index.html +++ b/dev/layers/mapping/index.html @@ -12,7 +12,7 @@ )

Pregrouped

With data(Pregrouped()) * mapping(...) or the shortcut pregrouped(...), each element in mapping specifies input data directly, like with nothing. However, in this mode, data should be passed in pregrouped. Categorical variables should come as a vector of categories, while numerical variables should come as a vector of vectors of values, with as many inner vectors as there are groups in the categorical variables.

pregrouped(
     [[1, 2, 3], [4, 5]], # two grouped vectors, of length 3 and 2
     color = ["A", "B"]   # a vector with two categorical group values
-)
source

Aesthetics

The structure of a mapping is always directly tied to the signature of the plotting function (or analysis) that it is being connected with. What visual aspects of the plot the positional or keyword arguments affect depends on the plotting function in use.

To be used with AlgebraOfGraphics, a plotting function has to add a declaration which aesthetics (like X, Y, Color, MarkerSize, LineStyle) its arguments map to. This mechanism allows AlgebraOfGraphics to correctly convert the raw input data into visual attributes for each plotting function and to correctly create and label axes, colorbars and legends.

Aesthetics can also change depending on attributes passed to visual.

For example, for a BarPlot, args 1 and 2 correspond to the X and Y aesthetics by default. But if you change the direction in the visual, then axis labels shift accordingly because the aesthetic mapping has changed to 1 = Y, 2 = X:

using AlgebraOfGraphics
+)
source

Aesthetics

The structure of a mapping is always directly tied to the signature of the plotting function (or analysis) that it is being connected with. What visual aspects of the plot the positional or keyword arguments affect depends on the plotting function in use.

To be used with AlgebraOfGraphics, a plotting function has to add a declaration which aesthetics (like X, Y, Color, MarkerSize, LineStyle) its arguments map to. This mechanism allows AlgebraOfGraphics to correctly convert the raw input data into visual attributes for each plotting function and to correctly create and label axes, colorbars and legends.

Aesthetics can also change depending on attributes passed to visual.

For example, for a BarPlot, args 1 and 2 correspond to the X and Y aesthetics by default. But if you change the direction in the visual, then axis labels shift accordingly because the aesthetic mapping has changed to 1 = Y, 2 = X:

using AlgebraOfGraphics
 using CairoMakie
 
 df = (; name = ["Anna", "Beatrix", "Claire"], height_meters = [1.55, 1.76, 1.63])
@@ -101,4 +101,4 @@
       Color = (; palette = [:red, :green, :blue]),
       color2 = (; palette = [:gray30, :gray80]),
    )
-)
Example block output +)Example block output diff --git a/dev/layers/operations/index.html b/dev/layers/operations/index.html index 2d59fbfd4..cd991f2f5 100644 --- a/dev/layers/operations/index.html +++ b/dev/layers/operations/index.html @@ -1,2 +1,2 @@ -Algebraic Operations · Algebra of Graphics
GitHub

Algebraic Operations

There are two algebraic types that can be added or multiplied with each other: AlgebraOfGraphics.Layer and AlgebraOfGraphics.Layers.

Multiplication on individual layers

Each layer is composed of data, mappings, and transformations. Datasets can be replaced, mappings can be merged, and transformations can be concatenated. These operations, taken together, define an associative operation on layers, which we call multiplication *.

Multiplication is primarily useful to combine partially defined layers.

Addition

The operation + is used to superimpose separate layers. a + b has as many layers as la + lb, where la and lb are the number of layers in a and b respectively.

Multiplication on lists of layers

Multiplication naturally extends to lists of layers. Given two Layers objects a and b, containing la and lb layers respectively, the product a * b contains la * lb layers—all possible pair-wise products.

+Algebraic Operations · Algebra of Graphics
GitHub

Algebraic Operations

There are two algebraic types that can be added or multiplied with each other: AlgebraOfGraphics.Layer and AlgebraOfGraphics.Layers.

Multiplication on individual layers

Each layer is composed of data, mappings, and transformations. Datasets can be replaced, mappings can be merged, and transformations can be concatenated. These operations, taken together, define an associative operation on layers, which we call multiplication *.

Multiplication is primarily useful to combine partially defined layers.

Addition

The operation + is used to superimpose separate layers. a + b has as many layers as la + lb, where la and lb are the number of layers in a and b respectively.

Multiplication on lists of layers

Multiplication naturally extends to lists of layers. Given two Layers objects a and b, containing la and lb layers respectively, the product a * b contains la * lb layers—all possible pair-wise products.

diff --git a/dev/objects.inv b/dev/objects.inv index 0c58c68bb..2e5aa0bfa 100644 Binary files a/dev/objects.inv and b/dev/objects.inv differ diff --git a/dev/philosophy/index.html b/dev/philosophy/index.html index 7f49fb10f..bc1de63ff 100644 --- a/dev/philosophy/index.html +++ b/dev/philosophy/index.html @@ -12,4 +12,4 @@ draw(plt)Example block output

In this case, thanks to the distributive property, it is clear that the dataset and the positional arguments :y, :z are shared across layers, the transparency and the grouping are specific to the data layer, whereas the density analysis, the Contour visualization, and the choice of color map are specific to the analysis layer.

User-defined building blocks

It is common in data analysis tasks to "pipe" a sequence of operations. This became very popular in the data science field with the %>% operator in the R language, and it can allow users to seamlessly compose a sequence of tasks:

df %>%
     filter(Weight < 3) %>%
     group_by(Species) %>%
-    summarise(avg_height = mean(Height))

Naturally, the alternative would be to create a statement per operation and to assign each intermediate result to its own variable.

AlgebraOfGraphics is markedly in favor of the latter approach. It is recommended that commonly used building blocks are stored in variables with meaningful names. If we often make a scatter plot with some transparency, we can create a variable transparent_scatter = visual(Scatter, alpha = 0.5) and use it consistently. If some columns of our dataset are always analyzed together, with a similar set of transformations, we can store that information as variables = mapping(variable1 => f1 => label1, variable2 => f2 => label2).

Working over one or more datasets, the user would then create a library of building blocks to be combined with each other with * and +. These two operators allow for a much larger number of possible combinations than just sequential composition, thus fully justifying the extra characters used to name intermediate entities.

Opinionated defaults

While users should be able to customize every aspect of their plots, it is important to note that this customization can be very time-consuming, and many subtleties can escape the attention of the casual user:

To remedy this, AlgebraOfGraphics aims to provide solid, opinionated default settings. In particular, it uses a conservative, colorblind-friendly palette and a perceptually uniform, universally readable color map. It follows IBM guidelines to differentiate titles and labels from tick labels via font weight, while using the same typeface at a readable size.

Wide format support

Finally, AlgebraOfGraphics aims to support many different data formats. Different problems require organizing the data in different formats, and AlgebraOfGraphics should support a wide range of options.

This is achieved in three different ways. First, the Tables interface ensures integration with a large variety of data sources. Second, using the Wide data syntax, users can compare many different columns in the same visualization, without having to first reshape the dataset to a long format. Finally, tabular datasets are not a requirement: users may also work directly with Pre-grouped data, which are not organized as a table, but rather as a collection of (possibly multi-dimensional) arrays.

+ summarise(avg_height = mean(Height))

Naturally, the alternative would be to create a statement per operation and to assign each intermediate result to its own variable.

AlgebraOfGraphics is markedly in favor of the latter approach. It is recommended that commonly used building blocks are stored in variables with meaningful names. If we often make a scatter plot with some transparency, we can create a variable transparent_scatter = visual(Scatter, alpha = 0.5) and use it consistently. If some columns of our dataset are always analyzed together, with a similar set of transformations, we can store that information as variables = mapping(variable1 => f1 => label1, variable2 => f2 => label2).

Working over one or more datasets, the user would then create a library of building blocks to be combined with each other with * and +. These two operators allow for a much larger number of possible combinations than just sequential composition, thus fully justifying the extra characters used to name intermediate entities.

Opinionated defaults

While users should be able to customize every aspect of their plots, it is important to note that this customization can be very time-consuming, and many subtleties can escape the attention of the casual user:

To remedy this, AlgebraOfGraphics aims to provide solid, opinionated default settings. In particular, it uses a conservative, colorblind-friendly palette and a perceptually uniform, universally readable color map. It follows IBM guidelines to differentiate titles and labels from tick labels via font weight, while using the same typeface at a readable size.

Wide format support

Finally, AlgebraOfGraphics aims to support many different data formats. Different problems require organizing the data in different formats, and AlgebraOfGraphics should support a wide range of options.

This is achieved in three different ways. First, the Tables interface ensures integration with a large variety of data sources. Second, using the Wide data syntax, users can compare many different columns in the same visualization, without having to first reshape the dataset to a long format. Finally, tabular datasets are not a requirement: users may also work directly with Pre-grouped data, which are not organized as a table, but rather as a collection of (possibly multi-dimensional) arrays.

diff --git a/dev/search_index.js b/dev/search_index.js index 0f01f6822..451c6155c 100644 --- a/dev/search_index.js +++ b/dev/search_index.js @@ -1,3 +1,3 @@ var documenterSearchIndex = {"docs": -[{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/basic visualizations/statistical_visualizations.jl\"","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/#Statistical-visualizations","page":"Statistical visualizations","title":"Statistical visualizations","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"using AlgebraOfGraphics, CairoMakie, PalmerPenguins, DataFrames\n\npenguins = dropmissing(DataFrame(PalmerPenguins.load()))\n\ndata(penguins) * visual(Violin) *\n mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"plt = data(penguins) * visual(Violin, datalimits=extrema)\nplt *= mapping(:species, :bill_depth_mm, color=:sex, side=:sex, dodge=:island)\nfg = draw(plt, axis=(limits=((0.5, 3.5), nothing),))","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"data(penguins) * visual(BoxPlot, show_notch=true) *\n mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"data(penguins) *\n mapping(:bill_length_mm, :bill_depth_mm, col=:sex) *\n visual(QQPlot, qqline=:fit) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"FAQs/#Frequently-Asked-Questions","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"","category":"section"},{"location":"FAQs/#What-is-the-algebraic-structure-of-AlgebraOfGraphics?","page":"Frequently Asked Questions","title":"What is the algebraic structure of AlgebraOfGraphics?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"AlgebraOfGraphics is based on two operators, + and *. These two operators induce a semiring structure, with a small caveat. Addition is commutative only up to the drawing order. For example, visual(Lines) + visual(Scatter) is slightly different from visual(Scatter) + visual(Lines), in that the former draws the scatter on top of the lines, and the latter draws the lines on top of the scatter. As a consequence, only right distributivity holds with full generality, whereas left distributivity only holds up to the drawing order.","category":"page"},{"location":"FAQs/#Why-is-the-mapping-pair-syntax-different-from-DataFrames?","page":"Frequently Asked Questions","title":"Why is the mapping pair syntax different from DataFrames?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The transformations passed within a mapping, e.g. mapping(:x => log => \"log(x)\"), are applied element-wise. Operations that require the whole column are not supported on purpose. An important reason to prefer element-wise operations (other than performance) is that whole-column operations can be error prone in this setting, especially when","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"the data is grouped or\ndifferent datasets are used.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"If you do need column-wise transformations, consider implementing a custom analysis, such as density, which takes the whole data as input, or apply the transformation directly in your data before passing it to AlgebraOfGraphics.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"See also Pair syntax for a detailed description of the pair syntax within a mapping.","category":"page"},{"location":"FAQs/#What-is-the-difference-between-axis-scales-and-data-transformations?","page":"Frequently Asked Questions","title":"What is the difference between axis scales and data transformations?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"There are two overlapping but distinct ways to rescale data.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Keep the data as is and use a nonlinear scale, e.g. axis=(xscale=log,).\nTransform the data directly, e.g. mapping(:x => log => \"log(x)\").","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Note that the resulting plots may \"look different\" in some cases. Consider for instance the following example.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics\nusing AlgebraOfGraphics: density\ndf = (x = exp.(randn(1000)),)\nkde1 = data(df) * mapping(:x) * density()\ndraw(kde1, axis=(width=225, height=225, xscale=log,))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"df = (x = exp.(randn(1000)),)\nkde2 = data(df) * mapping(:x => log => \"log(x)\") * density()\ndraw(kde2, axis=(width=225, height=225))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The two plots look different. The first represents the pdf of x in a log scale, while the second represents the pdf of log(x) in a linear scale. The two curves differ by a factor 1 / x, the derivative of log(x). See e.g. this post for some mathematical background on the topic.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"In general, the second approach (plotting the density of log(x)) could be considered more principled, as it preserves the proportionality between area and probability mass. On the contrary, the first approach (plotting the density of x in a log scale) breaks this proportionality relationship.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"A similar reasoning applies to histograms:","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics\ndf = (x = exp.(rand(1000)),)\nhist1 = data(df) * mapping(:x) * histogram()\ndraw(hist1, axis=(width=225, height=225, xscale=log))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"df = (x = exp.(rand(1000)),)\nhist2 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\ndraw(hist2, axis=(width=225, height=225))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The data transformation approach is preferable as it produces uniform bins, which are easier to interpret.","category":"page"},{"location":"FAQs/#How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?","page":"Frequently Asked Questions","title":"How to combine AlgebraOfGraphics with plain Makie plots?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Since AlgebraOfGraphics is built upon the Makie ecosystem we can easily combine plots from both packages. Two approaches can be taken. Firstly, by using draw! you can pass a Figure or FigurePosition created by Makie to be used by AlgebraOfGraphics, e.g.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics, CairoMakie #hide\n\nf, a, p = lines(0..2pi, sin; figure = (size = (600, 400),))\n\ndf = (x = exp.(rand(1000)),)\nhist1 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\ndraw!(f[1, 2], hist1)\n\nf #hide","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Alternatively, we can create the AlgebraOfGraphics figure first and then add in additional plain Makie axes alongside the result by accessing the .figure field of fg, e.g.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics, CairoMakie #hide\n\ndf = (x = exp.(rand(1000)),)\nhist2 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\nfg = draw(hist2; figure = (size = (600, 400),))\n\nlines(fg.figure[1, 2], 0..2pi, cos)\n\nfg #hide","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"note: Note\nWhen setting the width and height dimensions of each axis manually you will need to call resize_to_layout!(fg) before displaying the figure such that each axis is sized correctly.","category":"page"},{"location":"philosophy/#Philosophy","page":"Philosophy","title":"Philosophy","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"AlgebraOfGraphics aims to be a declarative, question-driven language for data visualizations. This section describes its main guiding principles.","category":"page"},{"location":"philosophy/#From-question-to-plot","page":"Philosophy","title":"From question to plot","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"When analyzing a dataset, we often think in abstract, declarative terms. We have questions concerning our data, which can be answered by appropriate visualizations. For instance, we could ask whether a discrete variable :x affects the distribution of a continuous variable :y. We would then like to generate a visualization that answers this question.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In imperative programming, this would be implemented via the following steps.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Pick the dataset.\nDivide the dataset into subgroups according to the values of :x.\nCompute the density of :y on each subgroup.\nChoose a plot attribute to distinguish subgroups, for instance color.\nSelect as many distinguishable colors as there are unique values of :x.\nPlot all the density curves on top of each other.\nCreate a legend, describing how unique values of :x are associated to colors.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"While the above procedure is certainly feasible, it can introduce a cognitive overhead, especially when more variables and attributes are involved.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In a declarative framework, the user needs to express the question, and the library will take care of creating the visualization. Let us solve the above problem in a toy dataset.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\nset_aog_theme!()\nN = 1000\nx = rand([\"Class 1\", \"Class 2\", \"Class 3\", \"Class 4\"], N)\ny = @. (x == \"Class 1\") * randn() + (x == \"Class 2\") - (x == \"Class 3\") + randn()\nz = @. (x == \"Class 2\") * randn() + (x == \"Class 2\") + (x == \"Class 3\") + y + randn()\ndf = (; x, y, z)","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"plt = data(df) # declare the dataset\nplt *= density() # declare the analysis\nplt *= mapping(:y) # declare the arguments of the analysis\nplt *= mapping(color = :x) # declare the grouping and the respective visual attribute\ndraw(plt) # draw the visualization and its legend","category":"page"},{"location":"philosophy/#No-mind-reading","page":"Philosophy","title":"No mind reading","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Plotting packages requires the user to specify a large amount of settings. The temptation is then to engineer a plotting library in such a way that it would guess what the user actually wanted. AlgebraOfGraphics follows a different approach, based on algebraic manipulations of plot descriptors.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"The key intuition is that a large fraction of the \"clutter\" in a plot specification comes from repeating the same information over and over. Different layers of the same plot will share some but not all information, and the user should be able to distinguish settings that are private to a layer from those that are shared across layers.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"We achieve this goal using the distributive properties of addition and multiplication. This is best explained by example. Let us assume that we wish to visually inspect whether a discrete variable :x affects the joint distribution of two continuous variables, :y and :z.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"We would like to have two layers, one with the raw data, the other with an analysis (kernel density estimation).","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Naturally, the axes should represent the same variables (:y and :z) for both layers. Only the density layer should be a contour plot, whereas only the scatter layer should have some transparency and be grouped (according to :x) in different subplots.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"plt = data(df) *\n (\n visual(Scatter, alpha = 0.3) * mapping(layout = :x) +\n density() * visual(Contour, colormap = Reverse(:grays))\n ) *\n mapping(:y, :z)\ndraw(plt)","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In this case, thanks to the distributive property, it is clear that the dataset and the positional arguments :y, :z are shared across layers, the transparency and the grouping are specific to the data layer, whereas the density analysis, the Contour visualization, and the choice of color map are specific to the analysis layer.","category":"page"},{"location":"philosophy/#User-defined-building-blocks","page":"Philosophy","title":"User-defined building blocks","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"It is common in data analysis tasks to \"pipe\" a sequence of operations. This became very popular in the data science field with the %>% operator in the R language, and it can allow users to seamlessly compose a sequence of tasks:","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"df %>%\n filter(Weight < 3) %>%\n group_by(Species) %>%\n summarise(avg_height = mean(Height))","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Naturally, the alternative would be to create a statement per operation and to assign each intermediate result to its own variable.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"AlgebraOfGraphics is markedly in favor of the latter approach. It is recommended that commonly used building blocks are stored in variables with meaningful names. If we often make a scatter plot with some transparency, we can create a variable transparent_scatter = visual(Scatter, alpha = 0.5) and use it consistently. If some columns of our dataset are always analyzed together, with a similar set of transformations, we can store that information as variables = mapping(variable1 => f1 => label1, variable2 => f2 => label2).","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Working over one or more datasets, the user would then create a library of building blocks to be combined with each other with * and +. These two operators allow for a much larger number of possible combinations than just sequential composition, thus fully justifying the extra characters used to name intermediate entities.","category":"page"},{"location":"philosophy/#Opinionated-defaults","page":"Philosophy","title":"Opinionated defaults","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"While users should be able to customize every aspect of their plots, it is important to note that this customization can be very time-consuming, and many subtleties can escape the attention of the casual user:","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Is the color palette colorblind-friendly?\nWould the colors be distinguishable in black and white (when printed)?\nIs the color gradient perceptually uniform?\nAre the labels and the ticks legible for readers with low vision?\nAre the spacing and typographic hierarchies respected?","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"To remedy this, AlgebraOfGraphics aims to provide solid, opinionated default settings. In particular, it uses a conservative, colorblind-friendly palette and a perceptually uniform, universally readable color map. It follows IBM guidelines to differentiate titles and labels from tick labels via font weight, while using the same typeface at a readable size.","category":"page"},{"location":"philosophy/#Wide-format-support","page":"Philosophy","title":"Wide format support","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Finally, AlgebraOfGraphics aims to support many different data formats. Different problems require organizing the data in different formats, and AlgebraOfGraphics should support a wide range of options.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"This is achieved in three different ways. First, the Tables interface ensures integration with a large variety of data sources. Second, using the Wide data syntax, users can compare many different columns in the same visualization, without having to first reshape the dataset to a long format. Finally, tabular datasets are not a requirement: users may also work directly with Pre-grouped data, which are not organized as a table, but rather as a collection of (possibly multi-dimensional) arrays.","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/multiple_color_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/#Multiple-color-scales","page":"Multiple color scales","title":"Multiple color scales","text":"","category":"section"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"Continuous and discrete color scales can coexist in the same plot. This should be used sparingly, as it can make the plot harder to interpret.","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"x = range(-π, π, length=100)\ny = sin.(x)\nŷ = y .+ randn.() .* 0.1\nz = cos.(x)\nc = rand([\"a\", \"b\"], 100)\ndf = (; x, y, ŷ, z, c)\nlayers = mapping(:y, color=:z) * visual(Lines) + mapping(:ŷ => \"y\", color=:c)\nplt = data(df) * mapping(:x) * layers\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"EditURL = \"analyses.jl\"","category":"page"},{"location":"generated/analyses/#Analyses","page":"Analyses","title":"Analyses","text":"","category":"section"},{"location":"generated/analyses/#Histogram","page":"Analyses","title":"Histogram","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"histogram","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.histogram","page":"Analyses","title":"AlgebraOfGraphics.histogram","text":"histogram(; bins=automatic, datalimits=automatic, closed=:left, normalization=:none)\n\nCompute a histogram.\n\nThe attribute bins can be an Integer, an AbstractVector (in particular, a range), or a Tuple of either integers or abstract vectors (useful for 2- or 3-dimensional histograms). When bins is an Integer, it denotes the approximate number of equal-width intervals used to compute the histogram. In that case, the range covered by the intervals is defined by datalimits (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. When bins is an AbstractVector, it denotes the intervals directly.\n\nclosed determines whether the the intervals are closed to the left or to the right.\n\nThe histogram can be normalized by setting normalization. Possible values are:\n\n:pdf: Normalize by sum of weights and bin sizes. Resulting histogram has norm 1 and represents a PDF.\n:density: Normalize by bin sizes only. Resulting histogram represents count density of input and does not have norm 1.\n:probability: Normalize by sum of weights only. Resulting histogram represents the fraction of probability mass for each bin and does not have norm 1.\n:none: Do not normalize.\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\nnote: Note\nNormalizations are computed withing groups. For example, in the case of normalization=:pdf, sum of weights within each group will be equal to 1.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\ndf = (x=randn(5000), y=randn(5000), z=rand([\"a\", \"b\", \"c\"], 5000))\nspecs = data(df) * mapping(:x, layout=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, dodge=:z, color=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, stack=:z, color=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) *\n mapping((:x, :z) => ((x, z) -> x + 5 * (z == \"b\")) => \"new x\", col=:z) *\n histogram(datalimits=extrema, bins=20)\ndraw(specs, facet=(linkxaxes=:minimal,))","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"data(df) * mapping(:x, :y, layout=:z) * histogram(bins=15) |> draw","category":"page"},{"location":"generated/analyses/#Density","page":"Analyses","title":"Density","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"AlgebraOfGraphics.density","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.density","page":"Analyses","title":"AlgebraOfGraphics.density","text":"density(; datalimits=automatic, kernel=automatic, bandwidth=automatic, npoints=200)\n\nFit a kernel density estimation of data.\n\nHere, datalimits specifies the range for which the density should be calculated (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. The keyword arguments kernel and bandwidth are forwarded to KernelDensity.kde. npoints is the number of points used by Makie to draw the line\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=randn(5000), y=randn(5000), z=rand([\"a\", \"b\", \"c\", \"d\"], 5000))\nspecs = data(df) * mapping(:x, layout=:z) * AlgebraOfGraphics.density(datalimits=((-2.5, 2.5),))\n\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) *\n mapping((:x, :z) => ((x, z) -> x + 5 * (z ∈ [\"b\", \"d\"])) => \"new x\", layout=:z) *\n AlgebraOfGraphics.density(datalimits=extrema)\ndraw(specs, facet=(linkxaxes=:minimal,))","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"data(df) * mapping(:x, :y, layout=:z) * AlgebraOfGraphics.density(npoints=50) |> draw","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, layout=:z) *\n AlgebraOfGraphics.density(npoints=50) * visual(Surface)\n\ndraw(specs, axis=(type=Axis3, zticks=0:0.1:0.2, limits=(nothing, nothing, (0, 0.2))))","category":"page"},{"location":"generated/analyses/#Frequency","page":"Analyses","title":"Frequency","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"frequency","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.frequency","page":"Analyses","title":"AlgebraOfGraphics.frequency","text":"frequency()\n\nCompute a frequency table of the arguments.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand([\"a\", \"b\", \"c\"], 100), z=rand([\"a\", \"b\", \"c\"], 100))\nspecs = data(df) * mapping(:x, layout=:z) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, layout=:z, color=:y, stack=:y) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, layout=:z) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Expectation","page":"Analyses","title":"Expectation","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"expectation","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.expectation","page":"Analyses","title":"AlgebraOfGraphics.expectation","text":"expectation()\n\nCompute the expected value of the last argument conditioned on the preceding ones.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand([\"a\", \"b\", \"c\"], 100), z=rand(100), c=rand([\"a\", \"b\", \"c\"], 100))\nspecs = data(df) * mapping(:x, :z, layout=:c) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :z, layout=:c, color=:y, dodge=:y) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, :z, layout=:c) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Linear","page":"Analyses","title":"Linear","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"linear","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.linear","page":"Analyses","title":"AlgebraOfGraphics.linear","text":"linear(; interval=automatic, level=0.95, dropcollinear=false, npoints=200)\n\nCompute a linear fit of y ~ 1 + x. An optional named mapping weights determines the weights. Use interval to specify what type of interval the shaded band should represent, for a given coverage level (the default 0.95 equates alpha = 0.05). Valid values of interval are :confidence, to delimit the uncertainty of the predicted relationship, and :prediction, to delimit estimated bounds for new data points. Use interval = nothing to only compute the line fit, without any uncertainty estimate. By default, this analysis errors on singular (collinear) data. To avoid that, it is possible to set dropcollinear=true. npoints is the number of points used by Makie to draw the shaded band.\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = 1:0.05:10\na = rand(1:7, length(x))\ny = 1.2 .* x .+ a .+ 0.5 .* randn.()\ndf = (; x, y, a)\nspecs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (linear() + visual(Scatter))\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Smoothing","page":"Analyses","title":"Smoothing","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"smooth","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.smooth","page":"Analyses","title":"AlgebraOfGraphics.smooth","text":"smooth(; span=0.75, degree=2, npoints=200)\n\nFit a loess model. span is the degree of smoothing, typically in [0,1]. Smaller values result in smaller local context in fitting. degree is the polynomial degree used in the loess model. npoints is the number of points used by Makie to draw the line\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = 1:0.05:10\na = rand(1:7, length(x))\ny = sin.(x) .+ a .+ 0.1 .* randn.()\ndf = (; x, y, a)\nspecs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (smooth() + visual(Scatter))\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Contours","page":"Analyses","title":"Contours","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"contours","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.contours","page":"Analyses","title":"AlgebraOfGraphics.contours","text":"contours(; levels=5, kwargs...)\n\nCreate contour lines over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. \n\nYou can pass the number of levels as an integer or a vector of levels. The levels are calculated across the whole z data if they are specified as an integer.\n\nNote that visual(Contour) only works in a limited way with AlgebraOfGraphics since version 0.7, because the internal calculations it does are not compatible with the scale system. With visual(Contour), you can only have categorically-colored contours (for example to visualize contours of multiple categories). Alternatively, if you set the colormap attribute, you can get continuously-colored contours but the levels will not be known to AlgebraOfGraphics, so they won't be synchronized across facets and there will not be a colorbar.\n\nAll other keyword arguments are forwarded as attributes to the underlying Contour plot.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * contours(levels = 8)\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * contours(levels = 8, labels = true)\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Filled-Contours","page":"Analyses","title":"Filled Contours","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"filled_contours","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.filled_contours","page":"Analyses","title":"AlgebraOfGraphics.filled_contours","text":"filled_contours(; bands=automatic, levels=automatic)\n\nCreate filled contours over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. \n\nYou can pass either the number of bands to bands or pass a vector of levels (the boundaries of the bands) to levels, but not both. The number of bands when levels is passed is length(levels) - 1. The levels are calculated across the whole z data if the number of bands is specified. If neither levels nor bands are specified, the default is bands = 10.\n\nNote that visual(Contourf) does not work with AlgebraOfGraphics since version 0.7, because the internal binning it does is not compatible with the scale system.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * filled_contours(levels = 3:2:15)\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/pre_grouped_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/#Pre-grouped-data","page":"Pre-grouped data","title":"Pre-grouped data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"using AlgebraOfGraphics, CairoMakie\n\n\nx = [rand(10) .+ i for i in 1:3]\ny = [rand(10) .+ i for i in 1:3]\nz = [rand(10) .+ i for i in 1:3]\nc = [\"a\", \"b\", \"c\"]\n\nm = pregrouped(x, y, color=c => (t -> \"Type \" * t ) => \"Category\")\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, (y, z) => (+) => \"sum\", color=c => (t -> \"Type \" * t ) => \"Category\")\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, [y z], color=dims(1) => renamer([\"a\", \"b\", \"c\"])) * visual(Scatter)\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, [y z], color=[\"1\" \"2\"])\nlayers = visual(Scatter) + linear()\nfg = draw(m * layers)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/legend.jl\"","category":"page"},{"location":"gallery/gallery/customization/legend/#Legend-tweaking","page":"Legend tweaking","title":"Legend tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: Source code)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"To tweak the position and appearance of the legend, simply use the legend keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"labels = [\"a looooooong label\", \"an even loooooonger label\", \"and one more long label\"]\ndf = (x=rand(100), y=rand(100), group=rand(labels, 100))\nlayers = linear() + mapping(color=:group)\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"fg = draw(plt, legend=(position=:top, titleposition=:left, framevisible=true, padding=5))","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"To adjust the title and order of labels in a legend you can use the pair syntax.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"layers = linear() + mapping(color=:group => sorter(labels) => \"Labels\")\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"Adding a plot to a pre-existing figure with draw! will not draw the legend automatically. In this case, one must use legend! and specify the axis to which it should be added.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"The tellheight = false, tellwidth = false arguments are useful to avoid changing the dimensions of the axis.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"makie_fig = Figure()\nax_scatter = Axis(makie_fig[1, 1])\n\ngrid = draw!(ax_scatter, plt)\n\nlegend!(makie_fig[1, 1], grid; tellheight=false, tellwidth=false, halign=:right, valign=:top)\n\nmakie_fig","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"EditURL = \"visual.jl\"","category":"page"},{"location":"generated/visual/#Visual","page":"Visual","title":"Visual","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"visual","category":"page"},{"location":"generated/visual/#AlgebraOfGraphics.visual","page":"Visual","title":"AlgebraOfGraphics.visual","text":"visual(plottype; attributes...)\n\nCreate a Layer that will cause a plot spec multiplied with it to be visualized with plot type plottype, together with optional attributes.\n\nThe available plotting functions are documented here. Refer to plotting functions using upper CamelCase for visual's first argument (e.g. visual(Scatter), visual(BarPlot)). See the documentation of each plotting function to discover the available attributes. These attributes can be passed as additional keyword arguments to visual, or as part of the mapping you define.\n\nThe visual function can in principle be used for any plotting function that is defined using the @recipe macro from Makie. AlgebraOfGraphics just needs method definitions for aesthetic_mapping, which define what arguments of the plotting function map to which visual aesthetics. And for legend support, legend_elements must be overloaded for custom recipes as well, as Makie's default legend mechanism relies on instantiated plot objects, while AlgebraOfGraphics must go by the type and attributes alone.\n\nDepending on its aesthetic_mapping, a plot type and its attributes may change certain semantics of a given data(...) * mapping(...) spec. For example, visual(BarPlot) will show mapping 1 on the x axis and 2 on the y axis, while visual(BarPlot, direction = :x) shows mapping 1 on y and 2 on x.\n\n\n\n\n\n","category":"function"},{"location":"generated/visual/#Examples","page":"Visual","title":"Examples","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\ndf = (x=randn(1000), y=randn(1000))\nplt = data(df) * mapping(:x, :y) * AlgebraOfGraphics.density(npoints=50)\ndraw(plt * visual(Heatmap)) # plot as heatmap (the default)","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"From AlgebraOfGraphics version 0.7 on, some attributes of the underlying Makie functions will not have an effect if they are controlled by scales instead. For example, continuous colors are completely controlled by color scales, so setting colormap in visual does not have an effect.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"Set the colormap in the Scale options instead.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt, scales(Color = (; colormap = :viridis))) # set a different colormap","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt * visual(Contour)) # plot as contour","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt * visual(Contour, linewidth=2)) # plot as contour with thicker lines","category":"page"},{"location":"generated/visual/#Manual-legend-entries-via-label","page":"Visual","title":"Manual legend entries via label","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"The legend normally contains entries for all appropriate scales used in the plot. Sometimes, however, you just want to label certain plots such that they appear in the legend without using any scale. You can achieve this by adding the label keyword to all visuals that you want to label. Layers with the same label will be combined within a legend entry.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"x = range(0, 4pi, length = 40)\nlayer1 = data((; x = x, y = cos.(x))) * mapping(:x, :y) * visual(Lines, linestyle = :dash, label = \"A cosine line\")\nlayer2 = data((; x = x, y = sin.(x) .+ 2)) * mapping(:x, :y) *\n (visual(Lines, color = (:tomato, 0.4)) + visual(Scatter, color = :tomato)) * visual(label = \"A sine line + scatter\")\ndraw(layer1 + layer2)","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"If the figure contains other scales, the legend will list the labelled group last by default. If you want to reorder, use the symbol :Label to specify the labelled group.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"df = (; x = repeat(1:10, 3), y = cos.(1:30), group = repeat([\"A\", \"B\", \"C\"], inner = 10))\nspec1 = data(df) * mapping(:x, :y, color = :group) * visual(Lines)\n\nspec2 = data((; x = 1:10, y = cos.(1:10) .+ 2)) * mapping(:x, :y) * visual(Scatter, color = :purple, label = \"Scatter\")\n\nf = Figure()\nfg = draw!(f[1, 1], spec1 + spec2)\nlegend!(f[1, 2], fg)\nlegend!(f[1, 3], fg, order = [:Label, :Color])\n\nf","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/axis.jl\"","category":"page"},{"location":"gallery/gallery/customization/axis/#Axis-tweaking","page":"Axis tweaking","title":"Axis tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"To tweak one or more axes, simply use the axis keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100))\nlayers = linear() + mapping(color=:z)\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt, axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"fg = draw(plt, axis=(aspect=1, xticks=0:0.1:1, yticks=0:0.1:1, ylabel=\"custom label\"))","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/secondary_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/#Secondary-scales","page":"Secondary scales","title":"Secondary scales","text":"","category":"section"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"By default, scales with the same aesthetic type have their categories merged. This can be undesirable if there are disjoint sets of categories, for example three different time series with two different event markers:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"nevents = 500\nngroups = 3\ntime = repeat(1:nevents, ngroups)\ny = reduce(vcat, [cumsum(randn(length(time))) for _ in 1:ngroups])\ngroup = repeat([\"A\", \"B\", \"C\"], inner = nevents)\ndf1 = (; time, y, group)\ndf2 = (; time = [30, 79, 250, 400], event = [\"X\", \"Y\", \"Y\", \"X\"])\n\nspec_a = data(df1) * mapping(:time, :y, color = :group) * visual(Lines)\nspec_b = data(df2) * mapping(:time, color = :event) * visual(VLines)\n\ndraw(spec_a + spec_b)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"By assigning an arbitrary scale identifier to one of the color mappings, we can split the two scales apart and receive a separate legend for both:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"split_spec = spec_a + spec_b * mapping(color = :event => scale(:secondary))\ndraw(split_spec)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"Each scale can then be modified separately in the scales configuration. For example, it is not desired that both scales use the same color palette:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"fg = draw(split_spec, scales(secondary = (;\n palette = [:gray70, :gray30]\n)))","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"If you don't want to have separate legend groups, you can merge them using the order keyword in the legend config.","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"draw(\n split_spec,\n scales(\n secondary = (; palette = [:gray70, :gray30])\n );\n legend = (; order = [[:Color, :secondary] => \"Legend\"])\n)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/figure.jl\"","category":"page"},{"location":"gallery/gallery/customization/figure/#Figure-tweaking","page":"Figure tweaking","title":"Figure tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"You can pass attributes to the underlying Makie.Figure using the figure keyword of draw.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100), c=rand([\"a\", \"b\"], 100))\nxyc = data(df) * mapping(:x, :y, layout=:c)\nlayers = linear() + mapping(color=:z)\nplt = xyc * layers\ndraw(\n plt,\n figure = (;\n figure_padding = 10,\n backgroundcolor = :gray80,\n size = (800, 400)\n )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"You can also add a figure title, subtitle and footnotes.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"fg = draw(\n plt,\n figure = (;\n figure_padding = 10,\n backgroundcolor = :gray80,\n size = (800, 400),\n title = \"Figure title\",\n subtitle = \"Some subtitle below the figure title\",\n footnotes = [\n rich(superscript(\"1\"), \"First footnote\"),\n rich(superscript(\"2\"), \"Second footnote\"),\n ]\n )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"The appearance of these elements can be modified further, for all options check the draw function.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"draw(\n plt,\n figure = (;\n figure_padding = 10,\n backgroundcolor = :gray80,\n size = (800, 400),\n title = \"Figure title\",\n subtitle = \"Some subtitle below the figure title\",\n footnotes = [\n rich(superscript(\"1\"), \"First footnote\"),\n rich(superscript(\"2\"), \"Second footnote\"),\n ],\n titlecolor = :firebrick,\n titlealign = :right,\n footnotefont = :bold,\n )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/mapping/#Mapping","page":"Mapping","title":"Mapping","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Mappings determine how the data is translated into a plot. For example, this mapping maps columns weight and height to positional arguments 1 and 2, and age to the markersize attribute of the Scatter plotting function:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"mapping(:weight, :height, markersize = :age)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"mapping","category":"page"},{"location":"layers/mapping/#AlgebraOfGraphics.mapping","page":"Mapping","title":"AlgebraOfGraphics.mapping","text":"mapping(positional...; named...)\n\nCreate a Layer with positional and named selectors. These selectors will be translated into input data for the Makie plotting function or AlgebraOfGraphics analysis that is chosen to visualize the Layer.\n\nA Layer created with mapping does not have a data source by default, you can add one by multiplying with the output of the data function.\n\nThe positional and named selectors of mapping are converted to actual input data for the plotting function that will be selected via visual. The translation from selector to data differs according to the data source.\n\nTabular data\n\nWhen a mapping is combined with a data(tabular) where tabular is some Tables.jl-compatible object, each argument will be interpreted as a column selector. Additionally, it's allowed to specify columns outside of the dataset directly by wrapping the values in direct. The values can either be vectors that have to match the number of rows from the tabular data, or scalars that will be expanded as if they were a column filled with the same value.\n\nmapping(\n :x, # column named \"x\"\n \"a column\"; # column named \"a column\"\n color = 1, # first column\n marker = direct(\"abc\"), # a new column filled with the string \"abc\"\n linestyle = direct(1:3), # a new column, length must match the table\n)\n\nnothing\n\nIf no data is set, each entry of mapping should be an AbstractVector that specifies a column of data directly. Scalars like strings for example will be expanded as if they were a column filled with the same value. This is useful when a legend should be shown, but there's only one group.\n\nmapping(\n 1:3, # a column with values 1 to 3\n [4, 5, 6], # a column with values 4 to 6 \n color = \"group 1\", # a column with repeated value \"group 1\" \n)\n\nPregrouped\n\nWith data(Pregrouped()) * mapping(...) or the shortcut pregrouped(...), each element in mapping specifies input data directly, like with nothing. However, in this mode, data should be passed in pregrouped. Categorical variables should come as a vector of categories, while numerical variables should come as a vector of vectors of values, with as many inner vectors as there are groups in the categorical variables.\n\npregrouped(\n [[1, 2, 3], [4, 5]], # two grouped vectors, of length 3 and 2\n color = [\"A\", \"B\"] # a vector with two categorical group values\n)\n\n\n\n\n\n","category":"function"},{"location":"layers/mapping/#Aesthetics","page":"Mapping","title":"Aesthetics","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The structure of a mapping is always directly tied to the signature of the plotting function (or analysis) that it is being connected with. What visual aspects of the plot the positional or keyword arguments affect depends on the plotting function in use.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"To be used with AlgebraOfGraphics, a plotting function has to add a declaration which aesthetics (like X, Y, Color, MarkerSize, LineStyle) its arguments map to. This mechanism allows AlgebraOfGraphics to correctly convert the raw input data into visual attributes for each plotting function and to correctly create and label axes, colorbars and legends.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Aesthetics can also change depending on attributes passed to visual.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"For example, for a BarPlot, args 1 and 2 correspond to the X and Y aesthetics by default. But if you change the direction in the visual, then axis labels shift accordingly because the aesthetic mapping has changed to 1 = Y, 2 = X:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])\nm = mapping(:name, :height_meters)\nspec1 = data(df) * m * visual(BarPlot)\nspec2 = data(df) * m * visual(BarPlot, direction = :x)\n\nf = Figure()\ndraw!(f[1, 1], spec1)\ndraw!(f[1, 2], spec2)\nf","category":"page"},{"location":"layers/mapping/#Hardcoded-aesthetics","page":"Mapping","title":"Hardcoded aesthetics","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Most aesthetics are tied to specific attributes of plot types, for example like AesColor to strokecolor of Scatter. There are a few aesthetics, however, which are hardcoded to belong to certain mapping keywords independent of the plot type in use.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"These are layout, row and col for facetting, group for creating a separate plot for each group (like separate lines instead of one long line) and dodge_x and dodge_y for dodging.","category":"page"},{"location":"layers/mapping/#Dodging","page":"Mapping","title":"Dodging","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Dodging refers to the shifting of plots on a (usually categorical) scale depending on the group they belong to. It is used to avoid overlaps. Some plot types, like BarPlot, have their own dodge keyword because their dodging logic additionally needs to transform the visual elements (for example, dodging a bar plot makes thinner bars). For all other plot types, you can use the generic dodge_x and dodge_y keywords.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"They work by shifting each categorical group by some value that depends on the chosen \"dodge width\". The dodge width refers to the width that all dodged elements in a group add up to at a given point. Some plot types have an inherent width, like barplots. Others have no width, like scatters or errorbars. For those plot types that have no width to use for dodging, you have to specify one manually in scales.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Here's an example of a manual width selection:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (\n x = repeat(1:10, inner = 2),\n y = cos.(range(0, 2pi, length = 20)),\n ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt = data(df) * (\n mapping(:x, :y, dodge_x = :dodge, color = :dodge) * visual(Scatter) +\n mapping(:x, :ylow, :yhigh, dodge_x = :dodge, color = :dodge) * visual(Rangebars)\n)\ndraw!(f[1, 1], plt, scales(DodgeX = (; width = 1)), axis = (; title = \"width = 1\"))\ndraw!(f[1, 2], plt, scales(DodgeX = (; width = 0.75)), axis = (; title = \"width = 0.75\"))\ndraw!(f[2, 1], plt, scales(DodgeX = (; width = 0.5)), axis = (; title = \"width = 0.5\"))\ndraw!(f[2, 2], plt, scales(DodgeX = (; width = 0.25)), axis = (; title = \"width = 0.25\"))\nf","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"A common scenario is plotting errorbars on top of barplots. In this case, AlgebraOfGraphics can detect the inherent dodging width of the barplots and adjust accordingly for the errorbars. Note in this example how choosing a manual dodging width only applies to the errorbars (because the barplot plot type handles this internally) and potentially leads to a misalignment between the different plot elements:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (\n x = repeat(1:10, inner = 2),\n y = cos.(range(0, 2pi, length = 20)),\n ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt = data(df) * (\n mapping(:x, :y, dodge = :dodge, color = :dodge) * visual(BarPlot) +\n mapping(:x, :ylow, :yhigh, dodge_x = :dodge) * visual(Rangebars)\n)\ndraw!(f[1, 1], plt, axis = (; title = \"No width specified, auto-determined by AlgebraOfGraphics\"))\ndraw!(f[2, 1], plt, scales(DodgeX = (; width = 0.25)), axis = (; title = \"Manually specifying width = 0.25 leads to a mismatch\"))\nf","category":"page"},{"location":"layers/mapping/#Pair-syntax","page":"Mapping","title":"Pair syntax","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The Pair operator => can be used for three different purposes within mapping:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"renaming columns\ntransforming columns by row\nmapping data to a custom scale","category":"page"},{"location":"layers/mapping/#Renaming-columns","page":"Mapping","title":"Renaming columns","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndata((; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])) *\n mapping(:name => \"Name\", :height_meters => \"Height (m)\") *\n visual(BarPlot) |> draw ","category":"page"},{"location":"layers/mapping/#Transforming-columns","page":"Mapping","title":"Transforming columns","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"If a Function is paired to the column selector, it is applied by row to the data. Often, you will want to also assign a new name that fits the transformed data, in which case you can use the three-element column => transformation => name syntax:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndata((; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])) *\n mapping(:name => (n -> n[1] * \".\"), :height_meters => (x -> x * 100) => \"Height (cm)\") *\n visual(BarPlot) |> draw","category":"page"},{"location":"layers/mapping/#Row-by-row-versus-whole-column-operations","page":"Mapping","title":"Row-by-row versus whole-column operations","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The pair syntax acts row by row, unlike, e.g., DataFrames.transform. This has several advantages.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Simpler for the user in most cases.\nLess error prone especially\nwith grouped data (should a column operation apply to each group or the whole dataset?)\nwhen several datasets are used","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Naturally, this also incurs some downsides, as whole-column operations, such as z-score standardization, are not supported: they should be done by adding a new column to the underlying dataset beforehand.","category":"page"},{"location":"layers/mapping/#Functions-of-several-arguments","page":"Mapping","title":"Functions of several arguments","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"In the case of functions of several arguments, such as isequal, the input variables must be passed as a Tuple.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"accuracy = (:species, :predicted_species) => isequal => \"accuracy\"","category":"page"},{"location":"layers/mapping/#Helper-functions","page":"Mapping","title":"Helper functions","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Some helper functions are provided, which can be used within the pair syntax to either rename and reorder unique values of a categorical column on the fly or to signal whether a numerical column should be treated as categorical.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The complete API of helper functions is available at Mapping helpers, but here are a few examples:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"# column `train` has two unique values, `true` and `false`\n:train => renamer([true => \"training\", false => \"testing\"]) => \"Dataset\"\n# column `price` has three unique values, `\"low\"`, `\"medium\"`, and `\"high\"`\n:price => sorter([\"low\", \"medium\", \"high\"])\n# column `age` is expressed in integers and we want to treat it as categorical\n:age => nonnumeric\n# column `labels` is expressed in strings and we do not want to treat it as categorical\n:labels => verbatim\n# wrap categorical values to signal that the order from the data source should be respected\n:weight => presorted","category":"page"},{"location":"layers/mapping/#Custom-scales","page":"Mapping","title":"Custom scales","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"All columns mapped to the same aesthetic type are represented using the same scale by default. This is evident if you plot two different datasets with two different plot types.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"In the following example, both Scatter and HLines use the Color aesthetic, Scatter for the strokecolor keyword and HLines for color. A single merged legend is rendered for both, which does not have a title because it derives from two differently named columns.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf_a = (; x = 1:9, y = [1, 2, 3, 5, 6, 7, 9, 10, 11], group = repeat([\"A\", \"B\", \"C\"], inner = 3))\n\nspec1 = data(df_a) * mapping(:x, :y, strokecolor = :group) * visual(Scatter, color = :transparent, strokewidth = 3, markersize = 15)\n\ndf_b = (; y = [4, 8], threshold = [\"first\", \"second\"])\n\nspec2 = data(df_b) * mapping(:y, color = :threshold) * visual(HLines)\n\ndraw(spec1 + spec2)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"If we want to have separate legends for both, we can assign a custom scale identifier to either the strokecolor or the color mapping. The name can be chosen freely, it serves only to disambiguate.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"spec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(spec1 + spec2_custom_scale)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Each scale can be customized further by passing configuration options via scales as the second argument of the draw function. More information on scale options can be found under Scale options.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"As an example, we can pass separate colormaps using the palette keyword:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"spec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(\n spec1 + spec2_custom_scale,\n scales(\n Color = (; palette = [:red, :green, :blue]),\n color2 = (; palette = [:gray30, :gray80]),\n )\n)","category":"page"},{"location":"changelog/#Changelog","page":"Changelog","title":"Changelog","text":"","category":"section"},{"location":"changelog/#Unreleased","page":"Changelog","title":"Unreleased","text":"","category":"section"},{"location":"changelog/#v0.8.10-2024-09-24","page":"Changelog","title":"v0.8.10 -2024-09-24","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed markercolor in ScatterLines legends when it did not match color #567.","category":"page"},{"location":"changelog/#v0.8.9-2024-09-24","page":"Changelog","title":"v0.8.9 - 2024-09-24","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added ability to include layers in the legend without using scales by adding visual(label = \"some label\") #565.","category":"page"},{"location":"changelog/#v0.8.8-2024-09-17","page":"Changelog","title":"v0.8.8 - 2024-09-17","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed aesthetics of errorbar so that x and y stay labelled correctly when using direction = :x #560.\nAdded ability to specify title, subtitle and footnotes plus settings in the draw function #556.\nAdded dodge_x and dodge_y keywords to mapping that allow to dodge any plot types that have AesX or AesY data #558.","category":"page"},{"location":"changelog/#v0.8.7-2024-09-06","page":"Changelog","title":"v0.8.7 - 2024-09-06","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added ability to return ProcessedLayers from transformations, thereby enabling multi-layer transformations, such as scatter plus errorbars #549.\nFixed bug where mergesorted applied on string vectors used isless instead of natural sort #553.","category":"page"},{"location":"changelog/#v0.8.6-2024-09-02","page":"Changelog","title":"v0.8.6 - 2024-09-02","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added bar_labels to BarPlot's aesthetic mapping #544.\nAdded ability to hide legend or colorbar by passing, e.g., legend = (; show = false) to draw #547.","category":"page"},{"location":"changelog/#v0.8.5-2024-08-27","page":"Changelog","title":"v0.8.5 - 2024-08-27","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added presorted helper function to keep categorical data in the order encountered in the source table, instead of sorting it alphabetically #529.\nAdded from_continuous helper function which allows to sample continuous colormaps evenly to use them as categorical palettes without having to specify how many categories there are #541.","category":"page"},{"location":"changelog/#v0.8.4-2024-08-26","page":"Changelog","title":"v0.8.4 - 2024-08-26","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added fillto to BarPlot aesthetics #535.\nFixed bug when giving datalimits of density as a (low, high) tuple #536.\nFixed bug where facet-local continuous scale limits were used instead of the globally merged ones, possibly leading to mismatches between data and legend #539.","category":"page"},{"location":"changelog/#v0.8.3-2024-08-23","page":"Changelog","title":"v0.8.3 - 2024-08-23","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed incorrect x/y axis assignment for the violin plot type #528.","category":"page"},{"location":"changelog/#v0.8.2-2024-08-21","page":"Changelog","title":"v0.8.2 - 2024-08-21","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Enable use of LaTeXStrings and rich text in renamer #525.\nFixed widths of boxplots with color groupings #524.","category":"page"},{"location":"changelog/#v0.8.1-2024-08-20","page":"Changelog","title":"v0.8.1 - 2024-08-20","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added back support for Hist, CrossBar, ECDFPlot and Density #522.","category":"page"},{"location":"changelog/#v0.8.0-2024-07-26","page":"Changelog","title":"v0.8.0 - 2024-07-26","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Columns with element types of Union{Missing,T} are not treated as categorical by default anymore, instead T decides if data is seen as categorical, continuous or geometrical. If you relied on numerical vectors with missings being treated as categorical, you can use :columnname => nonnumeric in the mapping instead.\nBreaking: AbstractString categories are now sorted with natural sort order by default. This means that where you got [\"1\", \"10\", \"2\"] before, you now get [\"1\", \"2\", \"10\"]. You can use sorter, the categories keyword or categorical arrays to sort your data differently if needed.","category":"page"},{"location":"changelog/#v0.7.0-2024-07-16","page":"Changelog","title":"v0.7.0 - 2024-07-16","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: The palette keyword of draw linking palettes to keyword arguments was removed. Instead, palettes need to be passed to specific scales like draw(..., scales(Color = (; palette = :Set1_3)))\nBreaking: All recipes need to have the new function aesthetic_mapping defined for all sets of positional arguments that should be supported, as can be seen in src/aesthetics.jl. This breaks usage of all custom recipes. Additionally, not all Makie plots have been ported to the new system yet. If you encounter missing plots, or missing attributes of already ported plots, please open an issue.\nBreaking: All custom recipes that should be displayed in a legend, need to have legend_elements(P, attributes, scale_args) defined as can be seen in src/guides/legend.jl. AlgebraOfGraphics cannot use the same default mechanism as Makie, which can create a legend from an existing plot, because AlgebraOfGraphics needs to create the legend before the plot is instantiated.\nBreaking: Pregrouped data cannot be passed anymore to the plain mapping(...) without any data(tabular). Instead, you should use pregrouped(...) which is a shortcut for data(Pregrouped()) * mapping(...).\nBreaking: Contour and Contourf generally do not work anymore with visual(). Instead, the contours() and filled_contours() analyses should be used. Contour can still be used with categorical colors, but not with continuous ones.\nBreaking: All colormap properties for continuous color scales need to be passed via scales now, and not through visual. This is to have central control over the scale as it can be used by multiple visuals simultaneously.\nHorizontal barplots, violins, errorbars, rangebars and other plot types that have two different orientations work correctly now. Axis labels switch accordingly when the orientation is changed.\nPlotting functions whose positional arguments don't correspond to X, Y, Z work correctly now. For example, HLines (1 => Y) or rangebars (1 => X, 2 => Y, 3 => Y).\nIt is possible to add categories beyond those present in the data with the categories keyword within a scale's settings. It is also possible to reorder or otherwise transform the existing categories by passing a function to categories.\nThe supported attributes are not limited anymore to a specific set of names, for example, strokecolor can work the same as color did before, and the two can share a scale via their shared aesthetic type.\nThere can be multiple scales of the same aesthetic now. This allows to have separate legends for different plot types using the same aesthetics. Scale separation works by pairing a variable in mapping with a scale(id_symbol).\nLegend entries can be reordered using the legend = (; order = ...) option in draw. Specific scales can opt out of the legend by passing legend = false in scales.\nLabels can now be anything that Makie supports, primarily Strings, LaTeXStrings or rich text.\nLegend elements now usually reflect all attributes set in their corresponding visual.\nSimple column vectors of data can now be passed directly to mapping without using data first. Additionally, scalar values are accepted as a shortcut for columns with the same repeated value.\nColumns from outside a table source in data can now be passed to mapping by wrapping them in the direct function. Scalar values are accepted as a shortcut for columns with the same repeated value. For example, to create a label for columns x and y from a dataframe passed to data, one could now do mapping(:x, :y, color = direct(\"label\")) without having to create a column full of \"label\" strings first.\nThe numbers at which categorical values are plotted on x and y axis can now be changed via scales(X = (; palette = [1, 2, 4])) or similar.\nContinuous marker size scales can now be shown in the legend. Numerical values are proportional to area and not diameter now, which makes more sense with respect to human perception. The min and max marker size can be set using the sizerange property for the respective scale in scales.","category":"page"},{"location":"changelog/#v0.6.11-2022-08-08","page":"Changelog","title":"v0.6.11 - 2022-08-08","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added paginate for pagination of large facet plots. ","category":"page"},{"location":"changelog/#v0.6.8-2022-06-14","page":"Changelog","title":"v0.6.8 - 2022-06-14","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added choropleth recipe to supersede geodata for geographical data.","category":"page"},{"location":"changelog/#v0.6.1-2022-01-28","page":"Changelog","title":"v0.6.1 - 2022-01-28","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Support level in linear analysis for confidence interval.\nReplaced tuples and named tuples in Layer and Entry with dictionaries from Dictionaries.jl.\nSplit internal Entry type into ProcessedLayer (to be used for analyses) and Entry (to be used for plotting).","category":"page"},{"location":"changelog/#v0.6.0-2021-10-24","page":"Changelog","title":"v0.6.0 - 2021-10-24","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Default axis linking behavior has changed: now only axes corresponding to the same variable are linked. For consistency with row/col, layout will hide decorations of linked axes and span axis labels if appropriate.\nCustomizable legend and colorbar position and look.\nCustomizable axis linking behavior.","category":"page"},{"location":"changelog/#v0.5-2021-08-05","page":"Changelog","title":"v0.5 - 2021-08-05","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Axis(ae) has been replaced by ae.axis.\nBreaking: Legend(fg) has been replaced by legend!(fg) and colorbar!(fg).\nlegend! and colorbar! API allows for custom legend placement.","category":"page"},{"location":"changelog/#v0.4-2021-05-21","page":"Changelog","title":"v0.4 - 2021-05-21","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Removed deprecations for style and spec (now only mapping and visual are allowed).\nBreaking: Analyses now require parentheses (i.e. linear() instead of linear).\nBreaking: Rename layout_x and layout_y to col and row.\nBreaking: Rename wts keyword argument to weights.\nBreaking: categorical has been replaced by nonnumeric.","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/density_plots.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/#Density-plots","page":"Density plots","title":"Density plots","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"df = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5) * visual(orientation=:vertical)\n\"Not yet supported\" # hide","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"\"Not yet supported\"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"API/types/#Types","page":"Types","title":"Types","text":"","category":"section"},{"location":"API/types/","page":"Types","title":"Types","text":"AlgebraOfGraphics.AbstractDrawable\nAlgebraOfGraphics.AbstractAlgebraic\nAlgebraOfGraphics.Layer\nAlgebraOfGraphics.Layers\nAlgebraOfGraphics.ProcessedLayer\nAlgebraOfGraphics.ProcessedLayers\nAlgebraOfGraphics.Entry\nAlgebraOfGraphics.AxisEntries","category":"page"},{"location":"API/types/#AlgebraOfGraphics.AbstractDrawable","page":"Types","title":"AlgebraOfGraphics.AbstractDrawable","text":"AbstractDrawable\n\nAbstract type encoding objects that can be drawn via AlgebraOfGraphics.draw.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.AbstractAlgebraic","page":"Types","title":"AlgebraOfGraphics.AbstractAlgebraic","text":"AbstractAlgebraic <: AbstractDrawable\n\nAbstract type encoding objects that can be combined together using + and *.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Layer","page":"Types","title":"AlgebraOfGraphics.Layer","text":"Layer(transformation, data, positional::AbstractVector, named::AbstractDictionary)\n\nAlgebraic object encoding a single layer of a visualization. It is composed of a dataset, positional and named arguments, as well as a transformation to be applied to those. Layer objects can be multiplied, yielding a novel Layer object, or added, yielding a AlgebraOfGraphics.Layers object.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Layers","page":"Types","title":"AlgebraOfGraphics.Layers","text":"Layers(layers::Vector{Layer})\n\nAlgebraic object encoding a list of AlgebraOfGraphics.Layer objects. Layers objects can be added or multiplied, yielding a novel Layers object.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.ProcessedLayer","page":"Types","title":"AlgebraOfGraphics.ProcessedLayer","text":"ProcessedLayer(l::Layer)\n\nProcess a Layer and return the resulting ProcessedLayer.\n\nNote that this method should not be used anymore as processing a Layer can now potentially return multiple ProcessedLayer objects. Therefore, you should use the plural form ProcessedLayers(layer).\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.ProcessedLayers","page":"Types","title":"AlgebraOfGraphics.ProcessedLayers","text":"ProcessedLayers(layers::Vector{ProcessedLayer})\n\nObject encoding a list of AlgebraOfGraphics.ProcessedLayer objects. ProcessedLayers objects are the output of the processing pipeline and can be drawn without further processing.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Entry","page":"Types","title":"AlgebraOfGraphics.Entry","text":"Entry(plottype::PlotType, positional::Arguments, named::NamedArguments)\n\nDefine plottype as well as positional and named arguments for a single plot.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.AxisEntries","page":"Types","title":"AlgebraOfGraphics.AxisEntries","text":"AxisEntries(axis::Union{Axis, Nothing}, entries::Vector{Entry}, categoricalscales, continuousscales)\n\nDefine all ingredients to make plots on an axis. Each categorical scale should be a CategoricalScale, and each continuous scale should be a ContinuousScale.\n\n\n\n\n\n","category":"type"},{"location":"API/recipes/#Recipes","page":"Recipes","title":"Recipes","text":"","category":"section"},{"location":"API/recipes/","page":"Recipes","title":"Recipes","text":"choropleth\nlinesfill","category":"page"},{"location":"API/recipes/#AlgebraOfGraphics.choropleth","page":"Recipes","title":"AlgebraOfGraphics.choropleth","text":"choropleth(geometries; transformation, attributes...)\n\nChoropleth map, where regions are defined by geometries. Use transformation to transform coordinates (see Proj.jl for more information).\n\nwarning: Warning\nThe transformation keyword argument is experimental and could be deprecated (even in a non-breaking release) in favor of a different syntax.\n\nAttributes\n\nAvailable attributes and their defaults for Plot{AlgebraOfGraphics.choropleth} are: \n\n alpha 1.0\n clip_planes MakieCore.Automatic()\n color :gray25\n colormap :batlow\n colorrange MakieCore.Automatic()\n colorscale identity\n cycle [:color => :patchcolor]\n depth_shift 0.0f0\n highclip MakieCore.Automatic()\n inspectable true\n inspector_clear MakieCore.Automatic()\n inspector_hover MakieCore.Automatic()\n inspector_label MakieCore.Automatic()\n joinstyle :miter\n linecap :butt\n linestyle \"nothing\"\n lowclip MakieCore.Automatic()\n miter_limit 1.0471975511965976\n nan_color :transparent\n overdraw false\n shading NoShading\n space :data\n ssao false\n stroke_depth_shift -1.0f-5\n strokecolor :black\n strokecolormap :batlow\n strokewidth 0\n transparency false\n visible true\n\n\n\n\n\n","category":"function"},{"location":"API/recipes/#AlgebraOfGraphics.linesfill","page":"Recipes","title":"AlgebraOfGraphics.linesfill","text":"linesfill(xs, ys; lower, upper, attributes...)\n\nLine plot with a shaded area between lower and upper. If lower and upper are not given, shaded area is between 0 and ys.\n\nAttributes\n\nAvailable attributes and their defaults for Plot{AlgebraOfGraphics.linesfill} are: \n\n color :gray25\n colormap :batlow\n colorrange MakieCore.Automatic()\n fillalpha 0.15\n linestyle \"nothing\"\n linewidth 1.5\n lower MakieCore.Automatic()\n upper MakieCore.Automatic()\n\n\n\n\n\n","category":"function"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/no_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/#Using-mapping-without-tabular-data","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"Sometimes it's easier to specify columnar data directly within mapping rather than first storing it in some tabular data source and accessing it by column name. Note that you can also use scalar values which will be treated like columns with repeated elements. In the example below, we specify color = \"marker\" instead of the more verbose color = fill(\"marker\", 3).","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"using AlgebraOfGraphics, CairoMakie\n\nx = 1:100\ny = sin.(range(0, 2pi, length = 100))\n\nplt = mapping(x, y, color = repeat([\"high\", \"low\"], inner = 50)) *\n visual(Lines) +\n mapping([20, 28, 51], color = \"marker\" => scale(:secondary)) *\n visual(VLines, linestyle = :dash)\n\nfg = draw(plt, scales(secondary = (; palette = [:gray80])))","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/data/#Data","page":"Data","title":"Data","text":"","category":"section"},{"location":"layers/data/","page":"Data","title":"Data","text":"data","category":"page"},{"location":"layers/data/#AlgebraOfGraphics.data","page":"Data","title":"AlgebraOfGraphics.data","text":"data(table)\n\nCreate a Layer with its data field set to a table-like object.\n\nThere are no type restrictions on this object, as long as it respects the Tables interface. In particular, any one of these formats should work out of the box.\n\nTo create a fully specified layer, the layer created with data needs to be multiplied with the output of mapping.\n\nspec = data(...) * mapping(...)\n\n\n\n\n\n","category":"function"},{"location":"layers/data/","page":"Data","title":"Data","text":"using AlgebraOfGraphics\ndf = (a = rand(10), b = rand(10))\ndata(df)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/time_series.jl\"","category":"page"},{"location":"gallery/gallery/applications/time_series/#Time-series","page":"Time series","title":"Time series","text":"","category":"section"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"using AlgebraOfGraphics, CairoMakie\nusing Dates\n\nx = today() - Year(1) : Day(1) : today()\ny = cumsum(randn(length(x)))\nz = cumsum(randn(length(x)))\ndf = (; x, y, z)\nlabels = [\"series 1\", \"series 2\", \"series 3\", \"series 4\", \"series 5\"]\nplt = data(df) *\n mapping(:x, [:y, :z] .=> \"value\", color=dims(1) => renamer(labels) => \"series \") *\n visual(Lines)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"x = now() - Hour(6) : Minute(1) : now()\ny = cumsum(randn(length(x)))\nz = cumsum(randn(length(x)))\ndf = (; x, y, z)\nplt = data(df) *\n mapping(:x, [:y, :z] .=> \"value\", color=dims(1) => renamer(labels) =>\"series \") *\n visual(Lines)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"dates = Date(2022, 1, 1):Day(1):Date(2022, 1, 31)\ntrend = cumsum(randn(31))\ndf = map(1:2000) do _\n idx = rand(1:31)\n date = dates[idx]\n observation = trend[idx] + 2 * rand()\n return (; date, observation)\nend\nplt = data(df) * mapping(:date, :observation) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/draw/#Drawing-Layers","page":"Drawing Layers","title":"Drawing Layers","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers object can be plotted using the functions draw or draw!.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Whereas draw automatically adds colorbar and legend, draw! does not, as it would be hard to infer a good default placement that works in all scenarios.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Colorbar and legend, should they be necessary, can be added separately with the colorbar! and legend! helper functions. See also Nested layouts for a complex example.","category":"page"},{"location":"layers/draw/#Scale-options","page":"Drawing Layers","title":"Scale options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"All properties that decide how scales are visualized can be modified by passing scale options (using the scales function) as the second argument of draw . The properties that are accepted differ depending on the scale aesthetic type (for example Color, Marker, LineStyle) and whether the scale is categorical or continuous.","category":"page"},{"location":"layers/draw/#Categorical-scale-options","page":"Drawing Layers","title":"Categorical scale options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"All categorical scales take the same properties, independent of aesthetic type.","category":"page"},{"location":"layers/draw/#palette","page":"Drawing Layers","title":"palette","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The palette decides which attribute values are passed to Makie's plotting functions for each categorical value in the scale.","category":"page"},{"location":"layers/draw/#Color","page":"Drawing Layers","title":"Color","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A Symbol is converted to a colormap with Makie.to_colormap. ","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n mapping(:x, :y, color = :z) *\n visual(BarPlot)\ndraw(spec, scales(Color = (; palette = :tab10)))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"It's also possible to directly specify a vector of colors, each of which Makie.to_color can handle:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\nusing CairoMakie.Colors: RGB, RGBA, Gray, HSV\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n mapping(:x, :y, color = :z) *\n visual(BarPlot)\ndraw(spec, scales(Color = (; palette = [:red, :green, :blue, RGB(1, 0, 1), RGB(1, 1, 0), \"#abcff0\", \"#c88cbccc\", HSV(0.9, 0.3, 0.7), RGBA(0.7, 0.9, 0.6, 0.5), Gray(0.5)])))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"If you want to use a continuous colormap for categorical data, you can use the from_continuous helper function. It automatically takes care that the continuous colormap is sampled evenly from start to end depending on the number of categories. Any colormap that Makie understands can be passed, including named colormaps such as :viridis.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"This example shows the difference in behavior when the two-element colormap [:red, :blue] is used with or without from_continuous:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n mapping(:x, :y, color = :z) *\n visual(BarPlot)\n\nf = Figure()\nfg1 = draw!(f[1, 1], spec, scales(Color = (; palette = [:red, :blue])))\nlegend!(f[1, 2], fg1)\nfg2 = draw!(f[1, 3], spec, scales(Color = (; palette = from_continuous([:red, :blue]))))\nlegend!(f[1, 4], fg2)\nf","category":"page"},{"location":"layers/draw/#Marker","page":"Drawing Layers","title":"Marker","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A vector of values that Makie.to_spritemarker can handle.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n mapping(:x, :y, marker = :z) *\n visual(Scatter, markersize = 20)\n\ndraw(\n spec,\n scales(\n Marker = (; palette = [:rect, :circle, :utriangle, :dtriangle, :diamond, :hline, :vline, :star5, :star6, :hexagon])\n )\n)","category":"page"},{"location":"layers/draw/#LineStyle","page":"Drawing Layers","title":"LineStyle","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A vector of values that Makie.to_linestyle can handle.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = repeat('A':'E', inner = 2))) *\n mapping(:x, :y, linestyle = :z) *\n visual(Lines, linewidth = 2)\n\ndraw(spec, scales(\n LineStyle = (; palette = [:solid, :dash, :dot, (:dot, :loose), Linestyle([0, 1, 2, 3, 4, 8])])\n))","category":"page"},{"location":"layers/draw/#X-and-Y","page":"Drawing Layers","title":"X & Y","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The \"palette\" values for X and Y axes are by default simply the numbers from 1 to N, the number of categories. In some circumstances, it might be useful to change these values, for example to visualize that one category is different than others. The palette values are normally assigned category-by-category in the sorted order, or in the order provided manually through the categories keyword. However, if you pass a vector of values, you can always use the category => value pair option to assign a specific category directly to a value, while the others cycle. Here, we do this with \"Unknown\" as it would otherwise be sorted before \"X\".","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (; group = [\"A\", \"B\", \"C\", \"X\", \"Y\", \"Unknown\"], count = [45, 10, 20, 32, 54, 72])\n\nspec = data(df) * mapping(:group, :count) * visual(BarPlot)\n\ndraw(spec, scales(X = (; palette = [1, 2, 3, 5, 6, \"Unknown\" => 8])))","category":"page"},{"location":"layers/draw/#Layout","page":"Drawing Layers","title":"Layout","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Normally, with the Layout aesthetic, rows wrap automatically such that an approximately square distribution of facets is attained. You can overwrite these values, however, to place axes at manually chosen positions:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (;\n group = repeat([\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], inner = 20),\n x = randn(160),\n y = randn(160)\n)\n\nspec = data(df) * mapping(:x, :y, layout = :group) * visual(Scatter)\n\nclockwise = [(1, 1), (1, 2), (1, 3), (2, 3), (3, 3), (3, 2), (3, 1), (2, 1)]\n\ndraw(spec, scales(Layout = (; palette = clockwise)))","category":"page"},{"location":"layers/draw/#categories","page":"Drawing Layers","title":"categories","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The categories keyword can be used to reorder, label and even add categorical values.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Some reordering and renaming can be done using the sorter and renamer helper functions applied directly to columns in mapping. However, this works less well when several data sources are combined where not all categories appear in each column. Also, no categories can be added this way, which is something that can be useful if the existence of categories should be shown even though there is no data for them.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"New labels can be assigned using the value => label pair syntax.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; group = [\"A\", \"C\", \"D\"], value = [1, 3, 4])) *\n mapping(:group, :value) * visual(BarPlot)\n\nf = Figure()\n\ndraw!(f[1, 1], spec, scales(\n X = (; categories = [\"A\", \"B\", \"C\", \"D\"])\n))\ndraw!(f[1, 2], spec, scales(\n X = (; categories = [\"D\", \"A\", \"C\"])\n))\ndraw!(f[1, 3], spec, scales(\n X = (; categories = [\"A\" => \"a\", \"C\" => \"c\", \"D\" => \"d\"])\n))\n\nf","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can also pass a Function to categories which should take the vector of category values and return a new vector of categories or category/label pairs.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"For example, you could add summary statistics to the facet layout titles this way, by grabbing them from a dictionary computed separately.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nsummary_stats = Dict(\"A\" => 1.32, \"B\" => 4.19, \"C\" => 0.04)\n\ndf = (;\n x = randn(90),\n y = randn(90) .+ repeat([0, 5, 10], inner = 30),\n group = repeat([\"A\", \"B\", \"C\"], inner = 30)\n)\n\nspec = data(df) * mapping(:x, :y, col = :group) * visual(Scatter)\n\ndraw(spec, scales(Col = (;\n categories = cats -> [\n cat => rich(\"$cat\\n\", rich(\"λ = $(summary_stats[cat])\", font = :italic))\n for cat in reverse(cats)\n ]\n)))","category":"page"},{"location":"layers/draw/#Continuous-scale-options","page":"Drawing Layers","title":"Continuous scale options","text":"","category":"section"},{"location":"layers/draw/#Color-2","page":"Drawing Layers","title":"Color","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Continuous color scales can be modified using the familiar Makie attributes colormap, colorrange, highclip, lowclip and nan_color. By default, colorrange is set to the extrema of the encountered values, so no clipping occurs.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = [1:4; NaN; 6:10])) *\n mapping(:x, :y, color = :z) *\n visual(Scatter, markersize = 20)\n\ndraw(spec, scales(\n Color = (;\n colormap = :plasma,\n nan_color = :cyan,\n lowclip = :lime,\n highclip = :red,\n colorrange = (2, 9)\n )\n))","category":"page"},{"location":"layers/draw/#MarkerSize","page":"Drawing Layers","title":"MarkerSize","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The range of marker sizes can be set with the sizerange attribute. Marker sizes are computed such that their area, and not markersize itself, grows linearly with the scale values. ","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 10:10:100)) *\n mapping(:x, :y, markersize = :z) *\n visual(Scatter)\n\nf = Figure()\n\ngrid = draw!(f[1, 1], spec, scales(\n MarkerSize = (;\n sizerange = (5, 15)\n )\n))\nlegend!(f[1, 2], grid)\n\ngrid2 = draw!(f[2, 1], spec, scales(\n MarkerSize = (;\n sizerange = (5, 30)\n )\n))\nlegend!(f[2, 2], grid2)\n\nf","category":"page"},{"location":"layers/draw/#Legend-options","page":"Drawing Layers","title":"Legend options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The legend keyword forwards most attributes to Makie's Legend function. The exceptions are listed here.","category":"page"},{"location":"layers/draw/#order","page":"Drawing Layers","title":"order","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"By default, the legend order depends on the order in which layers and mappings have been defined, as well as whether scales are categorical or continuous.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (;\n x = 1:12,\n y = 1:12,\n z = 1:12,\n group1 = repeat([\"A\", \"B\", \"C\"], inner = 4),\n group2 = repeat([\"X\", \"Y\"], 6),\n)\n\nspec = data(df) *\n mapping(:x, :y, markersize = :z, color = :group1, marker = :group2) *\n visual(Scatter)\n\ndraw(spec)","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can reorder the legend with the order keyword. This expects a vector with either Symbols or Vector{Symbol}s as elements, where each Symbol is the identifier for a scale that's represented in the legend.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Plain Symbols can be used for simple reordering:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec; legend = (; order = [:MarkerSize, :Color, :Marker]))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Symbols that are grouped into Vectors indicate that their groups should be merged together. For example, consider this plot that features two color scales, but one for a scatter plot and one for a line plot.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf_a = (; x = 1:9, y = [1, 2, 3, 5, 6, 7, 9, 10, 11], group = repeat([\"A\", \"B\", \"C\"], inner = 3))\n\nspec1 = data(df_a) * mapping(:x, :y, strokecolor = :group) * visual(Scatter, color = :transparent, strokewidth = 3, markersize = 15)\n\ndf_b = (; y = [4, 8], threshold = [\"first\", \"second\"])\n\nspec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(spec1 + spec2_custom_scale)","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can group the two scales together using order. The titles are dropped.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec1 + spec2_custom_scale; legend = (; order = [[:Color, :color2]]))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"If you want to add a title to a merged group, you can add it with the group => title pair syntax:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec1 + spec2_custom_scale; legend = (; order = [[:Color, :color2] => \"Title\"]))","category":"page"},{"location":"layers/draw/#Figure-options","page":"Drawing Layers","title":"Figure options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"AlgebraOfGraphics can add a title, subtitle and footnotes to a figure automatically. Settings for these must be passed to the figure keyword. Check the draw function for a complete list.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = pregrouped(\n fill(1:5, 6),\n fill(11:15, 6),\n [reshape(sin.(1:25), 5, 5) .+ i for i in 1:6],\n layout = 1:6 => nonnumeric) * visual(Heatmap)\n\ndraw(\n spec;\n figure = (;\n title = \"Numbers in square configuration\",\n subtitle = \"Arbitrary data exhibits sinusoidal properties\",\n footnotes = [\n rich(superscript(\"1\"), \"First footnote\"),\n rich(superscript(\"2\"), \"Second \", rich(\"footnote\", color = :red)),\n ],\n ),\n axis = (; width = 100, height = 100)\n)","category":"page"},{"location":"","page":"Home","title":"Home","text":"CurrentModule = AlgebraOfGraphics","category":"page"},{"location":"#Introduction","page":"Home","title":"Introduction","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"AlgebraOfGraphics defines a language for data visualization. It is based on a few simple building blocks that can be combined using + and *.","category":"page"},{"location":"","page":"Home","title":"Home","text":"This package can be installed typing","category":"page"},{"location":"","page":"Home","title":"Home","text":"julia> import Pkg; Pkg.add(\"AlgebraOfGraphics\")","category":"page"},{"location":"","page":"Home","title":"Home","text":"in the Julia REPL.","category":"page"},{"location":"","page":"Home","title":"Home","text":"See the Tutorial to get started.","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/legend_merging.jl\"","category":"page"},{"location":"gallery/gallery/scales/legend_merging/#Legend-merging","page":"Legend merging","title":"Legend merging","text":"","category":"section"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"using AlgebraOfGraphics, CairoMakie\n\nN = 40\n\nx = [1:N; 1:N]\ny = [cumsum(randn(N)); cumsum(randn(N))]\ngrp = [fill(\"a\", N); fill(\"b\", N)]\n\ndf = (; x, y, grp)\n\nlayers = visual(Lines) + visual(Scatter) * mapping(marker = :grp)\nplt = data(df) * layers * mapping(:x, :y, color = :grp)\n\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/regression_plots.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/#Regression-plots","page":"Regression plots","title":"Regression plots","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"using AlgebraOfGraphics, CairoMakie\n\nx = rand(100)\ny = @. randn() + x\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = linear() + visual(Scatter)\ndraw(layers * xy)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"x = rand(100)\ny = @. randn() + 5 * x ^ 2\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = smooth() + visual(Scatter)\nfg = draw(layers * xy)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/continuous_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/#Continuous-scales","page":"Continuous scales","title":"Continuous scales","text":"","category":"section"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"using AlgebraOfGraphics, CairoMakie\n\nx = 1:100\ny = @. sqrt(x) + 20x + 100\ndf = (; x, y)\nplt = data(df) *\n mapping(\n :x,\n :y => log => \"√x + 20x + 100 (log scale)\",\n ) * visual(Lines)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"x = 1:100\ny = @. sqrt(x) + 20x + 100\ndf = (; x, y)\nplt = data(df) *\n mapping(\n :x,\n :y => \"√x + 20x + 100 (log scale)\",\n ) * visual(Lines)\ndraw(plt, axis=(yscale=log,))","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"x = 0:100\ny = @. 0.01 + x/1000\ndf = (; x, y)\nplt = data(df) *\n mapping(\n :x,\n :y => \"y\",\n ) * visual(Lines)\nfg = draw(plt, axis=(yscale=log,))","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"API/functions/#Functions","page":"Functions","title":"Functions","text":"","category":"section"},{"location":"API/functions/#Drawing-functions","page":"Functions","title":"Drawing functions","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"draw\ndraw!\ncolorbar!\nlegend!\npaginate\nscales","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.draw","page":"Functions","title":"AlgebraOfGraphics.draw","text":"draw(d, scales::Scales = scales(); [axis, figure, facet, legend, colorbar])\n\nDraw a AlgebraOfGraphics.AbstractDrawable object d. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. Scale options can be passed as an optional second argument. The output can be customized by passing named tuples or dictionaries with settings via the axis, figure, facet, legend or colorbar keywords. Legend and colorbar are drawn automatically unless show = false is passed to the keyword arguments of either legend or colorbar.\n\nFor finer control, use draw!, legend!, and colorbar! independently.\n\nFigure options\n\nAlgebraOfGraphics accepts the following special keywords under the figure keyword, the remaining attributes are forwarded to Makie's Figure constructor. The title, subtitle and footnotes arguments accept objects of any kind that Makie's Label or text function can handle, such as rich text.\n\n- `title`\n- `subtitle`\n- `titlesize::Union{Nothing,Float64}`\n- `subtitlesize::Union{Nothing,Float64}`\n- `titlealign::Union{Nothing,Symbol}`\n- `titlecolor`\n- `subtitlecolor`\n- `titlefont`\n- `subtitlefont`\n- `titlelineheight`\n- `subtitlelineheight`\n- `footnotes::Union{Nothing,Vector{Any}}`\n- `footnotesize::Union{Nothing,Float64}`\n- `footnotefont`\n- `footnotecolor`\n- `footnotealign`\n- `footnotelineheight`\n\n\n\n\n\ndraw(p::PaginatedLayers; kws...)\n\nDraw each element of PaginatedLayers p and return a Vector{FigureGrid}. Keywords kws are passed to the underlying draw calls.\n\n\n\n\n\ndraw(p::PaginatedLayers, i::Int; kws...)\n\nDraw the ith element of PaginatedLayers p and return a FigureGrid. Keywords kws are passed to the underlying draw call.\n\nYou can retrieve the number of elements using length(p).\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.draw!","page":"Functions","title":"AlgebraOfGraphics.draw!","text":"draw!(fig, d::AbstractDrawable, scales::Scales = scales(); [axis, facet])\n\nDraw a AlgebraOfGraphics.AbstractDrawable object d on fig. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. fig can be a figure, a position in a layout, or an axis if d has no facet specification. The output can be customized by passing named tuples or dictionaries with settings via the axis or facet keywords.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.colorbar!","page":"Functions","title":"AlgebraOfGraphics.colorbar!","text":"colorbar!(figpos, grid; kwargs...)\n\nCompute colorbar for grid (which should be the output of draw!) and draw it in position figpos. Attributes allowed in kwargs are the same as MakieLayout.Colorbar.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.legend!","page":"Functions","title":"AlgebraOfGraphics.legend!","text":"legend!(figpos, grid; order = nothing, kwargs...)\n\nCompute legend for grid (which should be the output of draw!) and draw it in position figpos. All kwargs are forwarded to Makie's Legend constructor.\n\nThe order of scales represented in the legend can be changed with the order keyword. By default, legend sections are ordered the same as they appear in the plot specification. Assuming three scales Color, MarkerSize and custom exist in a spec, you can pass a vector to reorder them like [:MarkerSize, :custom, :Color], or merge multiple entries together with a nested vector like [[:MarkerSize, :custom], :Color], or give merged sections a title with the pair syntax [[:MarkerSize, :custom] => \"Merged group\", :Color].\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.paginate","page":"Functions","title":"AlgebraOfGraphics.paginate","text":"paginate(l; layout=nothing, row=nothing, col=nothing)\n\nPaginate l, the Layer or Layers object created by an AlgebraOfGraphics spec, to create a PaginatedLayers object. This contains a vector of layers where each layer operates on a subset of the input data.\n\nThe PaginatedLayers object can be passed to draw which will return a Vector{FigureGrid} rather than a single figure.\n\nThe keywords that limit the number of subplots on each page are the same that are used to specify facets in mapping:\n\nlayout: Maximum number of subplots in a wrapped linear layout.\nrow: Maximum number of rows in a 2D layout.\ncol: Maximum number of columns in a 2D layout.\n\nExample\n\nd = data((\n x = rand(1000),\n y = rand(1000),\n group1 = rand(string.('a':'i'), 1000),\n group2 = rand(string.('j':'r'), 1000),\n))\n\nlayer_1 = d * mapping(:x, :y, layout = :group1) * visual(Scatter)\npaginated_1 = paginate(layer_1, layout = 9)\nfiguregrids = draw(paginated_1)\n\nlayer_2 = d * mapping(:x, :y, row = :group1, col = :group2) * visual(Scatter)\npaginated_2 = paginate(layer_2, row = 4, col = 3)\nfiguregrid = draw(paginated_2, 1) # draw only the first grid\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scales","page":"Functions","title":"AlgebraOfGraphics.scales","text":"scales(; kwargs...)\n\nCreate a Scales object containing properties for aesthetic scales that can be passed to draw and draw!. Each keyword should be the name of a scale in the spec that is being drawn. That can either be a default one like Color, Marker or LineStyle, or a custom scale name defined in a mapping using the scale function.\n\nThe values attached to the keywords must be dict-like, with Symbols as keys (such as NamedTuples).\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Mapping-helpers","page":"Functions","title":"Mapping helpers","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"pregrouped\ndirect\nrenamer\nsorter\nnonnumeric\nverbatim\nscale\npresorted","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.pregrouped","page":"Functions","title":"AlgebraOfGraphics.pregrouped","text":"pregrouped(positional...; named...)\n\nEquivalent to data(Pregrouped()) * mapping(positional...; named...). Refer to mapping for more information.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.direct","page":"Functions","title":"AlgebraOfGraphics.direct","text":"direct(x)\n\nReturn DirectData(x) which marks x for direct use in a mapping that's used with a table-like data source. As a result, x will be used directly as data, without lookup in the table. If x is not an AbstractArray, it will be expanded like fill(x, n) where n is the number of rows in the data source.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.renamer","page":"Functions","title":"AlgebraOfGraphics.renamer","text":"renamer(arr::Union{AbstractArray, Tuple})\n\nUtility to rename a categorical variable, as in renamer([value1 => label1, value2 => label2]). The keys of all pairs should be all the unique values of the categorical variable and the values should be the corresponding labels. The order of arr is respected in the legend.\n\nExamples\n\njulia> r = renamer([\"class 1\" => \"Class One\", \"class 2\" => \"Class Two\"])\nAlgebraOfGraphics.Renamer{Vector{String}, Vector{String}}([\"class 1\", \"class 2\"], [\"Class One\", \"Class Two\"])\n\njulia> println(r(\"class 1\"))\nClass One\n\nAlternatively, a sequence of pair arguments may be passed.\n\njulia> r = renamer(\"class 1\" => \"Class One\", \"class 2\" => \"Class Two\")\nAlgebraOfGraphics.Renamer{Tuple{String, String}, Tuple{String, String}}((\"class 1\", \"class 2\"), (\"Class One\", \"Class Two\"))\n\njulia> println(r(\"class 1\"))\nClass One\n\nIf arr does not contain Pairs, elements of arr are assumed to be labels, and the unique values of the categorical variable are taken to be the indices of the array. This is particularly useful for dims mappings.\n\nExamples\n\njulia> r = renamer([\"Class One\", \"Class Two\"])\nAlgebraOfGraphics.Renamer{Nothing, Vector{String}}(nothing, [\"Class One\", \"Class Two\"])\n\njulia> println(r(2))\nClass Two\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.sorter","page":"Functions","title":"AlgebraOfGraphics.sorter","text":"sorter(ks)\n\nUtility to reorder a categorical variable, as in sorter([\"low\", \"medium\", \"high\"]). A vararg method sorter(\"low\", \"medium\", \"high\") is also supported. ks should include all the unique values of the categorical variable. The order of ks is respected in the legend.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.nonnumeric","page":"Functions","title":"AlgebraOfGraphics.nonnumeric","text":"nonnumeric(x)\n\nTransform x into a non numeric type that is printed and sorted in the same way.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.verbatim","page":"Functions","title":"AlgebraOfGraphics.verbatim","text":"verbatim(x)\n\nSignal that x should not be rescaled, but used in the plot as is.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scale","page":"Functions","title":"AlgebraOfGraphics.scale","text":"scale(id::Symbol)\n\nCreate a ScaleID object that can be used in a mapping to assign a custom id to the mapped variable. This variable will then not be merged into the default scale for its aesthetic type, but instead be handled separately, leading to a separate legend entry.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.presorted","page":"Functions","title":"AlgebraOfGraphics.presorted","text":"presorted(x)\n\nUse within a pair expression in mapping to signal that a categorical column from the data source should be used in the original order and not automatically sorted.\n\nExample:\n\n# normally, categories would be sorted a, b, c but with `presorted`\n# they stay in the order b, c, a\n\ndata((; some_column = [\"b\", \"c\", \"a\"])) * mapping(:some_column => presorted)\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Theming","page":"Functions","title":"Theming","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"set_aog_theme!\nAlgebraOfGraphics.aog_theme\nfrom_continuous","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.set_aog_theme!","page":"Functions","title":"AlgebraOfGraphics.set_aog_theme!","text":"set_aog_theme!(; kwargs...)\n\nSet the current theme to a predefined and opinionated theme, as defined by the unexported internal function AlgebraOfGraphics.aog_theme.\n\nTo tweak the predefined theme, use the function Makie.update_theme!. See the example below on how to change, e.g., default fontsize, title, and markersize.\n\nFor more information about setting themes, see the Theming section of the Makie.jl docs.\n\nExamples\n\njulia> using CairoMakie, AlgebraOfGraphics\n\njulia> set_aog_theme!() # Sets a prefedined theme\n\njulia> update_theme!( # Tweaks the current theme\n fontsize=30,\n markersize=40,\n Axis=(title=\"MyDefaultTitle\",)\n )\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.aog_theme","page":"Functions","title":"AlgebraOfGraphics.aog_theme","text":"aog_theme(; fonts=[firasans(\"Medium\"), firasans(\"Light\")])\n\nReturn a NamedTuple of theme settings. Intended for internal use. The provided functionality is exposed to the user by the function set_aog_theme!.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.from_continuous","page":"Functions","title":"AlgebraOfGraphics.from_continuous","text":"from_continuous(x)\n\nMark a colormap as continuous such that AlgebraOfGraphics will sample a categorical palette from start to end in n steps, and not by using the first n colors.\n\nYou could also use cgrad(colormap, n; categorical = true), however, this requires you to specify how many levels there are, which from_continuous detects automatically.\n\nExample:\n\ndraw(scales(Color = (; palette = from_continuous(:viridis))))\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Ticks-helpers","page":"Functions","title":"Ticks helpers","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"datetimeticks","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.datetimeticks","page":"Functions","title":"AlgebraOfGraphics.datetimeticks","text":"datetimeticks(datetimes::AbstractVector{<:TimeType}, labels::AbstractVector{<:AbstractString})\n\nGenerate ticks matching datetimes to the corresponding labels. The result can be passed to xticks, yticks, or zticks.\n\n\n\n\n\ndatetimeticks(f, datetimes::AbstractVector{<:TimeType})\n\nCompute ticks for the given datetimes using a formatting function f. The result can be passed to xticks, yticks, or zticks.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Internal-functions","page":"Functions","title":"Internal functions","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"AlgebraOfGraphics.scientific_eltype\nAlgebraOfGraphics.scientific_type\nAlgebraOfGraphics.plottypes_attributes\nAlgebraOfGraphics.compute_attributes","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.scientific_eltype","page":"Functions","title":"AlgebraOfGraphics.scientific_eltype","text":"scientific_eltype(v)\n\nDetermine whether v should be treated as a continuous, geometrical, or categorical array.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scientific_type","page":"Functions","title":"AlgebraOfGraphics.scientific_type","text":"scientific_type(T::Type)\n\nDetermine whether T represents a continuous, geometrical, or categorical variable.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.plottypes_attributes","page":"Functions","title":"AlgebraOfGraphics.plottypes_attributes","text":"plottypes_attributes(entries)\n\nReturn plottypes and relative attributes, as two vectors of the same length, for the given entries.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.compute_attributes","page":"Functions","title":"AlgebraOfGraphics.compute_attributes","text":"compute_attributes(pl::ProcessedLayer, categoricalscales, continuousscales_grid, continuousscales)\n\nProcess attributes of a ProcessedLayer. In particular,\n\nremove AlgebraOfGraphics-specific layout attributes,\nopt out of Makie cycling mechanism,\ncustomize behavior of color (implementing alpha transparency),\ncustomize behavior of bar width (default to one unit when not specified),\nset correct colorrange.\n\nReturn computed attributes.\n\n\n\n\n\n","category":"function"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/presorted_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/#Pre-sorted-data","page":"Pre-sorted data","title":"Pre-sorted data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"using AlgebraOfGraphics, CairoMakie, DataFrames","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"Sometimes we have datasets that have an inherent order we want to preserve. For example, this dataframe has countries sorted by some_value.","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"countries = [\"Algeria\", \"Bolivia\", \"China\", \"Denmark\", \"Ecuador\", \"France\"]\ngroup = [\"2\", \"3\", \"1\", \"1\", \"3\", \"2\"]\nsome_value = exp.(sin.(1:6))\n\ndf = DataFrame(; countries, group, some_value)\nsort!(df, :some_value)\n\ndf","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"
6×3 DataFrame
Rowcountriesgroupsome_value
StringStringFloat64
1Ecuador30.383305
2Denmark10.469164
3France20.756226
4China11.15156
5Algeria22.31978
6Bolivia32.48258
","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"When we plot this, the categorical variable countries is sorted alphabetically by default:","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n mapping(:countries, :some_value, color = :group) *\n visual(BarPlot, direction = :x)\ndraw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"We don't want this, because we have purposefully sorted the dataframe to visualize which countries have the highest value. To retain the order, we can use the presorted helper.","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n mapping(:countries => presorted, :some_value, color = :group) *\n visual(BarPlot, direction = :x)\nfg = draw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"We can also mark multiple variables as presorted, note how the order in the color legend shifts when we do the same for group:","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n mapping(:countries => presorted, :some_value, color = :group => presorted) *\n visual(BarPlot, direction = :x)\ndraw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/basic visualizations/lines_and_markers.jl\"","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#Lines-and-markers","page":"Lines and markers","title":"Lines and markers","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#A-simple-scatter-plot","page":"Lines and markers","title":"A simple scatter plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"using AlgebraOfGraphics, CairoMakie\n\ndf = (x=rand(100), y=rand(100))\nxy = data(df) * mapping(:x, :y)\ndraw(xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#A-simple-lines-plot","page":"Lines and markers","title":"A simple lines plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayer = visual(Lines)\ndraw(layer * xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#Lines-and-scatter-combined-plot","page":"Lines and markers","title":"Lines and scatter combined plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = visual(Scatter) + visual(Lines)\ndraw(layers * xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf1 = (; x, y)\ndf2 = (x=rand(10), y=rand(10))\nlayers = data(df1) * visual(Lines) + data(df2) * visual(Scatter)\nfg = draw(layers * mapping(:x, :y))","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/histograms.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/#Histograms","page":"Histograms","title":"Histograms","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (x=rand(0:99, 1000),)\nplt = data(df) * mapping(:x) * histogram(bins=20)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"df = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c, stack=:c) * histogram(bins=20)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"df = (x=rand(1000), y=randn(1000))\nplt = data(df) * mapping(:x, :y) * histogram(bins=20)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/wide_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Wide-data","page":"Wide data","title":"Wide data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (a=randn(100), b=randn(100), c=randn(100))\nlabels = [\"Trace 1\", \"Trace 2\", \"Trace 3\"]\nplt = data(df) *\n density() *\n mapping([:a, :b, :c] .=> \"some label\") *\n mapping(color=dims(1) => renamer(labels))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"df = (a=rand(100), b=rand(100), c=rand(100), d=rand(100))\nlabels = [\"Trace One\", \"Trace Two\", \"Trace Three\"]\nlayers = linear() + visual(Scatter)\nplt = data(df) * layers * mapping(1, 2:4 .=> \"value\", color=dims(1) => renamer(labels))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"The wide format is combined with broadcast semantics.","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Axes-are-linked-when-they-correspond-to-the-same-variable","page":"Wide data","title":"Axes are linked when they correspond to the same variable","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"df = (\n sepal_length = 1 .+ rand(100),\n sepal_width = 2 .+ rand(100),\n petal_length = 3 .+ rand(100),\n petal_width = 4 .+ rand(100)\n)\nxvars = [\"sepal_length\", \"sepal_width\"]\nyvars = [\"petal_length\" \"petal_width\"]\nlayers = linear() + visual(Scatter)\nplt = data(df) * layers * mapping(xvars, yvars, col=dims(1), row=dims(2))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Axes-can-be-fully-linked-or-fully-unlinked","page":"Wide data","title":"Axes can be fully linked or fully unlinked","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"draw(plt, facet = (; linkxaxes = :all, linkyaxes = :all))","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"fg = draw(plt, facet = (; linkxaxes = :none, linkyaxes = :none))","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/operations/#Algebraic-Operations","page":"Algebraic Operations","title":"Algebraic Operations","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"There are two algebraic types that can be added or multiplied with each other: AlgebraOfGraphics.Layer and AlgebraOfGraphics.Layers.","category":"page"},{"location":"layers/operations/#Multiplication-on-individual-layers","page":"Algebraic Operations","title":"Multiplication on individual layers","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Each layer is composed of data, mappings, and transformations. Datasets can be replaced, mappings can be merged, and transformations can be concatenated. These operations, taken together, define an associative operation on layers, which we call multiplication *.","category":"page"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Multiplication is primarily useful to combine partially defined layers.","category":"page"},{"location":"layers/operations/#Addition","page":"Algebraic Operations","title":"Addition","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"The operation + is used to superimpose separate layers. a + b has as many layers as la + lb, where la and lb are the number of layers in a and b respectively.","category":"page"},{"location":"layers/operations/#Multiplication-on-lists-of-layers","page":"Algebraic Operations","title":"Multiplication on lists of layers","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Multiplication naturally extends to lists of layers. Given two Layers objects a and b, containing la and lb layers respectively, the product a * b contains la * lb layers—all possible pair-wise products.","category":"page"},{"location":"layers/introduction/#Introduction","page":"Introduction","title":"Introduction","text":"","category":"section"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Layers are the key building blocks of AlgebraOfGraphics. Each layer is the product of the following elementary objects.","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Data (encoding the dataset).\nMapping (associating variables to plot attributes).\nVisual (encoding data-independent plot information).\nAnalyses (encoding transformations that are applied to the data before plotting).","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Data, mappings, visuals and analyses can be combined together using Algebraic Operations to form one or more layers.","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"The output of these algebraic operations can be visualized, as shown in the section Drawing Layers.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/discrete_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/#Discrete-scales","page":"Discrete scales","title":"Discrete scales","text":"","category":"section"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"By default categorical ticks, as well as names from legend entries, are taken from the value of the variable converted to a string. Scales can be equipped with labels to overwrite that. You can either use the renamer function to apply relabeling or reordering to some column on the fly, or you use the categories keyword for the respective scale in the draw call.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"note: Note\nThe categories keyword (added in v0.7) also allows adding categories not present in the data.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand(100))\nplt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n categories = [\"a\" => \"label1\", \"b\" => \"label2\", \"c\" => \"label3\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"The same operation can be done with renamer as well which modifies the input data","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) *\n mapping(\n :x => renamer(\"a\" => \"label1\", \"b\" => \"label2\", \"c\" => \"label3\"),\n :y\n ) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"With categories, you can add further categories that might be missing from your data","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n categories = [\"a\", \"missing\", \"b\", \"c\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"The order can also be changed:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n categories = [\"b\" => \"label b\", \"a\" => \"label a\", \"c\" => \"label c\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Or with renamer:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) *\n mapping(\n :x => renamer(\"b\" => \"label b\", \"a\" => \"label a\", \"c\" => \"label c\"),\n :y\n ) * visual(BoxPlot)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"When categories come from different datasets, you can either apply the same renamer to multiple mappings, or set the ordering at one place in categories:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df1 = (; x = rand([\"one\", \"two\"], 100), y = randn(100))\ndf2 = (; x = rand([\"three\", \"four\"], 50), y = randn(50))\nplt = (data(df1) + data(df2)) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"draw(plt, scales(X = (;\n categories = [\"one\", \"two\", \"three\", \"four\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"You can also pass a function to categories, which has to return a vector with (optionally labelled) categories in the desired order. For example, strings are by default ordered alphabetically:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df = (; name = [\"Anna Coolidge\", \"Berta Bauer\", \"Charlie Archer\"], age = [34, 79, 58])\nplt = data(df) * mapping(:name, :age) * visual(BarPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Instead of specifying the order manually, we could use a function to order by last name:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"draw(plt, scales(X = (;\n categories = cats -> sort(cats; by = name -> split(name)[2])\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Or even combine this with a relabeling function that shortens the first name to the initial:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"function initialed(name)\n a, b = split(name)\n return name => \"$(first(a)). $b\"\nend\n\ndraw(plt, scales(X = (;\n categories = cats -> initialed.(sort(cats; by = name -> split(name)[2]))\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/layout/faceting.jl\"","category":"page"},{"location":"gallery/gallery/layout/faceting/#Faceting","page":"Faceting","title":"Faceting","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid","page":"Faceting","title":"Facet grid","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = let\n N = 100\n x0 = rand(1:10, N)\n i = rand([\"α\", \"β\"], N)\n j = rand([\"a\", \"b\", \"c\"], N)\n\n x = map(zip(x0, j)) do (xx, jj)\n shift = jj == \"a\" ? -2.9 : jj == \"c\" ? 2.9 : 0.0\n xx + shift\n end\n\n y = map(zip(x0, i)) do (xx, ii)\n shift = ii == \"α\" ? -3.9 : 3.9\n xx + 2 + shift + rand()\n end\n\n (; x, y, i, j)\nend\n\nplt = data(df) * mapping(:x, :y, row=:i, col=:j)\n\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid-with-minimal-axes-linking-needed-to-remove-ticks","page":"Faceting","title":"Facet grid with minimal axes linking needed to remove ticks","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:minimal, linkyaxes=:minimal))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid-with-unlinked-x-axes","page":"Faceting","title":"Facet grid with unlinked x-axes","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:none))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap","page":"Faceting","title":"Facet wrap","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(100), y=rand(100), l=rand([\"a\", \"b\", \"c\", \"d\", \"e\"], 100))\nplt = data(df) * mapping(:x, :y, layout=:l)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap-with-unlinked-axes","page":"Faceting","title":"Facet wrap with unlinked axes","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:none, linkyaxes=:none))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap-with-specified-layout-for-rows-and-cols","page":"Faceting","title":"Facet wrap with specified layout for rows and cols","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, scales(Layout = (; palette = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2)])))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Adding-traces-to-only-some-subplots","page":"Faceting","title":"Adding traces to only some subplots","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df1 = (x=rand(100), y=rand(100), i=rand([\"a\", \"b\", \"c\"], 100), j=rand([\"d\", \"e\", \"f\"], 100))\ndf2 = (x=[0, 1], y=[0.5, 0.5], i=fill(\"a\", 2), j=fill(\"e\", 2))\nlayers = data(df1) * visual(Scatter) + data(df2) * visual(Lines)\nfg = draw(layers * mapping(:x, :y, col=:i, row=:j))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Caveats","page":"Faceting","title":"Caveats","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"The faceting variable must be non-numeric. If the source is numeric, you can convert it with nonnumeric.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(100), y=rand(100), l=rand([1, 2, 3, 4, 5], 100))\nplt = data(df) * mapping(:x, :y, layout=:l => nonnumeric)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Pagination","page":"Faceting","title":"Pagination","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"If you have too many facets for one figure, you can use paginate to split the data into several subsets given a maximum number of plots per layout, row or column.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"Note that pagination is considered an experimental feature. In the current implementation, scales and layouts are not synchronized across pages. This means that, e.g., linked limits on one page are not influenced by limits of other pages. The exact synchronization behavior can be subject to change in non-breaking versions.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(500), y=rand(500), l=rand([\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"], 500))\nplt = data(df) * mapping(:x, :y, layout=:l)\npag = paginate(plt, layout = 4)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"PaginatedLayers with 2 entries (layout = 4)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"The object returned from draw will be a Vector{FigureGrid}.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"figuregrids = draw(pag)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"2-element Vector{AlgebraOfGraphics.FigureGrid}:\n FigureGrid()\n FigureGrid()","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"You can either extract single figures from this vector...","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"figuregrids[1]","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"or use draw with an optional second argument specifying the index of the page to draw.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(pag, 2)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"EditURL = \"penguins.jl\"","category":"page"},{"location":"generated/penguins/#Tutorial","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This is a gentle and lighthearted tutorial on how to use tools from AlgebraOfGraphics, using as example dataset a collection of measurements on penguins[1]. See the Palmer penguins website for more information.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"[1]: Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. DOI","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"To follow along this tutorial, you will need to install a few packages. All the required packages can be installed with the following command.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"julia> import Pkg; Pkg.add([\"AlgebraOfGraphics\", \"CairoMakie\", \"DataFrames\", \"LIBSVM\", \"PalmerPenguins\"])","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"After the above command completes, we are ready to go.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using PalmerPenguins, DataFrames\n\npenguins = dropmissing(DataFrame(PalmerPenguins.load()))\nfirst(penguins, 6)","category":"page"},{"location":"generated/penguins/#Frequency-plots","page":"Tutorial 🐧","title":"Frequency plots","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Let us start by getting a rough idea of how the data is distributed.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"note: Note\nDue to julia's compilation model, the first plot may take a while to appear.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\naxis = (width = 225, height = 225)\npenguin_frequency = data(penguins) * frequency() * mapping(:species)\n\ndraw(penguin_frequency; axis = axis)","category":"page"},{"location":"generated/penguins/#Small-intermezzo:-saving-the-plot","page":"Tutorial 🐧","title":"Small intermezzo: saving the plot","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"If you are working in an interactive enviroment with inline plotting support, such VSCode or Pluto.jl, the above should have displayed a bar plot. If you are working directly in the console, you can simply save the plot and inspect it in the file explorer.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"fg = draw(penguin_frequency; axis = axis)\nsave(\"figure.png\", fg, px_per_unit = 3) # save high-resolution png","category":"page"},{"location":"generated/penguins/#Styling-by-categorical-variables","page":"Tutorial 🐧","title":"Styling by categorical variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Next, let us see whether the distribution is the same across islands.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Oops! The bars are in the same spot and are hiding each other. We need to specify how we want to fix this. Bars can either dodge each other, or be stacked on top of each other.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island, dodge = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This is our first finding. Adelie is the only species of penguins that can be found on all three islands. To be able to see both which species is more numerous and how different species are distributed across islands in a unique plot, we could have used stack.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island, stack = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Correlating-two-variables","page":"Tutorial 🐧","title":"Correlating two variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Now that we have understood the distribution of these three penguin species, we can start analyzing their features.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"penguin_bill = data(penguins) * mapping(:bill_length_mm, :bill_depth_mm)\ndraw(penguin_bill; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We would actually prefer to visualize these measures in centimeters, and to have cleaner axes labels. As we want this setting to be preserved in all of our bill visualizations, let us save it in the variable penguin_bill, to be reused in subsequent plots.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"penguin_bill = data(penguins) * mapping(\n :bill_length_mm => (t -> t / 10) => \"bill length (cm)\",\n :bill_depth_mm => (t -> t / 10) => \"bill depth (cm)\",\n)\ndraw(penguin_bill; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Much better! Note the parentheses around the function t -> t / 10. They are necessary to specify that the function maps t to t / 10, and not to t / 10 => \"bill length (cm)\".","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"There does not seem to be a strong correlation between the two dimensions, which is odd. Maybe dividing the data by species will help.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Ha! Within each species, penguins with a longer bill also have a deeper bill. We can confirm that with a linear regression","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * linear() * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This unfortunately no longer shows our data! We can use + to plot both things on top of each other:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * linear() * mapping(color = :species) + penguin_bill * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Note that the above expression seems a bit redundant, as we wrote the same thing twice. We can \"factor it out\" as follows","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * (linear() + mapping()) * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"where mapping() is a neutral multiplicative element. Of course, the above could be refactored as","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We could actually take advantage of the spare mapping() and use it to pass some extra info to the scatter, while still using all the species members to compute the linear fit.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping(marker = :sex)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This plot is getting a little bit crowded. We could instead show female and male penguins in separate subplots.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping(col = :sex)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"See how both plots show the same fit, because the sex mapping is not applied to linear(). The following on the other hand produces a separate fit for males and females:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species, col = :sex)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Smooth-density-plots","page":"Tutorial 🐧","title":"Smooth density plots","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"An alternative approach to understanding how two variables interact is to consider their joint probability density distribution (pdf).","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using AlgebraOfGraphics: density\nplt = penguin_bill * density(npoints=50) * mapping(col = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The default colormap is multi-hue, but it is possible to pass single-hue colormaps as well. The color range is inferred from the data by default, but it can also be passed manually. Both settings are passed via scales to draw, because multiple plots can share the same colormap, so visual is not the appropriate place for this setting.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"draw(plt, scales(Color = (; colormap = :grayC, colorrange = (0, 6))); axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"A Heatmap (the default visualization for a 2D density) is a bit unfortunate if we want to mark species by color. In that case, one can use visual to change the default visualization and, optionally, fine tune some arguments. In this case, a Wireframe with thin lines looks quite nice. (Note that, for the time being, we must specify explicitly that we require a 3D axis.)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (type = Axis3, width = 300, height = 300)\nlayer = density() * visual(Wireframe, linewidth=0.05)\nplt = penguin_bill * layer * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Of course, a more traditional approach would be to use a Contour plot instead:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (width = 225, height = 225)\nlayer = density() * visual(Contour)\nplt = penguin_bill * layer * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The data and the linear fit can also be added back to the plot:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = density() * visual(Contour) + linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"In the case of many layers (contour, density and scatter) it is important to think about balance. In the above plot, the markers are quite heavy and can obscure the linear fit and the contour lines. We can lighten the markers using alpha transparency.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = density() * visual(Contour) + linear() + visual(alpha = 0.5)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Correlating-three-variables","page":"Tutorial 🐧","title":"Correlating three variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We are now mostly up to speed with bill size, but we have not considered how it relates to other penguin features, such as their weight. For that, a possible approach is to use a continuous color on a gradient to denote weight and different marker shapes to denote species. Here we use group to split the data for the linear regression without adding any additional style.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"body_mass = :body_mass_g => (t -> t / 1000) => \"body mass (kg)\"\nlayers = linear() * mapping(group = :species) + mapping(color = body_mass, marker = :species)\nplt = penguin_bill * layers\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Naturally, within each species, heavier penguins have bigger bills, but perhaps counter-intuitively the species with the shallowest bills features the heaviest penguins. We could also try and see the interplay of these three variables in a 3D plot.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (type = Axis3, width = 300, height = 300)\nplt = penguin_bill * mapping(body_mass, color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * mapping(body_mass, color = :species, layout = :sex)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Note that static 3D plot can be misleading, as they only show one projection of 3D data. They are mostly useful when shown interactively.","category":"page"},{"location":"generated/penguins/#Machine-Learning","page":"Tutorial 🐧","title":"Machine Learning","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Finally, let us use Machine Learning techniques to build an automated penguin classifier!","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We would like to investigate whether it is possible to predict the species of a penguin based on its bill size. To do so, we will use a standard classifier technique called Support-Vector Machine.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The strategy is quite simple. We split the data into training and testing subdatasets. We then train our classifier on the training dataset and use it to make predictions on the whole data. We then add the new columns obtained this way to the dataset and visually inspect how well the classifier performed in both training and testing.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using LIBSVM, Random\n\n# use approximately 80% of penguins for training\nRandom.seed!(1234) # for reproducibility\nN = nrow(penguins)\ntrain = fill(false, N)\nperm = randperm(N)\ntrain_idxs = perm[1:floor(Int, 0.8N)]\ntrain[train_idxs] .= true\nnothing # hide\n\n# fit model on training data and make predictions on the whole dataset\nX = hcat(penguins.bill_length_mm, penguins.bill_depth_mm)\ny = penguins.species\nmodel = SVC() # Support-Vector Machine Classifier\nfit!(model, X[train, :], y[train])\nŷ = predict(model, X)\n\n# incorporate relevant information in the dataset\npenguins.train = train\npenguins.predicted_species = ŷ\nnothing #hide","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Now, we have all the columns we need to evaluate how well our classifier performed.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (width = 225, height = 225)\ndataset =:train => renamer(true => \"training\", false => \"testing\") => \"Dataset\"\naccuracy = (:species, :predicted_species) => isequal => \"accuracy\"\nplt = data(penguins) *\n expectation() *\n mapping(:species, accuracy) *\n mapping(col = dataset)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"That is a bit hard to read, as all values are very close to 1. Let us visualize the error rate instead.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"error_rate = (:species, :predicted_species) => !isequal => \"error rate\"\nplt = data(penguins) *\n expectation() *\n mapping(:species, error_rate) *\n mapping(col = dataset)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"So, mostly our classifier is doing quite well, but there are some mistakes, especially among Chinstrap penguins. Using at the same time the species and predicted_species mappings on different attributes, we can see which penguins are problematic.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"prediction = :predicted_species => \"predicted species\"\ndatalayer = mapping(color = prediction, row = :species, col = dataset)\nplt = penguin_bill * datalayer\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Um, some of the penguins are indeed being misclassified... Let us try to understand why by adding an extra layer, which describes the density of the distributions of the three species.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"pdflayer = density() * visual(Contour, colormap=Reverse(:grays)) * mapping(group = :species)\nlayers = pdflayer + datalayer\nplt = penguin_bill * layers\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We can conclude that the classifier is doing a reasonable job: it is mostly making mistakes on outlier penguins.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/geometries.jl\"","category":"page"},{"location":"gallery/gallery/applications/geometries/#Geometries","page":"Geometries","title":"Geometries","text":"","category":"section"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"using AlgebraOfGraphics, CairoMakie\nusing GeometryBasics\n\ngeometry = [Rect(Vec(i, j), Vec(1, 1)) for i in 0:7 for j in 0:7]\ngroup = [isodd(i + j) ? \"light square\" : \"dark square\" for i in 0:7 for j in 0:7]\ndf = (; geometry, group)\n\nplt = data(df) * visual(Poly) * mapping(:geometry, color = :group)\nfg = draw(plt; axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/index.md\"","category":"page"},{"location":"gallery/#Gallery","page":"Gallery","title":"Gallery","text":"","category":"section"},{"location":"gallery/#Basic-visualizations","page":"Gallery","title":"Basic visualizations","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Lines and markers\n

\n

\n Basic linear and scatter plots.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Statistical visualizations\n

\n

\n Violin plot, boxplot, qqplot.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Statistical-analyses","page":"Gallery","title":"Statistical analyses","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Density plots\n

\n

\n Visualizing kernel density estimation of data.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Histograms\n

\n

\n Computing 1- and 2-dimensional histograms.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Regression plots\n

\n

\n Linear and nonlinear regressions.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Data-manipulations","page":"Gallery","title":"Data manipulations","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Renaming and transforming variables\n

\n

\n Transforming data before generating the plot.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Using mapping without tabular data\n

\n

\n Passing columnar data directly to `mapping`.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Pre-grouped data\n

\n

\n Working with arrays of arrays.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Pre-sorted data\n

\n

\n Preserving the original order of categorical data.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Wide data\n

\n

\n Working with data in the wide format.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Scales","page":"Gallery","title":"Scales","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Some advanced keywords to tweak the plot","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Discrete scales\n

\n

\n Sorting and renaming categorical variables.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
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\n Continuous scales\n

\n

\n Applying nonlinear transformations.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Custom scales\n

\n

\n Custom palettes and custom attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Secondary scales\n

\n

\n Categorical and continuous scales in the same plot.\n

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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
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\n Multiple color scales\n

\n

\n Categorical and continuous scales in the same plot.\n

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\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Pre-scaled data\n

\n

\n Pass data to the plot as is.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Legend merging\n

\n

\n Multiple scales for the same variable.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Dodging\n

\n

\n Dodging groups to avoid overlaps.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Customization","page":"Gallery","title":"Customization","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Some advanced keywords to tweak the plot","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Axis tweaking\n

\n

\n Setting axis attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Figure tweaking\n

\n

\n Setting figure attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Legend tweaking\n

\n

\n Setting legend attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Colorbar tweaking\n

\n

\n Setting colorbar attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Layout","page":"Gallery","title":"Layout","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Faceting\n

\n

\n Generating a grid of plots.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Nested layouts\n

\n

\n AlgebraOfGraphics plots within a Makie figure.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Applications","page":"Gallery","title":"Applications","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Example applications with different types of data","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Geographic data\n

\n

\n Antarctic coastline.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Geometries\n

\n

\n Visualizing geometries.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Time series\n

\n

\n Visualizing time series data.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/new_columns_on_the_fly.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/#Renaming-and-transforming-variables","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"using AlgebraOfGraphics, CairoMakie\n\ndf = (x=rand(100), y=rand(100), z=rand(100), c=rand([\"a\", \"b\"], 100))\nlayers = linear() + mapping(color=:z)\n\nplt = data(df) * layers * mapping(:x => (x -> x^2) => L\"x^2\", :y => L\"This variable is called $y$\")\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"Use a Tuple to pass combine several columns into a unique operation.","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"plt = data(df) * layers * mapping(:x, (:x, :y, :z) => (+) => L\"the new variable $x + y + z$\", layout=:c)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/colorbar.jl\"","category":"page"},{"location":"gallery/gallery/customization/colorbar/#Colorbar-tweaking","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: Source code)","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"To tweak the position and appearance of the colorbar, simply use the colorbar keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100))\nplt = data(df) * mapping(:x, :y, color=:z)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"fg = draw(plt, colorbar=(position=:top, size=25))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"To change the colormap, you have to modify the corresponding scale. Usually, this will be the Color scale.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"note: Note\nBefore AlgebraOfGraphics v0.7, you would change the colormap by passing it via visual. This was changed so that each color scale, which can be used by multiple plot layers, has a single source of truth for these settings.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"draw(plt, scales(Color = (; colormap = :thermal)))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"Other continuous color parameters are highclip, lowclip, nan_color and colorrange.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"draw(plt, scales(Color = (;\n colormap = :thermal,\n colorrange = (0.25, 0.75),\n highclip = :cyan,\n lowclip = :lime,\n)))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/custom_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/custom_scales/#custom_scales","page":"Custom scales","title":"Custom scales","text":"","category":"section"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"A palette maps categorical values to particular attribute specifications (e.g. the first value maps to green, the second maps to red, and so on).","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x=repeat(1:20, inner=20)\ny=repeat(1:20, outer=20)\nu=cos.(x)\nv=sin.(y)\nc=rand(Bool, length(x))\nd=rand(Bool, length(x))\ndf = (; x, y, u, v, c, d)\ncolors = [colorant\"#E24A33\", colorant\"#348ABD\"]\nheads = ['◮', '◭']\nplt = data(df) *\n mapping(:x, :y, :u, :v) *\n mapping(arrowhead = :c => nonnumeric) *\n mapping(color = :d => nonnumeric) *\n visual(Arrows, arrowsize=10, lengthscale=0.4, linewidth = 1)\nfg = draw(plt, scales(Marker = (; palette = heads), Color = (; palette = colors)))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"To associate specific attribute values to specific data values, use pairs. Missing keys will cycle over values that are not pairs.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x = rand(100)\ny = rand(100)\nz = rand([\"a\", \"b\", \"c\", \"d\"], 100)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z)\ncolors = [\"a\" => :tomato, \"c\" => :lime, colorant\"#988ED5\", colorant\"#777777\"]\ndraw(plt, scales(Color = (; palette = colors)))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"Categorical color gradients can also be passed to palettes. You can use the from_continuous helper function to wrap continuous colormaps which you want to sample from start to end in n steps, where n is the number of categories you are visualizing.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x = rand(200)\ny = rand(200)\nz = rand([\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"], 200)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z)\ndraw(plt, scales(Color = (; palette = from_continuous(:cividis))))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/dodging.jl\"","category":"page"},{"location":"gallery/gallery/scales/dodging/#dodging","page":"Dodging","title":"Dodging","text":"","category":"section"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"Some plot types like barplots natively support a dodge attribute which avoids overlap between groups that share the same coordinates.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"df = (; x = [\"One\", \"One\", \"Two\", \"Two\"], y = 1:4, err = [0.2, 0.3, 0.4, 0.5], group = [\"A\", \"B\", \"A\", \"B\"])\nplt = data(df) * mapping(:x, :y, dodge = :group, color = :group) * visual(BarPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"Other plot types like errorbars do not have a dodge keyword, however you can dodge them using AlgebraOfGraphic's hardcoded dodge_x or dodge_y mappings. These will only shift the data away from the category centers but will not change other plot attributes (like dodging a barplot makes narrower bars). They are therefore mostly appropriate for \"width-less\" plot types like scatters or errorbars.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"If you combine errorbars with a barplot, AlgebraOfGraphics will apply the barplot's dodge width to the errorbars automatically so they match:","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"plt2 = data(df) * mapping(:x, :y, :err, dodge_x = :group) * visual(Errorbars)\nfg = draw(plt + plt2)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"If you only use \"width-less\" plot types, you will get an error if you don't set a dodge width manually. You can do so via the scales function:","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"df = (\n x = repeat(1:10, inner = 2),\n y = cos.(range(0, 2pi, length = 20)),\n ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt3 = data(df) * (\n mapping(:x, :y, dodge_x = :dodge, color = :dodge) * visual(Scatter) +\n mapping(:x, :ylow, :yhigh, dodge_x = :dodge, color = :dodge) * visual(Rangebars)\n)\nkw(; kwargs...) = (; xticklabelsvisible = false, xticksvisible = false, xlabelvisible = false, kwargs...)\n\ndraw!(f[1, 1], plt3, scales(DodgeX = (; width = 0.25)); axis = kw(title = \"DodgeX = (; width = 0.25)\"))\ndraw!(f[2, 1], plt3, scales(DodgeX = (; width = 0.5)); axis = kw(title = \"DodgeX = (; width = 0.5)\"))\ndraw!(f[3, 1], plt3, scales(DodgeX = (; width = 1.0)); axis = (; title = \"DodgeX = (; width = 1.0)\"))\n\nf","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/prescaled_data.jl\"","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/#Pre-scaled-data","page":"Pre-scaled data","title":"Pre-scaled data","text":"","category":"section"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors\n\nx = rand(100)\ny = rand(100)\nz = rand([colorant\"teal\", colorant\"orange\"], 100)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z => verbatim)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"Plotting labels instead of markers","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"x = rand(100)\ny = rand(100)\nlabel = rand([\"a\", \"b\"], 100)\ndf = (; x, y, label)\nplt = data(df) * mapping(:x, :y, text=:label => verbatim) * visual(Makie.Text)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/layout/nested_layouts.jl\"","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/#Nested-layouts","page":"Nested layouts","title":"Nested layouts","text":"","category":"section"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"All AlgebraOfGraphics plots can be inserted in any figure position, where the rest of the figure is managed by vanilla Makie. For example","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"fig = Figure(; size=(800, 600))\nax = Axis(fig[1, 1], title=\"Some plot\")\n\ndf = (\n x=rand(500),\n y=rand(500),\n i=rand([\"a\", \"b\", \"c\"], 500),\n j=rand([\"d\", \"e\", \"f\"], 500),\n k=rand(Bool, 500),\n l=rand(Bool, 500)\n)\nplt = data(df) * mapping(:x, :y, col=:i, row=:j, color=:k, marker=:l)\n\nsubfig = fig[1, 2:3]\nag = draw!(subfig, plt)\nfor ae in ag\n ae.axis.xticklabelrotation[] = π/2\nend\nlegend!(fig[end+1, 2], ag, orientation=:horizontal, tellheight=true)\nfig","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"The above also works in more nested situations.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"f = Figure(; size=(800, 600))\nax = Axis(f[1, 1], title=\"Some plot\")\nsubfig = f[1, 2]\nax2 = Axis(subfig[1, 1])\n\ndf = (\n x=rand(500),\n y=rand(500),\n c=rand([\"a\", \"b\", \"c\"], 500),\n)\nplt = data(df) * mapping(:x, :y, color=:c)\n\ndraw!(subfig[2, 1], plt)\nf","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"It is also possible to let Makie control the axis and plot directly on top of it.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"f = Figure(; size=(800, 300))\nax1 = Axis(f[1, 1])\nax2 = Axis(f[1, 2])\n\ndf = (x=rand(100), y=rand(100), c=rand([\"a\", \"b\", \"c\"], 100))\nplt = data(df) * mapping(:x, :y, color=:c)\n\nscatter!(ax1, rand(10), rand(10), color=:black)\ngrid = draw!(ax2, plt)\nlegend!(f[1, 3], grid)\n\nf","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/geographic.jl\"","category":"page"},{"location":"gallery/gallery/applications/geographic/#Geographic-data","page":"Geographic data","title":"Geographic data","text":"","category":"section"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"using AlgebraOfGraphics, CairoMakie\nusing Shapefile, ZipFile\nusing Downloads","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"Antarctic coastline. Data from the SCAR Antarctic Digital Database[1].","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"[1]: Gerrish, L., Fretwell, P., & Cooper, P. (2021). Medium resolution vector polygons of the Antarctic coastline (7.4) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/747e63e-9d93-49c2-bafc-cf3d3f8e5afa","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"# Download, extract, and load shapefile\nt = mktempdir() do dir\n url = \"https://ramadda.data.bas.ac.uk/repository/entry/get/add_coastline_medium_res_polygon_v7_4.zip?entryid=synth%3Ae747e63e-9d93-49c2-bafc-cf3d3f8e5afa%3AL2FkZF9jb2FzdGxpbmVfbWVkaXVtX3Jlc19wb2x5Z29uX3Y3XzQuemlw\"\n r = ZipFile.Reader(seekstart(Downloads.download(url, IOBuffer())))\n for f in r.files\n open(joinpath(dir, f.name), write = true) do io\n write(io, read(f, String));\n end\n end\n Shapefile.Table(joinpath(dir, \"add_coastline_medium_res_polygon_v7_4.shp\"))\nend\n\n# Draw map\nplt = data(t) * mapping(:geometry, color = :surface) * visual(Choropleth)\nfg = draw(plt; axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"}] +[{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/basic visualizations/statistical_visualizations.jl\"","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/#Statistical-visualizations","page":"Statistical visualizations","title":"Statistical visualizations","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"using AlgebraOfGraphics, CairoMakie, PalmerPenguins, DataFrames\n\npenguins = dropmissing(DataFrame(PalmerPenguins.load()))\n\ndata(penguins) * visual(Violin) *\n mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"plt = data(penguins) * visual(Violin, datalimits=extrema)\nplt *= mapping(:species, :bill_depth_mm, color=:sex, side=:sex, dodge=:island)\nfg = draw(plt, axis=(limits=((0.5, 3.5), nothing),))","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"data(penguins) * visual(BoxPlot, show_notch=true) *\n mapping(:species, :bill_depth_mm, color=:sex, dodge=:sex) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"data(penguins) *\n mapping(:bill_length_mm, :bill_depth_mm, col=:sex) *\n visual(QQPlot, qqline=:fit) |> draw","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"","category":"page"},{"location":"gallery/gallery/basic visualizations/statistical_visualizations/","page":"Statistical visualizations","title":"Statistical visualizations","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"FAQs/#Frequently-Asked-Questions","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"","category":"section"},{"location":"FAQs/#What-is-the-algebraic-structure-of-AlgebraOfGraphics?","page":"Frequently Asked Questions","title":"What is the algebraic structure of AlgebraOfGraphics?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"AlgebraOfGraphics is based on two operators, + and *. These two operators induce a semiring structure, with a small caveat. Addition is commutative only up to the drawing order. For example, visual(Lines) + visual(Scatter) is slightly different from visual(Scatter) + visual(Lines), in that the former draws the scatter on top of the lines, and the latter draws the lines on top of the scatter. As a consequence, only right distributivity holds with full generality, whereas left distributivity only holds up to the drawing order.","category":"page"},{"location":"FAQs/#Why-is-the-mapping-pair-syntax-different-from-DataFrames?","page":"Frequently Asked Questions","title":"Why is the mapping pair syntax different from DataFrames?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The transformations passed within a mapping, e.g. mapping(:x => log => \"log(x)\"), are applied element-wise. Operations that require the whole column are not supported on purpose. An important reason to prefer element-wise operations (other than performance) is that whole-column operations can be error prone in this setting, especially when","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"the data is grouped or\ndifferent datasets are used.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"If you do need column-wise transformations, consider implementing a custom analysis, such as density, which takes the whole data as input, or apply the transformation directly in your data before passing it to AlgebraOfGraphics.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"See also Pair syntax for a detailed description of the pair syntax within a mapping.","category":"page"},{"location":"FAQs/#What-is-the-difference-between-axis-scales-and-data-transformations?","page":"Frequently Asked Questions","title":"What is the difference between axis scales and data transformations?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"There are two overlapping but distinct ways to rescale data.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Keep the data as is and use a nonlinear scale, e.g. axis=(xscale=log,).\nTransform the data directly, e.g. mapping(:x => log => \"log(x)\").","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Note that the resulting plots may \"look different\" in some cases. Consider for instance the following example.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics\nusing AlgebraOfGraphics: density\ndf = (x = exp.(randn(1000)),)\nkde1 = data(df) * mapping(:x) * density()\ndraw(kde1, axis=(width=225, height=225, xscale=log,))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"df = (x = exp.(randn(1000)),)\nkde2 = data(df) * mapping(:x => log => \"log(x)\") * density()\ndraw(kde2, axis=(width=225, height=225))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The two plots look different. The first represents the pdf of x in a log scale, while the second represents the pdf of log(x) in a linear scale. The two curves differ by a factor 1 / x, the derivative of log(x). See e.g. this post for some mathematical background on the topic.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"In general, the second approach (plotting the density of log(x)) could be considered more principled, as it preserves the proportionality between area and probability mass. On the contrary, the first approach (plotting the density of x in a log scale) breaks this proportionality relationship.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"A similar reasoning applies to histograms:","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics\ndf = (x = exp.(rand(1000)),)\nhist1 = data(df) * mapping(:x) * histogram()\ndraw(hist1, axis=(width=225, height=225, xscale=log))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"df = (x = exp.(rand(1000)),)\nhist2 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\ndraw(hist2, axis=(width=225, height=225))","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"The data transformation approach is preferable as it produces uniform bins, which are easier to interpret.","category":"page"},{"location":"FAQs/#How-to-combine-AlgebraOfGraphics-with-plain-Makie-plots?","page":"Frequently Asked Questions","title":"How to combine AlgebraOfGraphics with plain Makie plots?","text":"","category":"section"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Since AlgebraOfGraphics is built upon the Makie ecosystem we can easily combine plots from both packages. Two approaches can be taken. Firstly, by using draw! you can pass a Figure or FigurePosition created by Makie to be used by AlgebraOfGraphics, e.g.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics, CairoMakie #hide\n\nf, a, p = lines(0..2pi, sin; figure = (size = (600, 400),))\n\ndf = (x = exp.(rand(1000)),)\nhist1 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\ndraw!(f[1, 2], hist1)\n\nf #hide","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"Alternatively, we can create the AlgebraOfGraphics figure first and then add in additional plain Makie axes alongside the result by accessing the .figure field of fg, e.g.","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"using AlgebraOfGraphics, CairoMakie #hide\n\ndf = (x = exp.(rand(1000)),)\nhist2 = data(df) * mapping(:x => log => \"log(x)\") * histogram()\nfg = draw(hist2; figure = (size = (600, 400),))\n\nlines(fg.figure[1, 2], 0..2pi, cos)\n\nfg #hide","category":"page"},{"location":"FAQs/","page":"Frequently Asked Questions","title":"Frequently Asked Questions","text":"note: Note\nWhen setting the width and height dimensions of each axis manually you will need to call resize_to_layout!(fg) before displaying the figure such that each axis is sized correctly.","category":"page"},{"location":"philosophy/#Philosophy","page":"Philosophy","title":"Philosophy","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"AlgebraOfGraphics aims to be a declarative, question-driven language for data visualizations. This section describes its main guiding principles.","category":"page"},{"location":"philosophy/#From-question-to-plot","page":"Philosophy","title":"From question to plot","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"When analyzing a dataset, we often think in abstract, declarative terms. We have questions concerning our data, which can be answered by appropriate visualizations. For instance, we could ask whether a discrete variable :x affects the distribution of a continuous variable :y. We would then like to generate a visualization that answers this question.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In imperative programming, this would be implemented via the following steps.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Pick the dataset.\nDivide the dataset into subgroups according to the values of :x.\nCompute the density of :y on each subgroup.\nChoose a plot attribute to distinguish subgroups, for instance color.\nSelect as many distinguishable colors as there are unique values of :x.\nPlot all the density curves on top of each other.\nCreate a legend, describing how unique values of :x are associated to colors.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"While the above procedure is certainly feasible, it can introduce a cognitive overhead, especially when more variables and attributes are involved.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In a declarative framework, the user needs to express the question, and the library will take care of creating the visualization. Let us solve the above problem in a toy dataset.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\nset_aog_theme!()\nN = 1000\nx = rand([\"Class 1\", \"Class 2\", \"Class 3\", \"Class 4\"], N)\ny = @. (x == \"Class 1\") * randn() + (x == \"Class 2\") - (x == \"Class 3\") + randn()\nz = @. (x == \"Class 2\") * randn() + (x == \"Class 2\") + (x == \"Class 3\") + y + randn()\ndf = (; x, y, z)","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"plt = data(df) # declare the dataset\nplt *= density() # declare the analysis\nplt *= mapping(:y) # declare the arguments of the analysis\nplt *= mapping(color = :x) # declare the grouping and the respective visual attribute\ndraw(plt) # draw the visualization and its legend","category":"page"},{"location":"philosophy/#No-mind-reading","page":"Philosophy","title":"No mind reading","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Plotting packages requires the user to specify a large amount of settings. The temptation is then to engineer a plotting library in such a way that it would guess what the user actually wanted. AlgebraOfGraphics follows a different approach, based on algebraic manipulations of plot descriptors.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"The key intuition is that a large fraction of the \"clutter\" in a plot specification comes from repeating the same information over and over. Different layers of the same plot will share some but not all information, and the user should be able to distinguish settings that are private to a layer from those that are shared across layers.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"We achieve this goal using the distributive properties of addition and multiplication. This is best explained by example. Let us assume that we wish to visually inspect whether a discrete variable :x affects the joint distribution of two continuous variables, :y and :z.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"We would like to have two layers, one with the raw data, the other with an analysis (kernel density estimation).","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Naturally, the axes should represent the same variables (:y and :z) for both layers. Only the density layer should be a contour plot, whereas only the scatter layer should have some transparency and be grouped (according to :x) in different subplots.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"plt = data(df) *\n (\n visual(Scatter, alpha = 0.3) * mapping(layout = :x) +\n density() * visual(Contour, colormap = Reverse(:grays))\n ) *\n mapping(:y, :z)\ndraw(plt)","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"In this case, thanks to the distributive property, it is clear that the dataset and the positional arguments :y, :z are shared across layers, the transparency and the grouping are specific to the data layer, whereas the density analysis, the Contour visualization, and the choice of color map are specific to the analysis layer.","category":"page"},{"location":"philosophy/#User-defined-building-blocks","page":"Philosophy","title":"User-defined building blocks","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"It is common in data analysis tasks to \"pipe\" a sequence of operations. This became very popular in the data science field with the %>% operator in the R language, and it can allow users to seamlessly compose a sequence of tasks:","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"df %>%\n filter(Weight < 3) %>%\n group_by(Species) %>%\n summarise(avg_height = mean(Height))","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Naturally, the alternative would be to create a statement per operation and to assign each intermediate result to its own variable.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"AlgebraOfGraphics is markedly in favor of the latter approach. It is recommended that commonly used building blocks are stored in variables with meaningful names. If we often make a scatter plot with some transparency, we can create a variable transparent_scatter = visual(Scatter, alpha = 0.5) and use it consistently. If some columns of our dataset are always analyzed together, with a similar set of transformations, we can store that information as variables = mapping(variable1 => f1 => label1, variable2 => f2 => label2).","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Working over one or more datasets, the user would then create a library of building blocks to be combined with each other with * and +. These two operators allow for a much larger number of possible combinations than just sequential composition, thus fully justifying the extra characters used to name intermediate entities.","category":"page"},{"location":"philosophy/#Opinionated-defaults","page":"Philosophy","title":"Opinionated defaults","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"While users should be able to customize every aspect of their plots, it is important to note that this customization can be very time-consuming, and many subtleties can escape the attention of the casual user:","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Is the color palette colorblind-friendly?\nWould the colors be distinguishable in black and white (when printed)?\nIs the color gradient perceptually uniform?\nAre the labels and the ticks legible for readers with low vision?\nAre the spacing and typographic hierarchies respected?","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"To remedy this, AlgebraOfGraphics aims to provide solid, opinionated default settings. In particular, it uses a conservative, colorblind-friendly palette and a perceptually uniform, universally readable color map. It follows IBM guidelines to differentiate titles and labels from tick labels via font weight, while using the same typeface at a readable size.","category":"page"},{"location":"philosophy/#Wide-format-support","page":"Philosophy","title":"Wide format support","text":"","category":"section"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"Finally, AlgebraOfGraphics aims to support many different data formats. Different problems require organizing the data in different formats, and AlgebraOfGraphics should support a wide range of options.","category":"page"},{"location":"philosophy/","page":"Philosophy","title":"Philosophy","text":"This is achieved in three different ways. First, the Tables interface ensures integration with a large variety of data sources. Second, using the Wide data syntax, users can compare many different columns in the same visualization, without having to first reshape the dataset to a long format. Finally, tabular datasets are not a requirement: users may also work directly with Pre-grouped data, which are not organized as a table, but rather as a collection of (possibly multi-dimensional) arrays.","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/multiple_color_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/#Multiple-color-scales","page":"Multiple color scales","title":"Multiple color scales","text":"","category":"section"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"Continuous and discrete color scales can coexist in the same plot. This should be used sparingly, as it can make the plot harder to interpret.","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"x = range(-π, π, length=100)\ny = sin.(x)\nŷ = y .+ randn.() .* 0.1\nz = cos.(x)\nc = rand([\"a\", \"b\"], 100)\ndf = (; x, y, ŷ, z, c)\nlayers = mapping(:y, color=:z) * visual(Lines) + mapping(:ŷ => \"y\", color=:c)\nplt = data(df) * mapping(:x) * layers\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"","category":"page"},{"location":"gallery/gallery/scales/multiple_color_scales/","page":"Multiple color scales","title":"Multiple color scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"EditURL = \"analyses.jl\"","category":"page"},{"location":"generated/analyses/#Analyses","page":"Analyses","title":"Analyses","text":"","category":"section"},{"location":"generated/analyses/#Histogram","page":"Analyses","title":"Histogram","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"histogram","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.histogram","page":"Analyses","title":"AlgebraOfGraphics.histogram","text":"histogram(; bins=automatic, datalimits=automatic, closed=:left, normalization=:none)\n\nCompute a histogram.\n\nThe attribute bins can be an Integer, an AbstractVector (in particular, a range), or a Tuple of either integers or abstract vectors (useful for 2- or 3-dimensional histograms). When bins is an Integer, it denotes the approximate number of equal-width intervals used to compute the histogram. In that case, the range covered by the intervals is defined by datalimits (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. When bins is an AbstractVector, it denotes the intervals directly.\n\nclosed determines whether the the intervals are closed to the left or to the right.\n\nThe histogram can be normalized by setting normalization. Possible values are:\n\n:pdf: Normalize by sum of weights and bin sizes. Resulting histogram has norm 1 and represents a PDF.\n:density: Normalize by bin sizes only. Resulting histogram represents count density of input and does not have norm 1.\n:probability: Normalize by sum of weights only. Resulting histogram represents the fraction of probability mass for each bin and does not have norm 1.\n:none: Do not normalize.\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\nnote: Note\nNormalizations are computed withing groups. For example, in the case of normalization=:pdf, sum of weights within each group will be equal to 1.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\ndf = (x=randn(5000), y=randn(5000), z=rand([\"a\", \"b\", \"c\"], 5000))\nspecs = data(df) * mapping(:x, layout=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, dodge=:z, color=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, stack=:z, color=:z) * histogram(bins=range(-2, 2, length=15))\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) *\n mapping((:x, :z) => ((x, z) -> x + 5 * (z == \"b\")) => \"new x\", col=:z) *\n histogram(datalimits=extrema, bins=20)\ndraw(specs, facet=(linkxaxes=:minimal,))","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"data(df) * mapping(:x, :y, layout=:z) * histogram(bins=15) |> draw","category":"page"},{"location":"generated/analyses/#Density","page":"Analyses","title":"Density","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"AlgebraOfGraphics.density","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.density","page":"Analyses","title":"AlgebraOfGraphics.density","text":"density(; datalimits=automatic, kernel=automatic, bandwidth=automatic, npoints=200)\n\nFit a kernel density estimation of data.\n\nHere, datalimits specifies the range for which the density should be calculated (it defaults to the extrema of the whole data). The keyword argument datalimits can be a tuple of two values, e.g. datalimits=(0, 10), or a function to be applied group by group, e.g. datalimits=extrema. The keyword arguments kernel and bandwidth are forwarded to KernelDensity.kde. npoints is the number of points used by Makie to draw the line\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=randn(5000), y=randn(5000), z=rand([\"a\", \"b\", \"c\", \"d\"], 5000))\nspecs = data(df) * mapping(:x, layout=:z) * AlgebraOfGraphics.density(datalimits=((-2.5, 2.5),))\n\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) *\n mapping((:x, :z) => ((x, z) -> x + 5 * (z ∈ [\"b\", \"d\"])) => \"new x\", layout=:z) *\n AlgebraOfGraphics.density(datalimits=extrema)\ndraw(specs, facet=(linkxaxes=:minimal,))","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"data(df) * mapping(:x, :y, layout=:z) * AlgebraOfGraphics.density(npoints=50) |> draw","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, layout=:z) *\n AlgebraOfGraphics.density(npoints=50) * visual(Surface)\n\ndraw(specs, axis=(type=Axis3, zticks=0:0.1:0.2, limits=(nothing, nothing, (0, 0.2))))","category":"page"},{"location":"generated/analyses/#Frequency","page":"Analyses","title":"Frequency","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"frequency","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.frequency","page":"Analyses","title":"AlgebraOfGraphics.frequency","text":"frequency()\n\nCompute a frequency table of the arguments.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand([\"a\", \"b\", \"c\"], 100), z=rand([\"a\", \"b\", \"c\"], 100))\nspecs = data(df) * mapping(:x, layout=:z) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, layout=:z, color=:y, stack=:y) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, layout=:z) * frequency()\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Expectation","page":"Analyses","title":"Expectation","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"expectation","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.expectation","page":"Analyses","title":"AlgebraOfGraphics.expectation","text":"expectation()\n\nCompute the expected value of the last argument conditioned on the preceding ones.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand([\"a\", \"b\", \"c\"], 100), z=rand(100), c=rand([\"a\", \"b\", \"c\"], 100))\nspecs = data(df) * mapping(:x, :z, layout=:c) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :z, layout=:c, color=:y, dodge=:y) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"specs = data(df) * mapping(:x, :y, :z, layout=:c) * expectation()\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Linear","page":"Analyses","title":"Linear","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"linear","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.linear","page":"Analyses","title":"AlgebraOfGraphics.linear","text":"linear(; interval=automatic, level=0.95, dropcollinear=false, npoints=200)\n\nCompute a linear fit of y ~ 1 + x. An optional named mapping weights determines the weights. Use interval to specify what type of interval the shaded band should represent, for a given coverage level (the default 0.95 equates alpha = 0.05). Valid values of interval are :confidence, to delimit the uncertainty of the predicted relationship, and :prediction, to delimit estimated bounds for new data points. Use interval = nothing to only compute the line fit, without any uncertainty estimate. By default, this analysis errors on singular (collinear) data. To avoid that, it is possible to set dropcollinear=true. npoints is the number of points used by Makie to draw the shaded band.\n\nWeighted data is supported via the keyword weights (passed to mapping).\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = 1:0.05:10\na = rand(1:7, length(x))\ny = 1.2 .* x .+ a .+ 0.5 .* randn.()\ndf = (; x, y, a)\nspecs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (linear() + visual(Scatter))\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Smoothing","page":"Analyses","title":"Smoothing","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"smooth","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.smooth","page":"Analyses","title":"AlgebraOfGraphics.smooth","text":"smooth(; span=0.75, degree=2, npoints=200)\n\nFit a loess model. span is the degree of smoothing, typically in [0,1]. Smaller values result in smaller local context in fitting. degree is the polynomial degree used in the loess model. npoints is the number of points used by Makie to draw the line\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = 1:0.05:10\na = rand(1:7, length(x))\ny = sin.(x) .+ a .+ 0.1 .* randn.()\ndf = (; x, y, a)\nspecs = data(df) * mapping(:x, :y, color=:a => nonnumeric) * (smooth() + visual(Scatter))\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Contours","page":"Analyses","title":"Contours","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"contours","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.contours","page":"Analyses","title":"AlgebraOfGraphics.contours","text":"contours(; levels=5, kwargs...)\n\nCreate contour lines over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. \n\nYou can pass the number of levels as an integer or a vector of levels. The levels are calculated across the whole z data if they are specified as an integer.\n\nNote that visual(Contour) only works in a limited way with AlgebraOfGraphics since version 0.7, because the internal calculations it does are not compatible with the scale system. With visual(Contour), you can only have categorically-colored contours (for example to visualize contours of multiple categories). Alternatively, if you set the colormap attribute, you can get continuously-colored contours but the levels will not be known to AlgebraOfGraphics, so they won't be synchronized across facets and there will not be a colorbar.\n\nAll other keyword arguments are forwarded as attributes to the underlying Contour plot.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * contours(levels = 8)\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * contours(levels = 8, labels = true)\ndraw(specs)","category":"page"},{"location":"generated/analyses/#Filled-Contours","page":"Analyses","title":"Filled Contours","text":"","category":"section"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"filled_contours","category":"page"},{"location":"generated/analyses/#AlgebraOfGraphics.filled_contours","page":"Analyses","title":"AlgebraOfGraphics.filled_contours","text":"filled_contours(; bands=automatic, levels=automatic)\n\nCreate filled contours over the grid spanned over x and y by args 1 and 2 in the mapping, with height values z passed via arg 3. \n\nYou can pass either the number of bands to bands or pass a vector of levels (the boundaries of the bands) to levels, but not both. The number of bands when levels is passed is length(levels) - 1. The levels are calculated across the whole z data if the number of bands is specified. If neither levels nor bands are specified, the default is bands = 10.\n\nNote that visual(Contourf) does not work with AlgebraOfGraphics since version 0.7, because the internal binning it does is not compatible with the scale system.\n\n\n\n\n\n","category":"function"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"x = repeat(1:10, 10)\ny = repeat(11:20, inner = 10)\nz = sqrt.(x .* y)\ndf = (; x, y, z)\nspecs = data(df) * mapping(:x, :y, :z) * filled_contours(levels = 3:2:15)\ndraw(specs)","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"","category":"page"},{"location":"generated/analyses/","page":"Analyses","title":"Analyses","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/pre_grouped_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/#Pre-grouped-data","page":"Pre-grouped data","title":"Pre-grouped data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"using AlgebraOfGraphics, CairoMakie\n\n\nx = [rand(10) .+ i for i in 1:3]\ny = [rand(10) .+ i for i in 1:3]\nz = [rand(10) .+ i for i in 1:3]\nc = [\"a\", \"b\", \"c\"]\n\nm = pregrouped(x, y, color=c => (t -> \"Type \" * t ) => \"Category\")\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, (y, z) => (+) => \"sum\", color=c => (t -> \"Type \" * t ) => \"Category\")\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, [y z], color=dims(1) => renamer([\"a\", \"b\", \"c\"])) * visual(Scatter)\ndraw(m)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"m = pregrouped(x, [y z], color=[\"1\" \"2\"])\nlayers = visual(Scatter) + linear()\nfg = draw(m * layers)","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/pre_grouped_data/","page":"Pre-grouped data","title":"Pre-grouped data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/legend.jl\"","category":"page"},{"location":"gallery/gallery/customization/legend/#Legend-tweaking","page":"Legend tweaking","title":"Legend tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: Source code)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"To tweak the position and appearance of the legend, simply use the legend keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"labels = [\"a looooooong label\", \"an even loooooonger label\", \"and one more long label\"]\ndf = (x=rand(100), y=rand(100), group=rand(labels, 100))\nlayers = linear() + mapping(color=:group)\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"fg = draw(plt, legend=(position=:top, titleposition=:left, framevisible=true, padding=5))","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"To adjust the title and order of labels in a legend you can use the pair syntax.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"layers = linear() + mapping(color=:group => sorter(labels) => \"Labels\")\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"Adding a plot to a pre-existing figure with draw! will not draw the legend automatically. In this case, one must use legend! and specify the axis to which it should be added.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"The tellheight = false, tellwidth = false arguments are useful to avoid changing the dimensions of the axis.","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"makie_fig = Figure()\nax_scatter = Axis(makie_fig[1, 1])\n\ngrid = draw!(ax_scatter, plt)\n\nlegend!(makie_fig[1, 1], grid; tellheight=false, tellwidth=false, halign=:right, valign=:top)\n\nmakie_fig","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/legend/","page":"Legend tweaking","title":"Legend tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"EditURL = \"visual.jl\"","category":"page"},{"location":"generated/visual/#Visual","page":"Visual","title":"Visual","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"visual","category":"page"},{"location":"generated/visual/#AlgebraOfGraphics.visual","page":"Visual","title":"AlgebraOfGraphics.visual","text":"visual(plottype; attributes...)\n\nCreate a Layer that will cause a plot spec multiplied with it to be visualized with plot type plottype, together with optional attributes.\n\nThe available plotting functions are documented here. Refer to plotting functions using upper CamelCase for visual's first argument (e.g. visual(Scatter), visual(BarPlot)). See the documentation of each plotting function to discover the available attributes. These attributes can be passed as additional keyword arguments to visual, or as part of the mapping you define.\n\nThe visual function can in principle be used for any plotting function that is defined using the @recipe macro from Makie. AlgebraOfGraphics just needs method definitions for aesthetic_mapping, which define what arguments of the plotting function map to which visual aesthetics. And for legend support, legend_elements must be overloaded for custom recipes as well, as Makie's default legend mechanism relies on instantiated plot objects, while AlgebraOfGraphics must go by the type and attributes alone.\n\nDepending on its aesthetic_mapping, a plot type and its attributes may change certain semantics of a given data(...) * mapping(...) spec. For example, visual(BarPlot) will show mapping 1 on the x axis and 2 on the y axis, while visual(BarPlot, direction = :x) shows mapping 1 on y and 2 on x.\n\n\n\n\n\n","category":"function"},{"location":"generated/visual/#Examples","page":"Visual","title":"Examples","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\ndf = (x=randn(1000), y=randn(1000))\nplt = data(df) * mapping(:x, :y) * AlgebraOfGraphics.density(npoints=50)\ndraw(plt * visual(Heatmap)) # plot as heatmap (the default)","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"From AlgebraOfGraphics version 0.7 on, some attributes of the underlying Makie functions will not have an effect if they are controlled by scales instead. For example, continuous colors are completely controlled by color scales, so setting colormap in visual does not have an effect.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"Set the colormap in the Scale options instead.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt, scales(Color = (; colormap = :viridis))) # set a different colormap","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt * visual(Contour)) # plot as contour","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"draw(plt * visual(Contour, linewidth=2)) # plot as contour with thicker lines","category":"page"},{"location":"generated/visual/#Manual-legend-entries-via-label","page":"Visual","title":"Manual legend entries via label","text":"","category":"section"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"The legend normally contains entries for all appropriate scales used in the plot. Sometimes, however, you just want to label certain plots such that they appear in the legend without using any scale. You can achieve this by adding the label keyword to all visuals that you want to label. Layers with the same label will be combined within a legend entry.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"x = range(0, 4pi, length = 40)\nlayer1 = data((; x = x, y = cos.(x))) * mapping(:x, :y) * visual(Lines, linestyle = :dash, label = \"A cosine line\")\nlayer2 = data((; x = x, y = sin.(x) .+ 2)) * mapping(:x, :y) *\n (visual(Lines, color = (:tomato, 0.4)) + visual(Scatter, color = :tomato)) * visual(label = \"A sine line + scatter\")\ndraw(layer1 + layer2)","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"If the figure contains other scales, the legend will list the labelled group last by default. If you want to reorder, use the symbol :Label to specify the labelled group.","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"df = (; x = repeat(1:10, 3), y = cos.(1:30), group = repeat([\"A\", \"B\", \"C\"], inner = 10))\nspec1 = data(df) * mapping(:x, :y, color = :group) * visual(Lines)\n\nspec2 = data((; x = 1:10, y = cos.(1:10) .+ 2)) * mapping(:x, :y) * visual(Scatter, color = :purple, label = \"Scatter\")\n\nf = Figure()\nfg = draw!(f[1, 1], spec1 + spec2)\nlegend!(f[1, 2], fg)\nlegend!(f[1, 3], fg, order = [:Label, :Color])\n\nf","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"","category":"page"},{"location":"generated/visual/","page":"Visual","title":"Visual","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/axis.jl\"","category":"page"},{"location":"gallery/gallery/customization/axis/#Axis-tweaking","page":"Axis tweaking","title":"Axis tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"To tweak one or more axes, simply use the axis keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100))\nlayers = linear() + mapping(color=:z)\nplt = data(df) * layers * mapping(:x, :y)\ndraw(plt, axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"fg = draw(plt, axis=(aspect=1, xticks=0:0.1:1, yticks=0:0.1:1, ylabel=\"custom label\"))","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/axis/","page":"Axis tweaking","title":"Axis tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/secondary_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/#Secondary-scales","page":"Secondary scales","title":"Secondary scales","text":"","category":"section"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"By default, scales with the same aesthetic type have their categories merged. This can be undesirable if there are disjoint sets of categories, for example three different time series with two different event markers:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"nevents = 500\nngroups = 3\ntime = repeat(1:nevents, ngroups)\ny = reduce(vcat, [cumsum(randn(length(time))) for _ in 1:ngroups])\ngroup = repeat([\"A\", \"B\", \"C\"], inner = nevents)\ndf1 = (; time, y, group)\ndf2 = (; time = [30, 79, 250, 400], event = [\"X\", \"Y\", \"Y\", \"X\"])\n\nspec_a = data(df1) * mapping(:time, :y, color = :group) * visual(Lines)\nspec_b = data(df2) * mapping(:time, color = :event) * visual(VLines)\n\ndraw(spec_a + spec_b)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"By assigning an arbitrary scale identifier to one of the color mappings, we can split the two scales apart and receive a separate legend for both:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"split_spec = spec_a + spec_b * mapping(color = :event => scale(:secondary))\ndraw(split_spec)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"Each scale can then be modified separately in the scales configuration. For example, it is not desired that both scales use the same color palette:","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"fg = draw(split_spec, scales(secondary = (;\n palette = [:gray70, :gray30]\n)))","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"If you don't want to have separate legend groups, you can merge them using the order keyword in the legend config.","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"draw(\n split_spec,\n scales(\n secondary = (; palette = [:gray70, :gray30])\n );\n legend = (; order = [[:Color, :secondary] => \"Legend\"])\n)","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"","category":"page"},{"location":"gallery/gallery/scales/secondary_scales/","page":"Secondary scales","title":"Secondary scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/figure.jl\"","category":"page"},{"location":"gallery/gallery/customization/figure/#Figure-tweaking","page":"Figure tweaking","title":"Figure tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"You can pass attributes to the underlying Makie.Figure using the figure keyword of draw.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100), c=rand([\"a\", \"b\"], 100))\nxyc = data(df) * mapping(:x, :y, layout=:c)\nlayers = linear() + mapping(color=:z)\nplt = xyc * layers\ndraw(\n plt,\n figure = (;\n figure_padding = 10,\n backgroundcolor = :gray80,\n size = (800, 400)\n )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"You can also add a figure title, subtitle and footnotes.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"fg = draw(\n plt,\n figure = (;\n figure_padding = 10,\n backgroundcolor = :gray80,\n size = (800, 400),\n title = \"Figure title\",\n subtitle = \"Some subtitle below the figure title\",\n footnotes = [\n rich(superscript(\"1\"), \"First footnote\"),\n rich(superscript(\"2\"), \"Second footnote\"),\n ]\n )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"The appearance of these elements can be modified further, for all options check the draw function.","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"draw(\n plt,\n figure = (;\n figure_padding = 10,\n backgroundcolor = :gray80,\n size = (800, 400),\n title = \"Figure title\",\n subtitle = \"Some subtitle below the figure title\",\n footnotes = [\n rich(superscript(\"1\"), \"First footnote\"),\n rich(superscript(\"2\"), \"Second footnote\"),\n ],\n titlecolor = :firebrick,\n titlealign = :right,\n footnotefont = :bold,\n )\n)","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/figure/","page":"Figure tweaking","title":"Figure tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/mapping/#Mapping","page":"Mapping","title":"Mapping","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Mappings determine how the data is translated into a plot. For example, this mapping maps columns weight and height to positional arguments 1 and 2, and age to the markersize attribute of the Scatter plotting function:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"mapping(:weight, :height, markersize = :age)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"mapping","category":"page"},{"location":"layers/mapping/#AlgebraOfGraphics.mapping","page":"Mapping","title":"AlgebraOfGraphics.mapping","text":"mapping(positional...; named...)\n\nCreate a Layer with positional and named selectors. These selectors will be translated into input data for the Makie plotting function or AlgebraOfGraphics analysis that is chosen to visualize the Layer.\n\nA Layer created with mapping does not have a data source by default, you can add one by multiplying with the output of the data function.\n\nThe positional and named selectors of mapping are converted to actual input data for the plotting function that will be selected via visual. The translation from selector to data differs according to the data source.\n\nTabular data\n\nWhen a mapping is combined with a data(tabular) where tabular is some Tables.jl-compatible object, each argument will be interpreted as a column selector. Additionally, it's allowed to specify columns outside of the dataset directly by wrapping the values in direct. The values can either be vectors that have to match the number of rows from the tabular data, or scalars that will be expanded as if they were a column filled with the same value.\n\nmapping(\n :x, # column named \"x\"\n \"a column\"; # column named \"a column\"\n color = 1, # first column\n marker = direct(\"abc\"), # a new column filled with the string \"abc\"\n linestyle = direct(1:3), # a new column, length must match the table\n)\n\nnothing\n\nIf no data is set, each entry of mapping should be an AbstractVector that specifies a column of data directly. Scalars like strings for example will be expanded as if they were a column filled with the same value. This is useful when a legend should be shown, but there's only one group.\n\nmapping(\n 1:3, # a column with values 1 to 3\n [4, 5, 6], # a column with values 4 to 6 \n color = \"group 1\", # a column with repeated value \"group 1\" \n)\n\nPregrouped\n\nWith data(Pregrouped()) * mapping(...) or the shortcut pregrouped(...), each element in mapping specifies input data directly, like with nothing. However, in this mode, data should be passed in pregrouped. Categorical variables should come as a vector of categories, while numerical variables should come as a vector of vectors of values, with as many inner vectors as there are groups in the categorical variables.\n\npregrouped(\n [[1, 2, 3], [4, 5]], # two grouped vectors, of length 3 and 2\n color = [\"A\", \"B\"] # a vector with two categorical group values\n)\n\n\n\n\n\n","category":"function"},{"location":"layers/mapping/#Aesthetics","page":"Mapping","title":"Aesthetics","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The structure of a mapping is always directly tied to the signature of the plotting function (or analysis) that it is being connected with. What visual aspects of the plot the positional or keyword arguments affect depends on the plotting function in use.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"To be used with AlgebraOfGraphics, a plotting function has to add a declaration which aesthetics (like X, Y, Color, MarkerSize, LineStyle) its arguments map to. This mechanism allows AlgebraOfGraphics to correctly convert the raw input data into visual attributes for each plotting function and to correctly create and label axes, colorbars and legends.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Aesthetics can also change depending on attributes passed to visual.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"For example, for a BarPlot, args 1 and 2 correspond to the X and Y aesthetics by default. But if you change the direction in the visual, then axis labels shift accordingly because the aesthetic mapping has changed to 1 = Y, 2 = X:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])\nm = mapping(:name, :height_meters)\nspec1 = data(df) * m * visual(BarPlot)\nspec2 = data(df) * m * visual(BarPlot, direction = :x)\n\nf = Figure()\ndraw!(f[1, 1], spec1)\ndraw!(f[1, 2], spec2)\nf","category":"page"},{"location":"layers/mapping/#Hardcoded-aesthetics","page":"Mapping","title":"Hardcoded aesthetics","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Most aesthetics are tied to specific attributes of plot types, for example like AesColor to strokecolor of Scatter. There are a few aesthetics, however, which are hardcoded to belong to certain mapping keywords independent of the plot type in use.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"These are layout, row and col for facetting, group for creating a separate plot for each group (like separate lines instead of one long line) and dodge_x and dodge_y for dodging.","category":"page"},{"location":"layers/mapping/#Dodging","page":"Mapping","title":"Dodging","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Dodging refers to the shifting of plots on a (usually categorical) scale depending on the group they belong to. It is used to avoid overlaps. Some plot types, like BarPlot, have their own dodge keyword because their dodging logic additionally needs to transform the visual elements (for example, dodging a bar plot makes thinner bars). For all other plot types, you can use the generic dodge_x and dodge_y keywords.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"They work by shifting each categorical group by some value that depends on the chosen \"dodge width\". The dodge width refers to the width that all dodged elements in a group add up to at a given point. Some plot types have an inherent width, like barplots. Others have no width, like scatters or errorbars. For those plot types that have no width to use for dodging, you have to specify one manually in scales.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Here's an example of a manual width selection:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (\n x = repeat(1:10, inner = 2),\n y = cos.(range(0, 2pi, length = 20)),\n ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt = data(df) * (\n mapping(:x, :y, dodge_x = :dodge, color = :dodge) * visual(Scatter) +\n mapping(:x, :ylow, :yhigh, dodge_x = :dodge, color = :dodge) * visual(Rangebars)\n)\ndraw!(f[1, 1], plt, scales(DodgeX = (; width = 1)), axis = (; title = \"width = 1\"))\ndraw!(f[1, 2], plt, scales(DodgeX = (; width = 0.75)), axis = (; title = \"width = 0.75\"))\ndraw!(f[2, 1], plt, scales(DodgeX = (; width = 0.5)), axis = (; title = \"width = 0.5\"))\ndraw!(f[2, 2], plt, scales(DodgeX = (; width = 0.25)), axis = (; title = \"width = 0.25\"))\nf","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"A common scenario is plotting errorbars on top of barplots. In this case, AlgebraOfGraphics can detect the inherent dodging width of the barplots and adjust accordingly for the errorbars. Note in this example how choosing a manual dodging width only applies to the errorbars (because the barplot plot type handles this internally) and potentially leads to a misalignment between the different plot elements:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (\n x = repeat(1:10, inner = 2),\n y = cos.(range(0, 2pi, length = 20)),\n ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt = data(df) * (\n mapping(:x, :y, dodge = :dodge, color = :dodge) * visual(BarPlot) +\n mapping(:x, :ylow, :yhigh, dodge_x = :dodge) * visual(Rangebars)\n)\ndraw!(f[1, 1], plt, axis = (; title = \"No width specified, auto-determined by AlgebraOfGraphics\"))\ndraw!(f[2, 1], plt, scales(DodgeX = (; width = 0.25)), axis = (; title = \"Manually specifying width = 0.25 leads to a mismatch\"))\nf","category":"page"},{"location":"layers/mapping/#Pair-syntax","page":"Mapping","title":"Pair syntax","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The Pair operator => can be used for three different purposes within mapping:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"renaming columns\ntransforming columns by row\nmapping data to a custom scale","category":"page"},{"location":"layers/mapping/#Renaming-columns","page":"Mapping","title":"Renaming columns","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndata((; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])) *\n mapping(:name => \"Name\", :height_meters => \"Height (m)\") *\n visual(BarPlot) |> draw ","category":"page"},{"location":"layers/mapping/#Transforming-columns","page":"Mapping","title":"Transforming columns","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"If a Function is paired to the column selector, it is applied by row to the data. Often, you will want to also assign a new name that fits the transformed data, in which case you can use the three-element column => transformation => name syntax:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndata((; name = [\"Anna\", \"Beatrix\", \"Claire\"], height_meters = [1.55, 1.76, 1.63])) *\n mapping(:name => (n -> n[1] * \".\"), :height_meters => (x -> x * 100) => \"Height (cm)\") *\n visual(BarPlot) |> draw","category":"page"},{"location":"layers/mapping/#Row-by-row-versus-whole-column-operations","page":"Mapping","title":"Row-by-row versus whole-column operations","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The pair syntax acts row by row, unlike, e.g., DataFrames.transform. This has several advantages.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Simpler for the user in most cases.\nLess error prone especially\nwith grouped data (should a column operation apply to each group or the whole dataset?)\nwhen several datasets are used","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Naturally, this also incurs some downsides, as whole-column operations, such as z-score standardization, are not supported: they should be done by adding a new column to the underlying dataset beforehand.","category":"page"},{"location":"layers/mapping/#Functions-of-several-arguments","page":"Mapping","title":"Functions of several arguments","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"In the case of functions of several arguments, such as isequal, the input variables must be passed as a Tuple.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"accuracy = (:species, :predicted_species) => isequal => \"accuracy\"","category":"page"},{"location":"layers/mapping/#Helper-functions","page":"Mapping","title":"Helper functions","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Some helper functions are provided, which can be used within the pair syntax to either rename and reorder unique values of a categorical column on the fly or to signal whether a numerical column should be treated as categorical.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"The complete API of helper functions is available at Mapping helpers, but here are a few examples:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"# column `train` has two unique values, `true` and `false`\n:train => renamer([true => \"training\", false => \"testing\"]) => \"Dataset\"\n# column `price` has three unique values, `\"low\"`, `\"medium\"`, and `\"high\"`\n:price => sorter([\"low\", \"medium\", \"high\"])\n# column `age` is expressed in integers and we want to treat it as categorical\n:age => nonnumeric\n# column `labels` is expressed in strings and we do not want to treat it as categorical\n:labels => verbatim\n# wrap categorical values to signal that the order from the data source should be respected\n:weight => presorted","category":"page"},{"location":"layers/mapping/#Custom-scales","page":"Mapping","title":"Custom scales","text":"","category":"section"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"All columns mapped to the same aesthetic type are represented using the same scale by default. This is evident if you plot two different datasets with two different plot types.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"In the following example, both Scatter and HLines use the Color aesthetic, Scatter for the strokecolor keyword and HLines for color. A single merged legend is rendered for both, which does not have a title because it derives from two differently named columns.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf_a = (; x = 1:9, y = [1, 2, 3, 5, 6, 7, 9, 10, 11], group = repeat([\"A\", \"B\", \"C\"], inner = 3))\n\nspec1 = data(df_a) * mapping(:x, :y, strokecolor = :group) * visual(Scatter, color = :transparent, strokewidth = 3, markersize = 15)\n\ndf_b = (; y = [4, 8], threshold = [\"first\", \"second\"])\n\nspec2 = data(df_b) * mapping(:y, color = :threshold) * visual(HLines)\n\ndraw(spec1 + spec2)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"If we want to have separate legends for both, we can assign a custom scale identifier to either the strokecolor or the color mapping. The name can be chosen freely, it serves only to disambiguate.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"spec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(spec1 + spec2_custom_scale)","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"Each scale can be customized further by passing configuration options via scales as the second argument of the draw function. More information on scale options can be found under Scale options.","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"As an example, we can pass separate colormaps using the palette keyword:","category":"page"},{"location":"layers/mapping/","page":"Mapping","title":"Mapping","text":"spec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(\n spec1 + spec2_custom_scale,\n scales(\n Color = (; palette = [:red, :green, :blue]),\n color2 = (; palette = [:gray30, :gray80]),\n )\n)","category":"page"},{"location":"changelog/#Changelog","page":"Changelog","title":"Changelog","text":"","category":"section"},{"location":"changelog/#Unreleased","page":"Changelog","title":"Unreleased","text":"","category":"section"},{"location":"changelog/#v0.8.11-2024-09-25","page":"Changelog","title":"v0.8.11 - 2024-09-25","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed lexicographic natural sorting of tuples (this would fall back to default sort order before) #568.","category":"page"},{"location":"changelog/#v0.8.10-2024-09-24","page":"Changelog","title":"v0.8.10 - 2024-09-24","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed markercolor in ScatterLines legends when it did not match color #567.","category":"page"},{"location":"changelog/#v0.8.9-2024-09-24","page":"Changelog","title":"v0.8.9 - 2024-09-24","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added ability to include layers in the legend without using scales by adding visual(label = \"some label\") #565.","category":"page"},{"location":"changelog/#v0.8.8-2024-09-17","page":"Changelog","title":"v0.8.8 - 2024-09-17","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed aesthetics of errorbar so that x and y stay labelled correctly when using direction = :x #560.\nAdded ability to specify title, subtitle and footnotes plus settings in the draw function #556.\nAdded dodge_x and dodge_y keywords to mapping that allow to dodge any plot types that have AesX or AesY data #558.","category":"page"},{"location":"changelog/#v0.8.7-2024-09-06","page":"Changelog","title":"v0.8.7 - 2024-09-06","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added ability to return ProcessedLayers from transformations, thereby enabling multi-layer transformations, such as scatter plus errorbars #549.\nFixed bug where mergesorted applied on string vectors used isless instead of natural sort #553.","category":"page"},{"location":"changelog/#v0.8.6-2024-09-02","page":"Changelog","title":"v0.8.6 - 2024-09-02","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added bar_labels to BarPlot's aesthetic mapping #544.\nAdded ability to hide legend or colorbar by passing, e.g., legend = (; show = false) to draw #547.","category":"page"},{"location":"changelog/#v0.8.5-2024-08-27","page":"Changelog","title":"v0.8.5 - 2024-08-27","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added presorted helper function to keep categorical data in the order encountered in the source table, instead of sorting it alphabetically #529.\nAdded from_continuous helper function which allows to sample continuous colormaps evenly to use them as categorical palettes without having to specify how many categories there are #541.","category":"page"},{"location":"changelog/#v0.8.4-2024-08-26","page":"Changelog","title":"v0.8.4 - 2024-08-26","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added fillto to BarPlot aesthetics #535.\nFixed bug when giving datalimits of density as a (low, high) tuple #536.\nFixed bug where facet-local continuous scale limits were used instead of the globally merged ones, possibly leading to mismatches between data and legend #539.","category":"page"},{"location":"changelog/#v0.8.3-2024-08-23","page":"Changelog","title":"v0.8.3 - 2024-08-23","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Fixed incorrect x/y axis assignment for the violin plot type #528.","category":"page"},{"location":"changelog/#v0.8.2-2024-08-21","page":"Changelog","title":"v0.8.2 - 2024-08-21","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Enable use of LaTeXStrings and rich text in renamer #525.\nFixed widths of boxplots with color groupings #524.","category":"page"},{"location":"changelog/#v0.8.1-2024-08-20","page":"Changelog","title":"v0.8.1 - 2024-08-20","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added back support for Hist, CrossBar, ECDFPlot and Density #522.","category":"page"},{"location":"changelog/#v0.8.0-2024-07-26","page":"Changelog","title":"v0.8.0 - 2024-07-26","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Columns with element types of Union{Missing,T} are not treated as categorical by default anymore, instead T decides if data is seen as categorical, continuous or geometrical. If you relied on numerical vectors with missings being treated as categorical, you can use :columnname => nonnumeric in the mapping instead.\nBreaking: AbstractString categories are now sorted with natural sort order by default. This means that where you got [\"1\", \"10\", \"2\"] before, you now get [\"1\", \"2\", \"10\"]. You can use sorter, the categories keyword or categorical arrays to sort your data differently if needed.","category":"page"},{"location":"changelog/#v0.7.0-2024-07-16","page":"Changelog","title":"v0.7.0 - 2024-07-16","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: The palette keyword of draw linking palettes to keyword arguments was removed. Instead, palettes need to be passed to specific scales like draw(..., scales(Color = (; palette = :Set1_3)))\nBreaking: All recipes need to have the new function aesthetic_mapping defined for all sets of positional arguments that should be supported, as can be seen in src/aesthetics.jl. This breaks usage of all custom recipes. Additionally, not all Makie plots have been ported to the new system yet. If you encounter missing plots, or missing attributes of already ported plots, please open an issue.\nBreaking: All custom recipes that should be displayed in a legend, need to have legend_elements(P, attributes, scale_args) defined as can be seen in src/guides/legend.jl. AlgebraOfGraphics cannot use the same default mechanism as Makie, which can create a legend from an existing plot, because AlgebraOfGraphics needs to create the legend before the plot is instantiated.\nBreaking: Pregrouped data cannot be passed anymore to the plain mapping(...) without any data(tabular). Instead, you should use pregrouped(...) which is a shortcut for data(Pregrouped()) * mapping(...).\nBreaking: Contour and Contourf generally do not work anymore with visual(). Instead, the contours() and filled_contours() analyses should be used. Contour can still be used with categorical colors, but not with continuous ones.\nBreaking: All colormap properties for continuous color scales need to be passed via scales now, and not through visual. This is to have central control over the scale as it can be used by multiple visuals simultaneously.\nHorizontal barplots, violins, errorbars, rangebars and other plot types that have two different orientations work correctly now. Axis labels switch accordingly when the orientation is changed.\nPlotting functions whose positional arguments don't correspond to X, Y, Z work correctly now. For example, HLines (1 => Y) or rangebars (1 => X, 2 => Y, 3 => Y).\nIt is possible to add categories beyond those present in the data with the categories keyword within a scale's settings. It is also possible to reorder or otherwise transform the existing categories by passing a function to categories.\nThe supported attributes are not limited anymore to a specific set of names, for example, strokecolor can work the same as color did before, and the two can share a scale via their shared aesthetic type.\nThere can be multiple scales of the same aesthetic now. This allows to have separate legends for different plot types using the same aesthetics. Scale separation works by pairing a variable in mapping with a scale(id_symbol).\nLegend entries can be reordered using the legend = (; order = ...) option in draw. Specific scales can opt out of the legend by passing legend = false in scales.\nLabels can now be anything that Makie supports, primarily Strings, LaTeXStrings or rich text.\nLegend elements now usually reflect all attributes set in their corresponding visual.\nSimple column vectors of data can now be passed directly to mapping without using data first. Additionally, scalar values are accepted as a shortcut for columns with the same repeated value.\nColumns from outside a table source in data can now be passed to mapping by wrapping them in the direct function. Scalar values are accepted as a shortcut for columns with the same repeated value. For example, to create a label for columns x and y from a dataframe passed to data, one could now do mapping(:x, :y, color = direct(\"label\")) without having to create a column full of \"label\" strings first.\nThe numbers at which categorical values are plotted on x and y axis can now be changed via scales(X = (; palette = [1, 2, 4])) or similar.\nContinuous marker size scales can now be shown in the legend. Numerical values are proportional to area and not diameter now, which makes more sense with respect to human perception. The min and max marker size can be set using the sizerange property for the respective scale in scales.","category":"page"},{"location":"changelog/#v0.6.11-2022-08-08","page":"Changelog","title":"v0.6.11 - 2022-08-08","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added paginate for pagination of large facet plots. ","category":"page"},{"location":"changelog/#v0.6.8-2022-06-14","page":"Changelog","title":"v0.6.8 - 2022-06-14","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Added choropleth recipe to supersede geodata for geographical data.","category":"page"},{"location":"changelog/#v0.6.1-2022-01-28","page":"Changelog","title":"v0.6.1 - 2022-01-28","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Support level in linear analysis for confidence interval.\nReplaced tuples and named tuples in Layer and Entry with dictionaries from Dictionaries.jl.\nSplit internal Entry type into ProcessedLayer (to be used for analyses) and Entry (to be used for plotting).","category":"page"},{"location":"changelog/#v0.6.0-2021-10-24","page":"Changelog","title":"v0.6.0 - 2021-10-24","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Default axis linking behavior has changed: now only axes corresponding to the same variable are linked. For consistency with row/col, layout will hide decorations of linked axes and span axis labels if appropriate.\nCustomizable legend and colorbar position and look.\nCustomizable axis linking behavior.","category":"page"},{"location":"changelog/#v0.5-2021-08-05","page":"Changelog","title":"v0.5 - 2021-08-05","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Axis(ae) has been replaced by ae.axis.\nBreaking: Legend(fg) has been replaced by legend!(fg) and colorbar!(fg).\nlegend! and colorbar! API allows for custom legend placement.","category":"page"},{"location":"changelog/#v0.4-2021-05-21","page":"Changelog","title":"v0.4 - 2021-05-21","text":"","category":"section"},{"location":"changelog/","page":"Changelog","title":"Changelog","text":"Breaking: Removed deprecations for style and spec (now only mapping and visual are allowed).\nBreaking: Analyses now require parentheses (i.e. linear() instead of linear).\nBreaking: Rename layout_x and layout_y to col and row.\nBreaking: Rename wts keyword argument to weights.\nBreaking: categorical has been replaced by nonnumeric.","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/density_plots.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/#Density-plots","page":"Density plots","title":"Density plots","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"df = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c) * density(bandwidth=0.5) * visual(orientation=:vertical)\n\"Not yet supported\" # hide","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"\"Not yet supported\"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/density_plots/","page":"Density plots","title":"Density plots","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"API/types/#Types","page":"Types","title":"Types","text":"","category":"section"},{"location":"API/types/","page":"Types","title":"Types","text":"AlgebraOfGraphics.AbstractDrawable\nAlgebraOfGraphics.AbstractAlgebraic\nAlgebraOfGraphics.Layer\nAlgebraOfGraphics.Layers\nAlgebraOfGraphics.ProcessedLayer\nAlgebraOfGraphics.ProcessedLayers\nAlgebraOfGraphics.Entry\nAlgebraOfGraphics.AxisEntries","category":"page"},{"location":"API/types/#AlgebraOfGraphics.AbstractDrawable","page":"Types","title":"AlgebraOfGraphics.AbstractDrawable","text":"AbstractDrawable\n\nAbstract type encoding objects that can be drawn via AlgebraOfGraphics.draw.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.AbstractAlgebraic","page":"Types","title":"AlgebraOfGraphics.AbstractAlgebraic","text":"AbstractAlgebraic <: AbstractDrawable\n\nAbstract type encoding objects that can be combined together using + and *.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Layer","page":"Types","title":"AlgebraOfGraphics.Layer","text":"Layer(transformation, data, positional::AbstractVector, named::AbstractDictionary)\n\nAlgebraic object encoding a single layer of a visualization. It is composed of a dataset, positional and named arguments, as well as a transformation to be applied to those. Layer objects can be multiplied, yielding a novel Layer object, or added, yielding a AlgebraOfGraphics.Layers object.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Layers","page":"Types","title":"AlgebraOfGraphics.Layers","text":"Layers(layers::Vector{Layer})\n\nAlgebraic object encoding a list of AlgebraOfGraphics.Layer objects. Layers objects can be added or multiplied, yielding a novel Layers object.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.ProcessedLayer","page":"Types","title":"AlgebraOfGraphics.ProcessedLayer","text":"ProcessedLayer(l::Layer)\n\nProcess a Layer and return the resulting ProcessedLayer.\n\nNote that this method should not be used anymore as processing a Layer can now potentially return multiple ProcessedLayer objects. Therefore, you should use the plural form ProcessedLayers(layer).\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.ProcessedLayers","page":"Types","title":"AlgebraOfGraphics.ProcessedLayers","text":"ProcessedLayers(layers::Vector{ProcessedLayer})\n\nObject encoding a list of AlgebraOfGraphics.ProcessedLayer objects. ProcessedLayers objects are the output of the processing pipeline and can be drawn without further processing.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.Entry","page":"Types","title":"AlgebraOfGraphics.Entry","text":"Entry(plottype::PlotType, positional::Arguments, named::NamedArguments)\n\nDefine plottype as well as positional and named arguments for a single plot.\n\n\n\n\n\n","category":"type"},{"location":"API/types/#AlgebraOfGraphics.AxisEntries","page":"Types","title":"AlgebraOfGraphics.AxisEntries","text":"AxisEntries(axis::Union{Axis, Nothing}, entries::Vector{Entry}, categoricalscales, continuousscales)\n\nDefine all ingredients to make plots on an axis. Each categorical scale should be a CategoricalScale, and each continuous scale should be a ContinuousScale.\n\n\n\n\n\n","category":"type"},{"location":"API/recipes/#Recipes","page":"Recipes","title":"Recipes","text":"","category":"section"},{"location":"API/recipes/","page":"Recipes","title":"Recipes","text":"choropleth\nlinesfill","category":"page"},{"location":"API/recipes/#AlgebraOfGraphics.choropleth","page":"Recipes","title":"AlgebraOfGraphics.choropleth","text":"choropleth(geometries; transformation, attributes...)\n\nChoropleth map, where regions are defined by geometries. Use transformation to transform coordinates (see Proj.jl for more information).\n\nwarning: Warning\nThe transformation keyword argument is experimental and could be deprecated (even in a non-breaking release) in favor of a different syntax.\n\nAttributes\n\nAvailable attributes and their defaults for Plot{AlgebraOfGraphics.choropleth} are: \n\n alpha 1.0\n clip_planes MakieCore.Automatic()\n color :gray25\n colormap :batlow\n colorrange MakieCore.Automatic()\n colorscale identity\n cycle [:color => :patchcolor]\n depth_shift 0.0f0\n highclip MakieCore.Automatic()\n inspectable true\n inspector_clear MakieCore.Automatic()\n inspector_hover MakieCore.Automatic()\n inspector_label MakieCore.Automatic()\n joinstyle :miter\n linecap :butt\n linestyle \"nothing\"\n lowclip MakieCore.Automatic()\n miter_limit 1.0471975511965976\n nan_color :transparent\n overdraw false\n shading NoShading\n space :data\n ssao false\n stroke_depth_shift -1.0f-5\n strokecolor :black\n strokecolormap :batlow\n strokewidth 0\n transparency false\n visible true\n\n\n\n\n\n","category":"function"},{"location":"API/recipes/#AlgebraOfGraphics.linesfill","page":"Recipes","title":"AlgebraOfGraphics.linesfill","text":"linesfill(xs, ys; lower, upper, attributes...)\n\nLine plot with a shaded area between lower and upper. If lower and upper are not given, shaded area is between 0 and ys.\n\nAttributes\n\nAvailable attributes and their defaults for Plot{AlgebraOfGraphics.linesfill} are: \n\n color :gray25\n colormap :batlow\n colorrange MakieCore.Automatic()\n fillalpha 0.15\n linestyle \"nothing\"\n linewidth 1.5\n lower MakieCore.Automatic()\n upper MakieCore.Automatic()\n\n\n\n\n\n","category":"function"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/no_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/#Using-mapping-without-tabular-data","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"Sometimes it's easier to specify columnar data directly within mapping rather than first storing it in some tabular data source and accessing it by column name. Note that you can also use scalar values which will be treated like columns with repeated elements. In the example below, we specify color = \"marker\" instead of the more verbose color = fill(\"marker\", 3).","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"using AlgebraOfGraphics, CairoMakie\n\nx = 1:100\ny = sin.(range(0, 2pi, length = 100))\n\nplt = mapping(x, y, color = repeat([\"high\", \"low\"], inner = 50)) *\n visual(Lines) +\n mapping([20, 28, 51], color = \"marker\" => scale(:secondary)) *\n visual(VLines, linestyle = :dash)\n\nfg = draw(plt, scales(secondary = (; palette = [:gray80])))","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/no_data/","page":"Using mapping without tabular data","title":"Using mapping without tabular data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/data/#Data","page":"Data","title":"Data","text":"","category":"section"},{"location":"layers/data/","page":"Data","title":"Data","text":"data","category":"page"},{"location":"layers/data/#AlgebraOfGraphics.data","page":"Data","title":"AlgebraOfGraphics.data","text":"data(table)\n\nCreate a Layer with its data field set to a table-like object.\n\nThere are no type restrictions on this object, as long as it respects the Tables interface. In particular, any one of these formats should work out of the box.\n\nTo create a fully specified layer, the layer created with data needs to be multiplied with the output of mapping.\n\nspec = data(...) * mapping(...)\n\n\n\n\n\n","category":"function"},{"location":"layers/data/","page":"Data","title":"Data","text":"using AlgebraOfGraphics\ndf = (a = rand(10), b = rand(10))\ndata(df)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/time_series.jl\"","category":"page"},{"location":"gallery/gallery/applications/time_series/#Time-series","page":"Time series","title":"Time series","text":"","category":"section"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"using AlgebraOfGraphics, CairoMakie\nusing Dates\n\nx = today() - Year(1) : Day(1) : today()\ny = cumsum(randn(length(x)))\nz = cumsum(randn(length(x)))\ndf = (; x, y, z)\nlabels = [\"series 1\", \"series 2\", \"series 3\", \"series 4\", \"series 5\"]\nplt = data(df) *\n mapping(:x, [:y, :z] .=> \"value\", color=dims(1) => renamer(labels) => \"series \") *\n visual(Lines)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"x = now() - Hour(6) : Minute(1) : now()\ny = cumsum(randn(length(x)))\nz = cumsum(randn(length(x)))\ndf = (; x, y, z)\nplt = data(df) *\n mapping(:x, [:y, :z] .=> \"value\", color=dims(1) => renamer(labels) =>\"series \") *\n visual(Lines)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"dates = Date(2022, 1, 1):Day(1):Date(2022, 1, 31)\ntrend = cumsum(randn(31))\ndf = map(1:2000) do _\n idx = rand(1:31)\n date = dates[idx]\n observation = trend[idx] + 2 * rand()\n return (; date, observation)\nend\nplt = data(df) * mapping(:date, :observation) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"","category":"page"},{"location":"gallery/gallery/applications/time_series/","page":"Time series","title":"Time series","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/draw/#Drawing-Layers","page":"Drawing Layers","title":"Drawing Layers","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers object can be plotted using the functions draw or draw!.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Whereas draw automatically adds colorbar and legend, draw! does not, as it would be hard to infer a good default placement that works in all scenarios.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Colorbar and legend, should they be necessary, can be added separately with the colorbar! and legend! helper functions. See also Nested layouts for a complex example.","category":"page"},{"location":"layers/draw/#Scale-options","page":"Drawing Layers","title":"Scale options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"All properties that decide how scales are visualized can be modified by passing scale options (using the scales function) as the second argument of draw . The properties that are accepted differ depending on the scale aesthetic type (for example Color, Marker, LineStyle) and whether the scale is categorical or continuous.","category":"page"},{"location":"layers/draw/#Categorical-scale-options","page":"Drawing Layers","title":"Categorical scale options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"All categorical scales take the same properties, independent of aesthetic type.","category":"page"},{"location":"layers/draw/#palette","page":"Drawing Layers","title":"palette","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The palette decides which attribute values are passed to Makie's plotting functions for each categorical value in the scale.","category":"page"},{"location":"layers/draw/#Color","page":"Drawing Layers","title":"Color","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A Symbol is converted to a colormap with Makie.to_colormap. ","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n mapping(:x, :y, color = :z) *\n visual(BarPlot)\ndraw(spec, scales(Color = (; palette = :tab10)))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"It's also possible to directly specify a vector of colors, each of which Makie.to_color can handle:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\nusing CairoMakie.Colors: RGB, RGBA, Gray, HSV\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n mapping(:x, :y, color = :z) *\n visual(BarPlot)\ndraw(spec, scales(Color = (; palette = [:red, :green, :blue, RGB(1, 0, 1), RGB(1, 1, 0), \"#abcff0\", \"#c88cbccc\", HSV(0.9, 0.3, 0.7), RGBA(0.7, 0.9, 0.6, 0.5), Gray(0.5)])))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"If you want to use a continuous colormap for categorical data, you can use the from_continuous helper function. It automatically takes care that the continuous colormap is sampled evenly from start to end depending on the number of categories. Any colormap that Makie understands can be passed, including named colormaps such as :viridis.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"This example shows the difference in behavior when the two-element colormap [:red, :blue] is used with or without from_continuous:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n mapping(:x, :y, color = :z) *\n visual(BarPlot)\n\nf = Figure()\nfg1 = draw!(f[1, 1], spec, scales(Color = (; palette = [:red, :blue])))\nlegend!(f[1, 2], fg1)\nfg2 = draw!(f[1, 3], spec, scales(Color = (; palette = from_continuous([:red, :blue]))))\nlegend!(f[1, 4], fg2)\nf","category":"page"},{"location":"layers/draw/#Marker","page":"Drawing Layers","title":"Marker","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A vector of values that Makie.to_spritemarker can handle.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 'A':'J')) *\n mapping(:x, :y, marker = :z) *\n visual(Scatter, markersize = 20)\n\ndraw(\n spec,\n scales(\n Marker = (; palette = [:rect, :circle, :utriangle, :dtriangle, :diamond, :hline, :vline, :star5, :star6, :hexagon])\n )\n)","category":"page"},{"location":"layers/draw/#LineStyle","page":"Drawing Layers","title":"LineStyle","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"A vector of values that Makie.to_linestyle can handle.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = repeat('A':'E', inner = 2))) *\n mapping(:x, :y, linestyle = :z) *\n visual(Lines, linewidth = 2)\n\ndraw(spec, scales(\n LineStyle = (; palette = [:solid, :dash, :dot, (:dot, :loose), Linestyle([0, 1, 2, 3, 4, 8])])\n))","category":"page"},{"location":"layers/draw/#X-and-Y","page":"Drawing Layers","title":"X & Y","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The \"palette\" values for X and Y axes are by default simply the numbers from 1 to N, the number of categories. In some circumstances, it might be useful to change these values, for example to visualize that one category is different than others. The palette values are normally assigned category-by-category in the sorted order, or in the order provided manually through the categories keyword. However, if you pass a vector of values, you can always use the category => value pair option to assign a specific category directly to a value, while the others cycle. Here, we do this with \"Unknown\" as it would otherwise be sorted before \"X\".","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (; group = [\"A\", \"B\", \"C\", \"X\", \"Y\", \"Unknown\"], count = [45, 10, 20, 32, 54, 72])\n\nspec = data(df) * mapping(:group, :count) * visual(BarPlot)\n\ndraw(spec, scales(X = (; palette = [1, 2, 3, 5, 6, \"Unknown\" => 8])))","category":"page"},{"location":"layers/draw/#Layout","page":"Drawing Layers","title":"Layout","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Normally, with the Layout aesthetic, rows wrap automatically such that an approximately square distribution of facets is attained. You can overwrite these values, however, to place axes at manually chosen positions:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (;\n group = repeat([\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\"], inner = 20),\n x = randn(160),\n y = randn(160)\n)\n\nspec = data(df) * mapping(:x, :y, layout = :group) * visual(Scatter)\n\nclockwise = [(1, 1), (1, 2), (1, 3), (2, 3), (3, 3), (3, 2), (3, 1), (2, 1)]\n\ndraw(spec, scales(Layout = (; palette = clockwise)))","category":"page"},{"location":"layers/draw/#categories","page":"Drawing Layers","title":"categories","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The categories keyword can be used to reorder, label and even add categorical values.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Some reordering and renaming can be done using the sorter and renamer helper functions applied directly to columns in mapping. However, this works less well when several data sources are combined where not all categories appear in each column. Also, no categories can be added this way, which is something that can be useful if the existence of categories should be shown even though there is no data for them.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"New labels can be assigned using the value => label pair syntax.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; group = [\"A\", \"C\", \"D\"], value = [1, 3, 4])) *\n mapping(:group, :value) * visual(BarPlot)\n\nf = Figure()\n\ndraw!(f[1, 1], spec, scales(\n X = (; categories = [\"A\", \"B\", \"C\", \"D\"])\n))\ndraw!(f[1, 2], spec, scales(\n X = (; categories = [\"D\", \"A\", \"C\"])\n))\ndraw!(f[1, 3], spec, scales(\n X = (; categories = [\"A\" => \"a\", \"C\" => \"c\", \"D\" => \"d\"])\n))\n\nf","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can also pass a Function to categories which should take the vector of category values and return a new vector of categories or category/label pairs.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"For example, you could add summary statistics to the facet layout titles this way, by grabbing them from a dictionary computed separately.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nsummary_stats = Dict(\"A\" => 1.32, \"B\" => 4.19, \"C\" => 0.04)\n\ndf = (;\n x = randn(90),\n y = randn(90) .+ repeat([0, 5, 10], inner = 30),\n group = repeat([\"A\", \"B\", \"C\"], inner = 30)\n)\n\nspec = data(df) * mapping(:x, :y, col = :group) * visual(Scatter)\n\ndraw(spec, scales(Col = (;\n categories = cats -> [\n cat => rich(\"$cat\\n\", rich(\"λ = $(summary_stats[cat])\", font = :italic))\n for cat in reverse(cats)\n ]\n)))","category":"page"},{"location":"layers/draw/#Continuous-scale-options","page":"Drawing Layers","title":"Continuous scale options","text":"","category":"section"},{"location":"layers/draw/#Color-2","page":"Drawing Layers","title":"Color","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Continuous color scales can be modified using the familiar Makie attributes colormap, colorrange, highclip, lowclip and nan_color. By default, colorrange is set to the extrema of the encountered values, so no clipping occurs.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = [1:4; NaN; 6:10])) *\n mapping(:x, :y, color = :z) *\n visual(Scatter, markersize = 20)\n\ndraw(spec, scales(\n Color = (;\n colormap = :plasma,\n nan_color = :cyan,\n lowclip = :lime,\n highclip = :red,\n colorrange = (2, 9)\n )\n))","category":"page"},{"location":"layers/draw/#MarkerSize","page":"Drawing Layers","title":"MarkerSize","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The range of marker sizes can be set with the sizerange attribute. Marker sizes are computed such that their area, and not markersize itself, grows linearly with the scale values. ","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = data((; x = 1:10, y = 1:10, z = 10:10:100)) *\n mapping(:x, :y, markersize = :z) *\n visual(Scatter)\n\nf = Figure()\n\ngrid = draw!(f[1, 1], spec, scales(\n MarkerSize = (;\n sizerange = (5, 15)\n )\n))\nlegend!(f[1, 2], grid)\n\ngrid2 = draw!(f[2, 1], spec, scales(\n MarkerSize = (;\n sizerange = (5, 30)\n )\n))\nlegend!(f[2, 2], grid2)\n\nf","category":"page"},{"location":"layers/draw/#Legend-options","page":"Drawing Layers","title":"Legend options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"The legend keyword forwards most attributes to Makie's Legend function. The exceptions are listed here.","category":"page"},{"location":"layers/draw/#order","page":"Drawing Layers","title":"order","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"By default, the legend order depends on the order in which layers and mappings have been defined, as well as whether scales are categorical or continuous.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf = (;\n x = 1:12,\n y = 1:12,\n z = 1:12,\n group1 = repeat([\"A\", \"B\", \"C\"], inner = 4),\n group2 = repeat([\"X\", \"Y\"], 6),\n)\n\nspec = data(df) *\n mapping(:x, :y, markersize = :z, color = :group1, marker = :group2) *\n visual(Scatter)\n\ndraw(spec)","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can reorder the legend with the order keyword. This expects a vector with either Symbols or Vector{Symbol}s as elements, where each Symbol is the identifier for a scale that's represented in the legend.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Plain Symbols can be used for simple reordering:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec; legend = (; order = [:MarkerSize, :Color, :Marker]))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"Symbols that are grouped into Vectors indicate that their groups should be merged together. For example, consider this plot that features two color scales, but one for a scatter plot and one for a line plot.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\ndf_a = (; x = 1:9, y = [1, 2, 3, 5, 6, 7, 9, 10, 11], group = repeat([\"A\", \"B\", \"C\"], inner = 3))\n\nspec1 = data(df_a) * mapping(:x, :y, strokecolor = :group) * visual(Scatter, color = :transparent, strokewidth = 3, markersize = 15)\n\ndf_b = (; y = [4, 8], threshold = [\"first\", \"second\"])\n\nspec2_custom_scale = data(df_b) * mapping(:y, color = :threshold => scale(:color2)) * visual(HLines)\n\ndraw(spec1 + spec2_custom_scale)","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"You can group the two scales together using order. The titles are dropped.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec1 + spec2_custom_scale; legend = (; order = [[:Color, :color2]]))","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"If you want to add a title to a merged group, you can add it with the group => title pair syntax:","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"draw(spec1 + spec2_custom_scale; legend = (; order = [[:Color, :color2] => \"Title\"]))","category":"page"},{"location":"layers/draw/#Figure-options","page":"Drawing Layers","title":"Figure options","text":"","category":"section"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"AlgebraOfGraphics can add a title, subtitle and footnotes to a figure automatically. Settings for these must be passed to the figure keyword. Check the draw function for a complete list.","category":"page"},{"location":"layers/draw/","page":"Drawing Layers","title":"Drawing Layers","text":"using AlgebraOfGraphics\nusing CairoMakie\n\nspec = pregrouped(\n fill(1:5, 6),\n fill(11:15, 6),\n [reshape(sin.(1:25), 5, 5) .+ i for i in 1:6],\n layout = 1:6 => nonnumeric) * visual(Heatmap)\n\ndraw(\n spec;\n figure = (;\n title = \"Numbers in square configuration\",\n subtitle = \"Arbitrary data exhibits sinusoidal properties\",\n footnotes = [\n rich(superscript(\"1\"), \"First footnote\"),\n rich(superscript(\"2\"), \"Second \", rich(\"footnote\", color = :red)),\n ],\n ),\n axis = (; width = 100, height = 100)\n)","category":"page"},{"location":"","page":"Home","title":"Home","text":"CurrentModule = AlgebraOfGraphics","category":"page"},{"location":"#Introduction","page":"Home","title":"Introduction","text":"","category":"section"},{"location":"","page":"Home","title":"Home","text":"AlgebraOfGraphics defines a language for data visualization. It is based on a few simple building blocks that can be combined using + and *.","category":"page"},{"location":"","page":"Home","title":"Home","text":"This package can be installed typing","category":"page"},{"location":"","page":"Home","title":"Home","text":"julia> import Pkg; Pkg.add(\"AlgebraOfGraphics\")","category":"page"},{"location":"","page":"Home","title":"Home","text":"in the Julia REPL.","category":"page"},{"location":"","page":"Home","title":"Home","text":"See the Tutorial to get started.","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/legend_merging.jl\"","category":"page"},{"location":"gallery/gallery/scales/legend_merging/#Legend-merging","page":"Legend merging","title":"Legend merging","text":"","category":"section"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"using AlgebraOfGraphics, CairoMakie\n\nN = 40\n\nx = [1:N; 1:N]\ny = [cumsum(randn(N)); cumsum(randn(N))]\ngrp = [fill(\"a\", N); fill(\"b\", N)]\n\ndf = (; x, y, grp)\n\nlayers = visual(Lines) + visual(Scatter) * mapping(marker = :grp)\nplt = data(df) * layers * mapping(:x, :y, color = :grp)\n\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"","category":"page"},{"location":"gallery/gallery/scales/legend_merging/","page":"Legend merging","title":"Legend merging","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/regression_plots.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/#Regression-plots","page":"Regression plots","title":"Regression plots","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"using AlgebraOfGraphics, CairoMakie\n\nx = rand(100)\ny = @. randn() + x\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = linear() + visual(Scatter)\ndraw(layers * xy)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"x = rand(100)\ny = @. randn() + 5 * x ^ 2\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = smooth() + visual(Scatter)\nfg = draw(layers * xy)","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/regression_plots/","page":"Regression plots","title":"Regression plots","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/continuous_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/#Continuous-scales","page":"Continuous scales","title":"Continuous scales","text":"","category":"section"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"using AlgebraOfGraphics, CairoMakie\n\nx = 1:100\ny = @. sqrt(x) + 20x + 100\ndf = (; x, y)\nplt = data(df) *\n mapping(\n :x,\n :y => log => \"√x + 20x + 100 (log scale)\",\n ) * visual(Lines)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"x = 1:100\ny = @. sqrt(x) + 20x + 100\ndf = (; x, y)\nplt = data(df) *\n mapping(\n :x,\n :y => \"√x + 20x + 100 (log scale)\",\n ) * visual(Lines)\ndraw(plt, axis=(yscale=log,))","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"x = 0:100\ny = @. 0.01 + x/1000\ndf = (; x, y)\nplt = data(df) *\n mapping(\n :x,\n :y => \"y\",\n ) * visual(Lines)\nfg = draw(plt, axis=(yscale=log,))","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"","category":"page"},{"location":"gallery/gallery/scales/continuous_scales/","page":"Continuous scales","title":"Continuous scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"API/functions/#Functions","page":"Functions","title":"Functions","text":"","category":"section"},{"location":"API/functions/#Drawing-functions","page":"Functions","title":"Drawing functions","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"draw\ndraw!\ncolorbar!\nlegend!\npaginate\nscales","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.draw","page":"Functions","title":"AlgebraOfGraphics.draw","text":"draw(d, scales::Scales = scales(); [axis, figure, facet, legend, colorbar])\n\nDraw a AlgebraOfGraphics.AbstractDrawable object d. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. Scale options can be passed as an optional second argument. The output can be customized by passing named tuples or dictionaries with settings via the axis, figure, facet, legend or colorbar keywords. Legend and colorbar are drawn automatically unless show = false is passed to the keyword arguments of either legend or colorbar.\n\nFor finer control, use draw!, legend!, and colorbar! independently.\n\nFigure options\n\nAlgebraOfGraphics accepts the following special keywords under the figure keyword, the remaining attributes are forwarded to Makie's Figure constructor. The title, subtitle and footnotes arguments accept objects of any kind that Makie's Label or text function can handle, such as rich text.\n\n- `title`\n- `subtitle`\n- `titlesize::Union{Nothing,Float64}`\n- `subtitlesize::Union{Nothing,Float64}`\n- `titlealign::Union{Nothing,Symbol}`\n- `titlecolor`\n- `subtitlecolor`\n- `titlefont`\n- `subtitlefont`\n- `titlelineheight`\n- `subtitlelineheight`\n- `footnotes::Union{Nothing,Vector{Any}}`\n- `footnotesize::Union{Nothing,Float64}`\n- `footnotefont`\n- `footnotecolor`\n- `footnotealign`\n- `footnotelineheight`\n\n\n\n\n\ndraw(p::PaginatedLayers; kws...)\n\nDraw each element of PaginatedLayers p and return a Vector{FigureGrid}. Keywords kws are passed to the underlying draw calls.\n\n\n\n\n\ndraw(p::PaginatedLayers, i::Int; kws...)\n\nDraw the ith element of PaginatedLayers p and return a FigureGrid. Keywords kws are passed to the underlying draw call.\n\nYou can retrieve the number of elements using length(p).\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.draw!","page":"Functions","title":"AlgebraOfGraphics.draw!","text":"draw!(fig, d::AbstractDrawable, scales::Scales = scales(); [axis, facet])\n\nDraw a AlgebraOfGraphics.AbstractDrawable object d on fig. In practice, d will often be a AlgebraOfGraphics.Layer or AlgebraOfGraphics.Layers. fig can be a figure, a position in a layout, or an axis if d has no facet specification. The output can be customized by passing named tuples or dictionaries with settings via the axis or facet keywords.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.colorbar!","page":"Functions","title":"AlgebraOfGraphics.colorbar!","text":"colorbar!(figpos, grid; kwargs...)\n\nCompute colorbar for grid (which should be the output of draw!) and draw it in position figpos. Attributes allowed in kwargs are the same as MakieLayout.Colorbar.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.legend!","page":"Functions","title":"AlgebraOfGraphics.legend!","text":"legend!(figpos, grid; order = nothing, kwargs...)\n\nCompute legend for grid (which should be the output of draw!) and draw it in position figpos. All kwargs are forwarded to Makie's Legend constructor.\n\nThe order of scales represented in the legend can be changed with the order keyword. By default, legend sections are ordered the same as they appear in the plot specification. Assuming three scales Color, MarkerSize and custom exist in a spec, you can pass a vector to reorder them like [:MarkerSize, :custom, :Color], or merge multiple entries together with a nested vector like [[:MarkerSize, :custom], :Color], or give merged sections a title with the pair syntax [[:MarkerSize, :custom] => \"Merged group\", :Color].\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.paginate","page":"Functions","title":"AlgebraOfGraphics.paginate","text":"paginate(l; layout=nothing, row=nothing, col=nothing)\n\nPaginate l, the Layer or Layers object created by an AlgebraOfGraphics spec, to create a PaginatedLayers object. This contains a vector of layers where each layer operates on a subset of the input data.\n\nThe PaginatedLayers object can be passed to draw which will return a Vector{FigureGrid} rather than a single figure.\n\nThe keywords that limit the number of subplots on each page are the same that are used to specify facets in mapping:\n\nlayout: Maximum number of subplots in a wrapped linear layout.\nrow: Maximum number of rows in a 2D layout.\ncol: Maximum number of columns in a 2D layout.\n\nExample\n\nd = data((\n x = rand(1000),\n y = rand(1000),\n group1 = rand(string.('a':'i'), 1000),\n group2 = rand(string.('j':'r'), 1000),\n))\n\nlayer_1 = d * mapping(:x, :y, layout = :group1) * visual(Scatter)\npaginated_1 = paginate(layer_1, layout = 9)\nfiguregrids = draw(paginated_1)\n\nlayer_2 = d * mapping(:x, :y, row = :group1, col = :group2) * visual(Scatter)\npaginated_2 = paginate(layer_2, row = 4, col = 3)\nfiguregrid = draw(paginated_2, 1) # draw only the first grid\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scales","page":"Functions","title":"AlgebraOfGraphics.scales","text":"scales(; kwargs...)\n\nCreate a Scales object containing properties for aesthetic scales that can be passed to draw and draw!. Each keyword should be the name of a scale in the spec that is being drawn. That can either be a default one like Color, Marker or LineStyle, or a custom scale name defined in a mapping using the scale function.\n\nThe values attached to the keywords must be dict-like, with Symbols as keys (such as NamedTuples).\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Mapping-helpers","page":"Functions","title":"Mapping helpers","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"pregrouped\ndirect\nrenamer\nsorter\nnonnumeric\nverbatim\nscale\npresorted","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.pregrouped","page":"Functions","title":"AlgebraOfGraphics.pregrouped","text":"pregrouped(positional...; named...)\n\nEquivalent to data(Pregrouped()) * mapping(positional...; named...). Refer to mapping for more information.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.direct","page":"Functions","title":"AlgebraOfGraphics.direct","text":"direct(x)\n\nReturn DirectData(x) which marks x for direct use in a mapping that's used with a table-like data source. As a result, x will be used directly as data, without lookup in the table. If x is not an AbstractArray, it will be expanded like fill(x, n) where n is the number of rows in the data source.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.renamer","page":"Functions","title":"AlgebraOfGraphics.renamer","text":"renamer(arr::Union{AbstractArray, Tuple})\n\nUtility to rename a categorical variable, as in renamer([value1 => label1, value2 => label2]). The keys of all pairs should be all the unique values of the categorical variable and the values should be the corresponding labels. The order of arr is respected in the legend.\n\nExamples\n\njulia> r = renamer([\"class 1\" => \"Class One\", \"class 2\" => \"Class Two\"])\nAlgebraOfGraphics.Renamer{Vector{String}, Vector{String}}([\"class 1\", \"class 2\"], [\"Class One\", \"Class Two\"])\n\njulia> println(r(\"class 1\"))\nClass One\n\nAlternatively, a sequence of pair arguments may be passed.\n\njulia> r = renamer(\"class 1\" => \"Class One\", \"class 2\" => \"Class Two\")\nAlgebraOfGraphics.Renamer{Tuple{String, String}, Tuple{String, String}}((\"class 1\", \"class 2\"), (\"Class One\", \"Class Two\"))\n\njulia> println(r(\"class 1\"))\nClass One\n\nIf arr does not contain Pairs, elements of arr are assumed to be labels, and the unique values of the categorical variable are taken to be the indices of the array. This is particularly useful for dims mappings.\n\nExamples\n\njulia> r = renamer([\"Class One\", \"Class Two\"])\nAlgebraOfGraphics.Renamer{Nothing, Vector{String}}(nothing, [\"Class One\", \"Class Two\"])\n\njulia> println(r(2))\nClass Two\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.sorter","page":"Functions","title":"AlgebraOfGraphics.sorter","text":"sorter(ks)\n\nUtility to reorder a categorical variable, as in sorter([\"low\", \"medium\", \"high\"]). A vararg method sorter(\"low\", \"medium\", \"high\") is also supported. ks should include all the unique values of the categorical variable. The order of ks is respected in the legend.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.nonnumeric","page":"Functions","title":"AlgebraOfGraphics.nonnumeric","text":"nonnumeric(x)\n\nTransform x into a non numeric type that is printed and sorted in the same way.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.verbatim","page":"Functions","title":"AlgebraOfGraphics.verbatim","text":"verbatim(x)\n\nSignal that x should not be rescaled, but used in the plot as is.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scale","page":"Functions","title":"AlgebraOfGraphics.scale","text":"scale(id::Symbol)\n\nCreate a ScaleID object that can be used in a mapping to assign a custom id to the mapped variable. This variable will then not be merged into the default scale for its aesthetic type, but instead be handled separately, leading to a separate legend entry.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.presorted","page":"Functions","title":"AlgebraOfGraphics.presorted","text":"presorted(x)\n\nUse within a pair expression in mapping to signal that a categorical column from the data source should be used in the original order and not automatically sorted.\n\nExample:\n\n# normally, categories would be sorted a, b, c but with `presorted`\n# they stay in the order b, c, a\n\ndata((; some_column = [\"b\", \"c\", \"a\"])) * mapping(:some_column => presorted)\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Theming","page":"Functions","title":"Theming","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"set_aog_theme!\nAlgebraOfGraphics.aog_theme\nfrom_continuous","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.set_aog_theme!","page":"Functions","title":"AlgebraOfGraphics.set_aog_theme!","text":"set_aog_theme!(; kwargs...)\n\nSet the current theme to a predefined and opinionated theme, as defined by the unexported internal function AlgebraOfGraphics.aog_theme.\n\nTo tweak the predefined theme, use the function Makie.update_theme!. See the example below on how to change, e.g., default fontsize, title, and markersize.\n\nFor more information about setting themes, see the Theming section of the Makie.jl docs.\n\nExamples\n\njulia> using CairoMakie, AlgebraOfGraphics\n\njulia> set_aog_theme!() # Sets a prefedined theme\n\njulia> update_theme!( # Tweaks the current theme\n fontsize=30,\n markersize=40,\n Axis=(title=\"MyDefaultTitle\",)\n )\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.aog_theme","page":"Functions","title":"AlgebraOfGraphics.aog_theme","text":"aog_theme(; fonts=[firasans(\"Medium\"), firasans(\"Light\")])\n\nReturn a NamedTuple of theme settings. Intended for internal use. The provided functionality is exposed to the user by the function set_aog_theme!.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.from_continuous","page":"Functions","title":"AlgebraOfGraphics.from_continuous","text":"from_continuous(x)\n\nMark a colormap as continuous such that AlgebraOfGraphics will sample a categorical palette from start to end in n steps, and not by using the first n colors.\n\nYou could also use cgrad(colormap, n; categorical = true), however, this requires you to specify how many levels there are, which from_continuous detects automatically.\n\nExample:\n\ndraw(scales(Color = (; palette = from_continuous(:viridis))))\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Ticks-helpers","page":"Functions","title":"Ticks helpers","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"datetimeticks","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.datetimeticks","page":"Functions","title":"AlgebraOfGraphics.datetimeticks","text":"datetimeticks(datetimes::AbstractVector{<:TimeType}, labels::AbstractVector{<:AbstractString})\n\nGenerate ticks matching datetimes to the corresponding labels. The result can be passed to xticks, yticks, or zticks.\n\n\n\n\n\ndatetimeticks(f, datetimes::AbstractVector{<:TimeType})\n\nCompute ticks for the given datetimes using a formatting function f. The result can be passed to xticks, yticks, or zticks.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#Internal-functions","page":"Functions","title":"Internal functions","text":"","category":"section"},{"location":"API/functions/","page":"Functions","title":"Functions","text":"AlgebraOfGraphics.scientific_eltype\nAlgebraOfGraphics.scientific_type\nAlgebraOfGraphics.plottypes_attributes\nAlgebraOfGraphics.compute_attributes","category":"page"},{"location":"API/functions/#AlgebraOfGraphics.scientific_eltype","page":"Functions","title":"AlgebraOfGraphics.scientific_eltype","text":"scientific_eltype(v)\n\nDetermine whether v should be treated as a continuous, geometrical, or categorical array.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.scientific_type","page":"Functions","title":"AlgebraOfGraphics.scientific_type","text":"scientific_type(T::Type)\n\nDetermine whether T represents a continuous, geometrical, or categorical variable.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.plottypes_attributes","page":"Functions","title":"AlgebraOfGraphics.plottypes_attributes","text":"plottypes_attributes(entries)\n\nReturn plottypes and relative attributes, as two vectors of the same length, for the given entries.\n\n\n\n\n\n","category":"function"},{"location":"API/functions/#AlgebraOfGraphics.compute_attributes","page":"Functions","title":"AlgebraOfGraphics.compute_attributes","text":"compute_attributes(pl::ProcessedLayer, categoricalscales, continuousscales_grid, continuousscales)\n\nProcess attributes of a ProcessedLayer. In particular,\n\nremove AlgebraOfGraphics-specific layout attributes,\nopt out of Makie cycling mechanism,\ncustomize behavior of color (implementing alpha transparency),\ncustomize behavior of bar width (default to one unit when not specified),\nset correct colorrange.\n\nReturn computed attributes.\n\n\n\n\n\n","category":"function"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/presorted_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/#Pre-sorted-data","page":"Pre-sorted data","title":"Pre-sorted data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"using AlgebraOfGraphics, CairoMakie, DataFrames","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"Sometimes we have datasets that have an inherent order we want to preserve. For example, this dataframe has countries sorted by some_value.","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"countries = [\"Algeria\", \"Bolivia\", \"China\", \"Denmark\", \"Ecuador\", \"France\"]\ngroup = [\"2\", \"3\", \"1\", \"1\", \"3\", \"2\"]\nsome_value = exp.(sin.(1:6))\n\ndf = DataFrame(; countries, group, some_value)\nsort!(df, :some_value)\n\ndf","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"
6×3 DataFrame
Rowcountriesgroupsome_value
StringStringFloat64
1Ecuador30.383305
2Denmark10.469164
3France20.756226
4China11.15156
5Algeria22.31978
6Bolivia32.48258
","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"When we plot this, the categorical variable countries is sorted alphabetically by default:","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n mapping(:countries, :some_value, color = :group) *\n visual(BarPlot, direction = :x)\ndraw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"We don't want this, because we have purposefully sorted the dataframe to visualize which countries have the highest value. To retain the order, we can use the presorted helper.","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n mapping(:countries => presorted, :some_value, color = :group) *\n visual(BarPlot, direction = :x)\nfg = draw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"We can also mark multiple variables as presorted, note how the order in the color legend shifts when we do the same for group:","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"spec = data(df) *\n mapping(:countries => presorted, :some_value, color = :group => presorted) *\n visual(BarPlot, direction = :x)\ndraw(spec)","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/presorted_data/","page":"Pre-sorted data","title":"Pre-sorted data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/basic visualizations/lines_and_markers.jl\"","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#Lines-and-markers","page":"Lines and markers","title":"Lines and markers","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#A-simple-scatter-plot","page":"Lines and markers","title":"A simple scatter plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"using AlgebraOfGraphics, CairoMakie\n\ndf = (x=rand(100), y=rand(100))\nxy = data(df) * mapping(:x, :y)\ndraw(xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#A-simple-lines-plot","page":"Lines and markers","title":"A simple lines plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayer = visual(Lines)\ndraw(layer * xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/#Lines-and-scatter-combined-plot","page":"Lines and markers","title":"Lines and scatter combined plot","text":"","category":"section"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf = (; x, y)\nxy = data(df) * mapping(:x, :y)\nlayers = visual(Scatter) + visual(Lines)\ndraw(layers * xy)","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"x = range(-π, π, length=100)\ny = sin.(x)\ndf1 = (; x, y)\ndf2 = (x=rand(10), y=rand(10))\nlayers = data(df1) * visual(Lines) + data(df2) * visual(Scatter)\nfg = draw(layers * mapping(:x, :y))","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"(Image: )","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"","category":"page"},{"location":"gallery/gallery/basic visualizations/lines_and_markers/","page":"Lines and markers","title":"Lines and markers","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/statistical analyses/histograms.jl\"","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/#Histograms","page":"Histograms","title":"Histograms","text":"","category":"section"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (x=rand(0:99, 1000),)\nplt = data(df) * mapping(:x) * histogram(bins=20)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"df = (x=randn(1000), c=rand([\"a\", \"b\"], 1000))\nplt = data(df) * mapping(:x, color=:c, stack=:c) * histogram(bins=20)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"df = (x=rand(1000), y=randn(1000))\nplt = data(df) * mapping(:x, :y) * histogram(bins=20)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"(Image: )","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"","category":"page"},{"location":"gallery/gallery/statistical analyses/histograms/","page":"Histograms","title":"Histograms","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/wide_data.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Wide-data","page":"Wide data","title":"Wide data","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"using AlgebraOfGraphics, CairoMakie\nusing AlgebraOfGraphics: density\n\ndf = (a=randn(100), b=randn(100), c=randn(100))\nlabels = [\"Trace 1\", \"Trace 2\", \"Trace 3\"]\nplt = data(df) *\n density() *\n mapping([:a, :b, :c] .=> \"some label\") *\n mapping(color=dims(1) => renamer(labels))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"df = (a=rand(100), b=rand(100), c=rand(100), d=rand(100))\nlabels = [\"Trace One\", \"Trace Two\", \"Trace Three\"]\nlayers = linear() + visual(Scatter)\nplt = data(df) * layers * mapping(1, 2:4 .=> \"value\", color=dims(1) => renamer(labels))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"The wide format is combined with broadcast semantics.","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Axes-are-linked-when-they-correspond-to-the-same-variable","page":"Wide data","title":"Axes are linked when they correspond to the same variable","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"df = (\n sepal_length = 1 .+ rand(100),\n sepal_width = 2 .+ rand(100),\n petal_length = 3 .+ rand(100),\n petal_width = 4 .+ rand(100)\n)\nxvars = [\"sepal_length\", \"sepal_width\"]\nyvars = [\"petal_length\" \"petal_width\"]\nlayers = linear() + visual(Scatter)\nplt = data(df) * layers * mapping(xvars, yvars, col=dims(1), row=dims(2))\ndraw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/#Axes-can-be-fully-linked-or-fully-unlinked","page":"Wide data","title":"Axes can be fully linked or fully unlinked","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"draw(plt, facet = (; linkxaxes = :all, linkyaxes = :all))","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"fg = draw(plt, facet = (; linkxaxes = :none, linkyaxes = :none))","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/wide_data/","page":"Wide data","title":"Wide data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"layers/operations/#Algebraic-Operations","page":"Algebraic Operations","title":"Algebraic Operations","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"There are two algebraic types that can be added or multiplied with each other: AlgebraOfGraphics.Layer and AlgebraOfGraphics.Layers.","category":"page"},{"location":"layers/operations/#Multiplication-on-individual-layers","page":"Algebraic Operations","title":"Multiplication on individual layers","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Each layer is composed of data, mappings, and transformations. Datasets can be replaced, mappings can be merged, and transformations can be concatenated. These operations, taken together, define an associative operation on layers, which we call multiplication *.","category":"page"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Multiplication is primarily useful to combine partially defined layers.","category":"page"},{"location":"layers/operations/#Addition","page":"Algebraic Operations","title":"Addition","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"The operation + is used to superimpose separate layers. a + b has as many layers as la + lb, where la and lb are the number of layers in a and b respectively.","category":"page"},{"location":"layers/operations/#Multiplication-on-lists-of-layers","page":"Algebraic Operations","title":"Multiplication on lists of layers","text":"","category":"section"},{"location":"layers/operations/","page":"Algebraic Operations","title":"Algebraic Operations","text":"Multiplication naturally extends to lists of layers. Given two Layers objects a and b, containing la and lb layers respectively, the product a * b contains la * lb layers—all possible pair-wise products.","category":"page"},{"location":"layers/introduction/#Introduction","page":"Introduction","title":"Introduction","text":"","category":"section"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Layers are the key building blocks of AlgebraOfGraphics. Each layer is the product of the following elementary objects.","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Data (encoding the dataset).\nMapping (associating variables to plot attributes).\nVisual (encoding data-independent plot information).\nAnalyses (encoding transformations that are applied to the data before plotting).","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"Data, mappings, visuals and analyses can be combined together using Algebraic Operations to form one or more layers.","category":"page"},{"location":"layers/introduction/","page":"Introduction","title":"Introduction","text":"The output of these algebraic operations can be visualized, as shown in the section Drawing Layers.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/discrete_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/#Discrete-scales","page":"Discrete scales","title":"Discrete scales","text":"","category":"section"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"By default categorical ticks, as well as names from legend entries, are taken from the value of the variable converted to a string. Scales can be equipped with labels to overwrite that. You can either use the renamer function to apply relabeling or reordering to some column on the fly, or you use the categories keyword for the respective scale in the draw call.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"note: Note\nThe categories keyword (added in v0.7) also allows adding categories not present in the data.","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df = (x=rand([\"a\", \"b\", \"c\"], 100), y=rand(100))\nplt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n categories = [\"a\" => \"label1\", \"b\" => \"label2\", \"c\" => \"label3\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"The same operation can be done with renamer as well which modifies the input data","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) *\n mapping(\n :x => renamer(\"a\" => \"label1\", \"b\" => \"label2\", \"c\" => \"label3\"),\n :y\n ) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"With categories, you can add further categories that might be missing from your data","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n categories = [\"a\", \"missing\", \"b\", \"c\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"The order can also be changed:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt, scales(X = (;\n categories = [\"b\" => \"label b\", \"a\" => \"label a\", \"c\" => \"label c\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Or with renamer:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"plt = data(df) *\n mapping(\n :x => renamer(\"b\" => \"label b\", \"a\" => \"label a\", \"c\" => \"label c\"),\n :y\n ) * visual(BoxPlot)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"When categories come from different datasets, you can either apply the same renamer to multiple mappings, or set the ordering at one place in categories:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df1 = (; x = rand([\"one\", \"two\"], 100), y = randn(100))\ndf2 = (; x = rand([\"three\", \"four\"], 50), y = randn(50))\nplt = (data(df1) + data(df2)) * mapping(:x, :y) * visual(BoxPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"draw(plt, scales(X = (;\n categories = [\"one\", \"two\", \"three\", \"four\"]\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"You can also pass a function to categories, which has to return a vector with (optionally labelled) categories in the desired order. For example, strings are by default ordered alphabetically:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"df = (; name = [\"Anna Coolidge\", \"Berta Bauer\", \"Charlie Archer\"], age = [34, 79, 58])\nplt = data(df) * mapping(:name, :age) * visual(BarPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Instead of specifying the order manually, we could use a function to order by last name:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"draw(plt, scales(X = (;\n categories = cats -> sort(cats; by = name -> split(name)[2])\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"Or even combine this with a relabeling function that shortens the first name to the initial:","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"function initialed(name)\n a, b = split(name)\n return name => \"$(first(a)). $b\"\nend\n\ndraw(plt, scales(X = (;\n categories = cats -> initialed.(sort(cats; by = name -> split(name)[2]))\n)))","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"","category":"page"},{"location":"gallery/gallery/scales/discrete_scales/","page":"Discrete scales","title":"Discrete scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/layout/faceting.jl\"","category":"page"},{"location":"gallery/gallery/layout/faceting/#Faceting","page":"Faceting","title":"Faceting","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid","page":"Faceting","title":"Facet grid","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = let\n N = 100\n x0 = rand(1:10, N)\n i = rand([\"α\", \"β\"], N)\n j = rand([\"a\", \"b\", \"c\"], N)\n\n x = map(zip(x0, j)) do (xx, jj)\n shift = jj == \"a\" ? -2.9 : jj == \"c\" ? 2.9 : 0.0\n xx + shift\n end\n\n y = map(zip(x0, i)) do (xx, ii)\n shift = ii == \"α\" ? -3.9 : 3.9\n xx + 2 + shift + rand()\n end\n\n (; x, y, i, j)\nend\n\nplt = data(df) * mapping(:x, :y, row=:i, col=:j)\n\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid-with-minimal-axes-linking-needed-to-remove-ticks","page":"Faceting","title":"Facet grid with minimal axes linking needed to remove ticks","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:minimal, linkyaxes=:minimal))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-grid-with-unlinked-x-axes","page":"Faceting","title":"Facet grid with unlinked x-axes","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:none))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap","page":"Faceting","title":"Facet wrap","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(100), y=rand(100), l=rand([\"a\", \"b\", \"c\", \"d\", \"e\"], 100))\nplt = data(df) * mapping(:x, :y, layout=:l)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap-with-unlinked-axes","page":"Faceting","title":"Facet wrap with unlinked axes","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, facet=(; linkxaxes=:none, linkyaxes=:none))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Facet-wrap-with-specified-layout-for-rows-and-cols","page":"Faceting","title":"Facet wrap with specified layout for rows and cols","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(plt, scales(Layout = (; palette = [(1, 1), (2, 1), (3, 1), (1, 2), (2, 2)])))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Adding-traces-to-only-some-subplots","page":"Faceting","title":"Adding traces to only some subplots","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df1 = (x=rand(100), y=rand(100), i=rand([\"a\", \"b\", \"c\"], 100), j=rand([\"d\", \"e\", \"f\"], 100))\ndf2 = (x=[0, 1], y=[0.5, 0.5], i=fill(\"a\", 2), j=fill(\"e\", 2))\nlayers = data(df1) * visual(Scatter) + data(df2) * visual(Lines)\nfg = draw(layers * mapping(:x, :y, col=:i, row=:j))","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Caveats","page":"Faceting","title":"Caveats","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"The faceting variable must be non-numeric. If the source is numeric, you can convert it with nonnumeric.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(100), y=rand(100), l=rand([1, 2, 3, 4, 5], 100))\nplt = data(df) * mapping(:x, :y, layout=:l => nonnumeric)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/#Pagination","page":"Faceting","title":"Pagination","text":"","category":"section"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"If you have too many facets for one figure, you can use paginate to split the data into several subsets given a maximum number of plots per layout, row or column.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"Note that pagination is considered an experimental feature. In the current implementation, scales and layouts are not synchronized across pages. This means that, e.g., linked limits on one page are not influenced by limits of other pages. The exact synchronization behavior can be subject to change in non-breaking versions.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"df = (x=rand(500), y=rand(500), l=rand([\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"], 500))\nplt = data(df) * mapping(:x, :y, layout=:l)\npag = paginate(plt, layout = 4)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"PaginatedLayers with 2 entries (layout = 4)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"The object returned from draw will be a Vector{FigureGrid}.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"figuregrids = draw(pag)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"2-element Vector{AlgebraOfGraphics.FigureGrid}:\n FigureGrid()\n FigureGrid()","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"You can either extract single figures from this vector...","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"figuregrids[1]","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"or use draw with an optional second argument specifying the index of the page to draw.","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"draw(pag, 2)","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"","category":"page"},{"location":"gallery/gallery/layout/faceting/","page":"Faceting","title":"Faceting","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"EditURL = \"penguins.jl\"","category":"page"},{"location":"generated/penguins/#Tutorial","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This is a gentle and lighthearted tutorial on how to use tools from AlgebraOfGraphics, using as example dataset a collection of measurements on penguins[1]. See the Palmer penguins website for more information.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"[1]: Gorman KB, Williams TD, Fraser WR (2014) Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. DOI","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"To follow along this tutorial, you will need to install a few packages. All the required packages can be installed with the following command.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"julia> import Pkg; Pkg.add([\"AlgebraOfGraphics\", \"CairoMakie\", \"DataFrames\", \"LIBSVM\", \"PalmerPenguins\"])","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"After the above command completes, we are ready to go.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using PalmerPenguins, DataFrames\n\npenguins = dropmissing(DataFrame(PalmerPenguins.load()))\nfirst(penguins, 6)","category":"page"},{"location":"generated/penguins/#Frequency-plots","page":"Tutorial 🐧","title":"Frequency plots","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Let us start by getting a rough idea of how the data is distributed.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"note: Note\nDue to julia's compilation model, the first plot may take a while to appear.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using AlgebraOfGraphics, CairoMakie\nset_aog_theme!()\n\naxis = (width = 225, height = 225)\npenguin_frequency = data(penguins) * frequency() * mapping(:species)\n\ndraw(penguin_frequency; axis = axis)","category":"page"},{"location":"generated/penguins/#Small-intermezzo:-saving-the-plot","page":"Tutorial 🐧","title":"Small intermezzo: saving the plot","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"If you are working in an interactive enviroment with inline plotting support, such VSCode or Pluto.jl, the above should have displayed a bar plot. If you are working directly in the console, you can simply save the plot and inspect it in the file explorer.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"fg = draw(penguin_frequency; axis = axis)\nsave(\"figure.png\", fg, px_per_unit = 3) # save high-resolution png","category":"page"},{"location":"generated/penguins/#Styling-by-categorical-variables","page":"Tutorial 🐧","title":"Styling by categorical variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Next, let us see whether the distribution is the same across islands.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Oops! The bars are in the same spot and are hiding each other. We need to specify how we want to fix this. Bars can either dodge each other, or be stacked on top of each other.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island, dodge = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This is our first finding. Adelie is the only species of penguins that can be found on all three islands. To be able to see both which species is more numerous and how different species are distributed across islands in a unique plot, we could have used stack.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_frequency * mapping(color = :island, stack = :island)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Correlating-two-variables","page":"Tutorial 🐧","title":"Correlating two variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Now that we have understood the distribution of these three penguin species, we can start analyzing their features.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"penguin_bill = data(penguins) * mapping(:bill_length_mm, :bill_depth_mm)\ndraw(penguin_bill; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We would actually prefer to visualize these measures in centimeters, and to have cleaner axes labels. As we want this setting to be preserved in all of our bill visualizations, let us save it in the variable penguin_bill, to be reused in subsequent plots.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"penguin_bill = data(penguins) * mapping(\n :bill_length_mm => (t -> t / 10) => \"bill length (cm)\",\n :bill_depth_mm => (t -> t / 10) => \"bill depth (cm)\",\n)\ndraw(penguin_bill; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Much better! Note the parentheses around the function t -> t / 10. They are necessary to specify that the function maps t to t / 10, and not to t / 10 => \"bill length (cm)\".","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"There does not seem to be a strong correlation between the two dimensions, which is odd. Maybe dividing the data by species will help.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Ha! Within each species, penguins with a longer bill also have a deeper bill. We can confirm that with a linear regression","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * linear() * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This unfortunately no longer shows our data! We can use + to plot both things on top of each other:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * linear() * mapping(color = :species) + penguin_bill * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Note that the above expression seems a bit redundant, as we wrote the same thing twice. We can \"factor it out\" as follows","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * (linear() + mapping()) * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"where mapping() is a neutral multiplicative element. Of course, the above could be refactored as","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We could actually take advantage of the spare mapping() and use it to pass some extra info to the scatter, while still using all the species members to compute the linear fit.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping(marker = :sex)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This plot is getting a little bit crowded. We could instead show female and male penguins in separate subplots.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping(col = :sex)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"See how both plots show the same fit, because the sex mapping is not applied to linear(). The following on the other hand produces a separate fit for males and females:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species, col = :sex)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Smooth-density-plots","page":"Tutorial 🐧","title":"Smooth density plots","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"An alternative approach to understanding how two variables interact is to consider their joint probability density distribution (pdf).","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using AlgebraOfGraphics: density\nplt = penguin_bill * density(npoints=50) * mapping(col = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The default colormap is multi-hue, but it is possible to pass single-hue colormaps as well. The color range is inferred from the data by default, but it can also be passed manually. Both settings are passed via scales to draw, because multiple plots can share the same colormap, so visual is not the appropriate place for this setting.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"draw(plt, scales(Color = (; colormap = :grayC, colorrange = (0, 6))); axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"A Heatmap (the default visualization for a 2D density) is a bit unfortunate if we want to mark species by color. In that case, one can use visual to change the default visualization and, optionally, fine tune some arguments. In this case, a Wireframe with thin lines looks quite nice. (Note that, for the time being, we must specify explicitly that we require a 3D axis.)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (type = Axis3, width = 300, height = 300)\nlayer = density() * visual(Wireframe, linewidth=0.05)\nplt = penguin_bill * layer * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Of course, a more traditional approach would be to use a Contour plot instead:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (width = 225, height = 225)\nlayer = density() * visual(Contour)\nplt = penguin_bill * layer * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The data and the linear fit can also be added back to the plot:","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = density() * visual(Contour) + linear() + mapping()\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"In the case of many layers (contour, density and scatter) it is important to think about balance. In the above plot, the markers are quite heavy and can obscure the linear fit and the contour lines. We can lighten the markers using alpha transparency.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"layers = density() * visual(Contour) + linear() + visual(alpha = 0.5)\nplt = penguin_bill * layers * mapping(color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/#Correlating-three-variables","page":"Tutorial 🐧","title":"Correlating three variables","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We are now mostly up to speed with bill size, but we have not considered how it relates to other penguin features, such as their weight. For that, a possible approach is to use a continuous color on a gradient to denote weight and different marker shapes to denote species. Here we use group to split the data for the linear regression without adding any additional style.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"body_mass = :body_mass_g => (t -> t / 1000) => \"body mass (kg)\"\nlayers = linear() * mapping(group = :species) + mapping(color = body_mass, marker = :species)\nplt = penguin_bill * layers\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Naturally, within each species, heavier penguins have bigger bills, but perhaps counter-intuitively the species with the shallowest bills features the heaviest penguins. We could also try and see the interplay of these three variables in a 3D plot.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (type = Axis3, width = 300, height = 300)\nplt = penguin_bill * mapping(body_mass, color = :species)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"plt = penguin_bill * mapping(body_mass, color = :species, layout = :sex)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Note that static 3D plot can be misleading, as they only show one projection of 3D data. They are mostly useful when shown interactively.","category":"page"},{"location":"generated/penguins/#Machine-Learning","page":"Tutorial 🐧","title":"Machine Learning","text":"","category":"section"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Finally, let us use Machine Learning techniques to build an automated penguin classifier!","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We would like to investigate whether it is possible to predict the species of a penguin based on its bill size. To do so, we will use a standard classifier technique called Support-Vector Machine.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"The strategy is quite simple. We split the data into training and testing subdatasets. We then train our classifier on the training dataset and use it to make predictions on the whole data. We then add the new columns obtained this way to the dataset and visually inspect how well the classifier performed in both training and testing.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"using LIBSVM, Random\n\n# use approximately 80% of penguins for training\nRandom.seed!(1234) # for reproducibility\nN = nrow(penguins)\ntrain = fill(false, N)\nperm = randperm(N)\ntrain_idxs = perm[1:floor(Int, 0.8N)]\ntrain[train_idxs] .= true\nnothing # hide\n\n# fit model on training data and make predictions on the whole dataset\nX = hcat(penguins.bill_length_mm, penguins.bill_depth_mm)\ny = penguins.species\nmodel = SVC() # Support-Vector Machine Classifier\nfit!(model, X[train, :], y[train])\nŷ = predict(model, X)\n\n# incorporate relevant information in the dataset\npenguins.train = train\npenguins.predicted_species = ŷ\nnothing #hide","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Now, we have all the columns we need to evaluate how well our classifier performed.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"axis = (width = 225, height = 225)\ndataset =:train => renamer(true => \"training\", false => \"testing\") => \"Dataset\"\naccuracy = (:species, :predicted_species) => isequal => \"accuracy\"\nplt = data(penguins) *\n expectation() *\n mapping(:species, accuracy) *\n mapping(col = dataset)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"That is a bit hard to read, as all values are very close to 1. Let us visualize the error rate instead.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"error_rate = (:species, :predicted_species) => !isequal => \"error rate\"\nplt = data(penguins) *\n expectation() *\n mapping(:species, error_rate) *\n mapping(col = dataset)\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"So, mostly our classifier is doing quite well, but there are some mistakes, especially among Chinstrap penguins. Using at the same time the species and predicted_species mappings on different attributes, we can see which penguins are problematic.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"prediction = :predicted_species => \"predicted species\"\ndatalayer = mapping(color = prediction, row = :species, col = dataset)\nplt = penguin_bill * datalayer\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"Um, some of the penguins are indeed being misclassified... Let us try to understand why by adding an extra layer, which describes the density of the distributions of the three species.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"pdflayer = density() * visual(Contour, colormap=Reverse(:grays)) * mapping(group = :species)\nlayers = pdflayer + datalayer\nplt = penguin_bill * layers\ndraw(plt; axis = axis)","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"We can conclude that the classifier is doing a reasonable job: it is mostly making mistakes on outlier penguins.","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"","category":"page"},{"location":"generated/penguins/","page":"Tutorial 🐧","title":"Tutorial 🐧","text":"This page was generated using Literate.jl.","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/geometries.jl\"","category":"page"},{"location":"gallery/gallery/applications/geometries/#Geometries","page":"Geometries","title":"Geometries","text":"","category":"section"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"using AlgebraOfGraphics, CairoMakie\nusing GeometryBasics\n\ngeometry = [Rect(Vec(i, j), Vec(1, 1)) for i in 0:7 for j in 0:7]\ngroup = [isodd(i + j) ? \"light square\" : \"dark square\" for i in 0:7 for j in 0:7]\ndf = (; geometry, group)\n\nplt = data(df) * visual(Poly) * mapping(:geometry, color = :group)\nfg = draw(plt; axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"","category":"page"},{"location":"gallery/gallery/applications/geometries/","page":"Geometries","title":"Geometries","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/index.md\"","category":"page"},{"location":"gallery/#Gallery","page":"Gallery","title":"Gallery","text":"","category":"section"},{"location":"gallery/#Basic-visualizations","page":"Gallery","title":"Basic visualizations","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Lines and markers\n

\n

\n Basic linear and scatter plots.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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\n
\n

\n Statistical visualizations\n

\n

\n Violin plot, boxplot, qqplot.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Statistical-analyses","page":"Gallery","title":"Statistical analyses","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Density plots\n

\n

\n Visualizing kernel density estimation of data.\n

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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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\n

\n Histograms\n

\n

\n Computing 1- and 2-dimensional histograms.\n

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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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\n
\n

\n Regression plots\n

\n

\n Linear and nonlinear regressions.\n

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\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Data-manipulations","page":"Gallery","title":"Data manipulations","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Renaming and transforming variables\n

\n

\n Transforming data before generating the plot.\n

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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
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\n
\n

\n Using mapping without tabular data\n

\n

\n Passing columnar data directly to `mapping`.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Pre-grouped data\n

\n

\n Working with arrays of arrays.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Pre-sorted data\n

\n

\n Preserving the original order of categorical data.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Wide data\n

\n

\n Working with data in the wide format.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Scales","page":"Gallery","title":"Scales","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Some advanced keywords to tweak the plot","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Discrete scales\n

\n

\n Sorting and renaming categorical variables.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Continuous scales\n

\n

\n Applying nonlinear transformations.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Custom scales\n

\n

\n Custom palettes and custom attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Secondary scales\n

\n

\n Categorical and continuous scales in the same plot.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Multiple color scales\n

\n

\n Categorical and continuous scales in the same plot.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Pre-scaled data\n

\n

\n Pass data to the plot as is.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Legend merging\n

\n

\n Multiple scales for the same variable.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Dodging\n

\n

\n Dodging groups to avoid overlaps.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Customization","page":"Gallery","title":"Customization","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Some advanced keywords to tweak the plot","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Axis tweaking\n

\n

\n Setting axis attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Figure tweaking\n

\n

\n Setting figure attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Legend tweaking\n

\n

\n Setting legend attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Colorbar tweaking\n

\n

\n Setting colorbar attributes.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Layout","page":"Gallery","title":"Layout","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Faceting\n

\n

\n Generating a grid of plots.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Nested layouts\n

\n

\n AlgebraOfGraphics plots within a Makie figure.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/#Applications","page":"Gallery","title":"Applications","text":"","category":"section"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"Example applications with different types of data","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Geographic data\n

\n

\n Antarctic coastline.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Geometries\n

\n

\n Visualizing geometries.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
\n

\n Time series\n

\n

\n Visualizing time series data.\n

\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"(Image: card image)","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
\n
\n
","category":"page"},{"location":"gallery/","page":"Gallery","title":"Gallery","text":"
","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/data manipulations/new_columns_on_the_fly.jl\"","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/#Renaming-and-transforming-variables","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"","category":"section"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"using AlgebraOfGraphics, CairoMakie\n\ndf = (x=rand(100), y=rand(100), z=rand(100), c=rand([\"a\", \"b\"], 100))\nlayers = linear() + mapping(color=:z)\n\nplt = data(df) * layers * mapping(:x => (x -> x^2) => L\"x^2\", :y => L\"This variable is called $y$\")\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"Use a Tuple to pass combine several columns into a unique operation.","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"plt = data(df) * layers * mapping(:x, (:x, :y, :z) => (+) => L\"the new variable $x + y + z$\", layout=:c)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"(Image: )","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"","category":"page"},{"location":"gallery/gallery/data manipulations/new_columns_on_the_fly/","page":"Renaming and transforming variables","title":"Renaming and transforming variables","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/customization/colorbar.jl\"","category":"page"},{"location":"gallery/gallery/customization/colorbar/#Colorbar-tweaking","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"","category":"section"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: Source code)","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"To tweak the position and appearance of the colorbar, simply use the colorbar keyword when plotting. For example","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"df = (x=rand(100), y=rand(100), z=rand(100))\nplt = data(df) * mapping(:x, :y, color=:z)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"fg = draw(plt, colorbar=(position=:top, size=25))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"To change the colormap, you have to modify the corresponding scale. Usually, this will be the Color scale.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"note: Note\nBefore AlgebraOfGraphics v0.7, you would change the colormap by passing it via visual. This was changed so that each color scale, which can be used by multiple plot layers, has a single source of truth for these settings.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"draw(plt, scales(Color = (; colormap = :thermal)))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"Other continuous color parameters are highclip, lowclip, nan_color and colorrange.","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"draw(plt, scales(Color = (;\n colormap = :thermal,\n colorrange = (0.25, 0.75),\n highclip = :cyan,\n lowclip = :lime,\n)))","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"(Image: )","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"","category":"page"},{"location":"gallery/gallery/customization/colorbar/","page":"Colorbar tweaking","title":"Colorbar tweaking","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/custom_scales.jl\"","category":"page"},{"location":"gallery/gallery/scales/custom_scales/#custom_scales","page":"Custom scales","title":"Custom scales","text":"","category":"section"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"A palette maps categorical values to particular attribute specifications (e.g. the first value maps to green, the second maps to red, and so on).","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x=repeat(1:20, inner=20)\ny=repeat(1:20, outer=20)\nu=cos.(x)\nv=sin.(y)\nc=rand(Bool, length(x))\nd=rand(Bool, length(x))\ndf = (; x, y, u, v, c, d)\ncolors = [colorant\"#E24A33\", colorant\"#348ABD\"]\nheads = ['◮', '◭']\nplt = data(df) *\n mapping(:x, :y, :u, :v) *\n mapping(arrowhead = :c => nonnumeric) *\n mapping(color = :d => nonnumeric) *\n visual(Arrows, arrowsize=10, lengthscale=0.4, linewidth = 1)\nfg = draw(plt, scales(Marker = (; palette = heads), Color = (; palette = colors)))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"To associate specific attribute values to specific data values, use pairs. Missing keys will cycle over values that are not pairs.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x = rand(100)\ny = rand(100)\nz = rand([\"a\", \"b\", \"c\", \"d\"], 100)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z)\ncolors = [\"a\" => :tomato, \"c\" => :lime, colorant\"#988ED5\", colorant\"#777777\"]\ndraw(plt, scales(Color = (; palette = colors)))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"Categorical color gradients can also be passed to palettes. You can use the from_continuous helper function to wrap continuous colormaps which you want to sample from start to end in n steps, where n is the number of categories you are visualizing.","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"x = rand(200)\ny = rand(200)\nz = rand([\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"], 200)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z)\ndraw(plt, scales(Color = (; palette = from_continuous(:cividis))))","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"","category":"page"},{"location":"gallery/gallery/scales/custom_scales/","page":"Custom scales","title":"Custom scales","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/dodging.jl\"","category":"page"},{"location":"gallery/gallery/scales/dodging/#dodging","page":"Dodging","title":"Dodging","text":"","category":"section"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"Some plot types like barplots natively support a dodge attribute which avoids overlap between groups that share the same coordinates.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"df = (; x = [\"One\", \"One\", \"Two\", \"Two\"], y = 1:4, err = [0.2, 0.3, 0.4, 0.5], group = [\"A\", \"B\", \"A\", \"B\"])\nplt = data(df) * mapping(:x, :y, dodge = :group, color = :group) * visual(BarPlot)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"Other plot types like errorbars do not have a dodge keyword, however you can dodge them using AlgebraOfGraphic's hardcoded dodge_x or dodge_y mappings. These will only shift the data away from the category centers but will not change other plot attributes (like dodging a barplot makes narrower bars). They are therefore mostly appropriate for \"width-less\" plot types like scatters or errorbars.","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"If you combine errorbars with a barplot, AlgebraOfGraphics will apply the barplot's dodge width to the errorbars automatically so they match:","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"plt2 = data(df) * mapping(:x, :y, :err, dodge_x = :group) * visual(Errorbars)\nfg = draw(plt + plt2)","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"If you only use \"width-less\" plot types, you will get an error if you don't set a dodge width manually. You can do so via the scales function:","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"df = (\n x = repeat(1:10, inner = 2),\n y = cos.(range(0, 2pi, length = 20)),\n ylow = cos.(range(0, 2pi, length = 20)) .- 0.2,\n yhigh = cos.(range(0, 2pi, length = 20)) .+ 0.3,\n dodge = repeat([\"A\", \"B\"], 10)\n)\n\nf = Figure()\nplt3 = data(df) * (\n mapping(:x, :y, dodge_x = :dodge, color = :dodge) * visual(Scatter) +\n mapping(:x, :ylow, :yhigh, dodge_x = :dodge, color = :dodge) * visual(Rangebars)\n)\nkw(; kwargs...) = (; xticklabelsvisible = false, xticksvisible = false, xlabelvisible = false, kwargs...)\n\ndraw!(f[1, 1], plt3, scales(DodgeX = (; width = 0.25)); axis = kw(title = \"DodgeX = (; width = 0.25)\"))\ndraw!(f[2, 1], plt3, scales(DodgeX = (; width = 0.5)); axis = kw(title = \"DodgeX = (; width = 0.5)\"))\ndraw!(f[3, 1], plt3, scales(DodgeX = (; width = 1.0)); axis = (; title = \"DodgeX = (; width = 1.0)\"))\n\nf","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"","category":"page"},{"location":"gallery/gallery/scales/dodging/","page":"Dodging","title":"Dodging","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/scales/prescaled_data.jl\"","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/#Pre-scaled-data","page":"Pre-scaled data","title":"Pre-scaled data","text":"","category":"section"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"using AlgebraOfGraphics, CairoMakie\nusing Colors\n\nx = rand(100)\ny = rand(100)\nz = rand([colorant\"teal\", colorant\"orange\"], 100)\ndf = (; x, y, z)\nplt = data(df) * mapping(:x, :y, color=:z => verbatim)\ndraw(plt)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"Plotting labels instead of markers","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"x = rand(100)\ny = rand(100)\nlabel = rand([\"a\", \"b\"], 100)\ndf = (; x, y, label)\nplt = data(df) * mapping(:x, :y, text=:label => verbatim) * visual(Makie.Text)\nfg = draw(plt)","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"","category":"page"},{"location":"gallery/gallery/scales/prescaled_data/","page":"Pre-scaled data","title":"Pre-scaled data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/layout/nested_layouts.jl\"","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/#Nested-layouts","page":"Nested layouts","title":"Nested layouts","text":"","category":"section"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"using AlgebraOfGraphics, CairoMakie","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"All AlgebraOfGraphics plots can be inserted in any figure position, where the rest of the figure is managed by vanilla Makie. For example","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"fig = Figure(; size=(800, 600))\nax = Axis(fig[1, 1], title=\"Some plot\")\n\ndf = (\n x=rand(500),\n y=rand(500),\n i=rand([\"a\", \"b\", \"c\"], 500),\n j=rand([\"d\", \"e\", \"f\"], 500),\n k=rand(Bool, 500),\n l=rand(Bool, 500)\n)\nplt = data(df) * mapping(:x, :y, col=:i, row=:j, color=:k, marker=:l)\n\nsubfig = fig[1, 2:3]\nag = draw!(subfig, plt)\nfor ae in ag\n ae.axis.xticklabelrotation[] = π/2\nend\nlegend!(fig[end+1, 2], ag, orientation=:horizontal, tellheight=true)\nfig","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"The above also works in more nested situations.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"f = Figure(; size=(800, 600))\nax = Axis(f[1, 1], title=\"Some plot\")\nsubfig = f[1, 2]\nax2 = Axis(subfig[1, 1])\n\ndf = (\n x=rand(500),\n y=rand(500),\n c=rand([\"a\", \"b\", \"c\"], 500),\n)\nplt = data(df) * mapping(:x, :y, color=:c)\n\ndraw!(subfig[2, 1], plt)\nf","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"It is also possible to let Makie control the axis and plot directly on top of it.","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"f = Figure(; size=(800, 300))\nax1 = Axis(f[1, 1])\nax2 = Axis(f[1, 2])\n\ndf = (x=rand(100), y=rand(100), c=rand([\"a\", \"b\", \"c\"], 100))\nplt = data(df) * mapping(:x, :y, color=:c)\n\nscatter!(ax1, rand(10), rand(10), color=:black)\ngrid = draw!(ax2, plt)\nlegend!(f[1, 3], grid)\n\nf","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"(Image: )","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"","category":"page"},{"location":"gallery/gallery/layout/nested_layouts/","page":"Nested layouts","title":"Nested layouts","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"EditURL = \"/home/runner/work/AlgebraOfGraphics.jl/AlgebraOfGraphics.jl/docs/gallery/gallery/applications/geographic.jl\"","category":"page"},{"location":"gallery/gallery/applications/geographic/#Geographic-data","page":"Geographic data","title":"Geographic data","text":"","category":"section"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"(Image: Source code) (Image: Author)","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"using AlgebraOfGraphics, CairoMakie\nusing Shapefile, ZipFile\nusing Downloads","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"Antarctic coastline. Data from the SCAR Antarctic Digital Database[1].","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"[1]: Gerrish, L., Fretwell, P., & Cooper, P. (2021). Medium resolution vector polygons of the Antarctic coastline (7.4) [Data set]. UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation. https://doi.org/10.5285/747e63e-9d93-49c2-bafc-cf3d3f8e5afa","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"# Download, extract, and load shapefile\nt = mktempdir() do dir\n url = \"https://ramadda.data.bas.ac.uk/repository/entry/get/add_coastline_medium_res_polygon_v7_4.zip?entryid=synth%3Ae747e63e-9d93-49c2-bafc-cf3d3f8e5afa%3AL2FkZF9jb2FzdGxpbmVfbWVkaXVtX3Jlc19wb2x5Z29uX3Y3XzQuemlw\"\n r = ZipFile.Reader(seekstart(Downloads.download(url, IOBuffer())))\n for f in r.files\n open(joinpath(dir, f.name), write = true) do io\n write(io, read(f, String));\n end\n end\n Shapefile.Table(joinpath(dir, \"add_coastline_medium_res_polygon_v7_4.shp\"))\nend\n\n# Draw map\nplt = data(t) * mapping(:geometry, color = :surface) * visual(Choropleth)\nfg = draw(plt; axis=(aspect=1,))","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"(Image: )","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"","category":"page"},{"location":"gallery/gallery/applications/geographic/","page":"Geographic data","title":"Geographic data","text":"This page was generated using DemoCards.jl and Literate.jl.","category":"page"}] }