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README.Rmd
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README.Rmd
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---
title: "tricolore. A flexible color scale for ternary compositions"
author: "Jonas Schöley & Ilya Kashnitsky"
output: github_document
---
```{r echo=FALSE}
knitr::opts_chunk$set(warning=FALSE,
message=FALSE,
fig.width = 12,
fig.height = 12)
```
[](https://cran.r-project.org/package=tricolore)
[](https://travis-ci.org/jschoeley/tricolore)
[](https://www.gnu.org/licenses/gpl-3.0)
What is *tricolore*?
--------------------
`tricolore` is an R library providing a flexible color scale for the visualization of three-part (ternary) compositions. Its main functionality is to color-code any ternary composition as a mixture of three primary colours and to draw a suitable color-key. `tricolore` flexibly adapts to different visualization challenges via
- *discrete* and *continuous* color support,
- support for unbalanced compositional data via *centering*,
- support for data with very narrow range via *scaling*,
- *hue*, *chroma* and *lightness* options.
Getting Started
---------------
```{r eval=FALSE}
install.packages('tricolore')
library(tricolore); DemoTricolore()
```
The `Tricolore()` function expects a dataframe of three-part compositions, color-codes the compositions and returns a list with elements `rgb` and `key`. The first list element is a vector of rgb codes for the color-coded compositions, the latter element gives a plot of the color key.
Here's a minimal example using simulated data.
```{r message=FALSE, fig.cap='A ternary color key with the color-coded compositional data visible as points.'}
library(tricolore)
# simulate 243 ternary compositions
P <- as.data.frame(prop.table(matrix(runif(3^6), ncol = 3), 1))
# color-code each composition and return a corresponding color key
colors_and_legend <- Tricolore(P, 'V1', 'V2', 'V3')
# the color-coded compositions
head(colors_and_legend$rgb)
colors_and_legend$key
```
You can familiarize yourself with the various options of `tricolore` by running `DemoTricolore()`.
Ternary choropleth maps
-----------------------
Here I demonstrate how to create a choropleth map of the regional distribution of education attainment in Europe 2016 using `ggplot2`.
The data set `euro_example` contains the administrative boundaries for the European NUTS-2 regions in the column `geometry`. This data can be used to plot a choropleth map of Europe using the `sf` package. Each region is represented by a single row. The name of a region is given by the variable `name` while the respective [NUTS-2](https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics) geocode is given by the variable `id`. For each region some compositional statistics are available: Variables starting with `ed` refer to the relative share of population ages 25 to 64 by educational attainment in 2016 and variables starting with `lf` refer to the relative share of workers by labor-force sector in the European NUTS-2 regions 2016.
**1. Using the `Tricolore()` function, color-code each educational composition in the `euro_example` data set and add the resulting vector of hex-srgb colors as a new variable to the dataframe. Store the color key seperately.**
```{r}
# color-code the data set and generate a color-key
tric_educ <- Tricolore(euro_example,
p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8')
```
`tric` contains both a vector of color-coded compositions (`tric$rgb`) and the corresponding color key (`tric$key`). We add the vector of colors to the map-data.
```{r}
# add the vector of colors to the `euro_example` data
euro_example$educ_rgb <- tric_educ$rgb
```
**2. Using `ggplot2` and the joined color-coded education data and geodata, plot a ternary choropleth map of education attainment in the European regions. Add the color key to the map.**
The secret ingredient is `scale_fill_identity()` to make sure that each region is colored according to the value in the `educ_rgb` variable of `euro_example`.
```{r}
library(ggplot2)
plot_educ <-
# using data sf data `euro_example`...
ggplot(euro_example) +
# ...draw a choropleth map
geom_sf(aes(fill = educ_rgb), size = 0.1) +
# ...and color each region according to the color-code
# in the variable `educ_rgb`
scale_fill_identity()
plot_educ
```
Using `annotation_custom()` and `ggplotGrob` we can add the color key produced by `Tricolore()` to the map. Internally, the color key is produced with the [`ggtern`](http://www.ggtern.com/) package. In order for it to render correctly we need to load `ggtern` *after* loading `ggplot2`. Don't worry, the `ggplot2` functions still work.
```{r}
library(ggtern)
plot_educ +
annotation_custom(
ggplotGrob(tric_educ$key),
xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5
)
```
Because the color key behaves just like a `ggplot2` plot we can change it to our liking.
```{r}
plot_educ <-
plot_educ +
annotation_custom(
ggplotGrob(
tric_educ$key +
labs(L = '0-2', T = '3-4', R = '5-8')),
xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5
)
plot_educ
```
Some final touches...
```{r}
plot_educ +
theme_void() +
coord_sf(datum = NA) +
labs(title = 'European inequalities in educational attainment',
subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.',
caption = 'Data by eurostat (edat_lfse_04).')
```
Continuous vs. discrete colors
------------------------------
By default `tricolore` uses a discrete colors scale with 16 colors. This can be changed via the `breaks` parameter. A value of `Inf` gives a continuous color scale...
```{r}
# color-code the data set and generate a color-key
tric_educ_disc <- Tricolore(euro_example,
p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8',
breaks = Inf)
euro_example$educ_rgb_disc <- tric_educ_disc$rgb
ggplot(euro_example) +
geom_sf(aes(fill = educ_rgb_disc), size = 0.1) +
scale_fill_identity() +
annotation_custom(
ggplotGrob(
tric_educ_disc$key +
labs(L = '0-2', T = '3-4', R = '5-8')),
xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5
) +
theme_void() +
coord_sf(datum = NA) +
labs(title = 'European inequalities in educational attainment',
subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.',
caption = 'Data by eurostat (edat_lfse_04).')
```
...and a `breaks = 2` gives a discrete color scale with $2^2=4$ colors, highlighting the regions with an absolute majority of any part of the composition.
```{r}
# color-code the data set and generate a color-key
tric_educ_disc <- Tricolore(euro_example,
p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8',
breaks = 2)
euro_example$educ_rgb_disc <- tric_educ_disc$rgb
ggplot(euro_example) +
geom_sf(aes(fill = educ_rgb_disc), size = 0.1) +
scale_fill_identity() +
annotation_custom(
ggplotGrob(
tric_educ_disc$key +
labs(L = '0-2', T = '3-4', R = '5-8')),
xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5
) +
theme_void() +
coord_sf(datum = NA) +
labs(title = 'European inequalities in educational attainment',
subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.',
caption = 'Data by eurostat (edat_lfse_04).')
```
Ternary centering
-----------------
While the ternary balance scheme allows for dense yet clear visualizations of *well spread out* ternary compositions the technique is less informative when used with highly *unbalanced data*. The map below shows the regional labor force composition in Europe as of 2016 in nearly monochromatic colors, the different shades of blue signifying a working population which is concentrated in the tertiary (services) sector. Regions in Turkey and Eastern Europe show a somewhat higher concentration of workers in the primary (production) sector but overall the data shows little variation with regards to the *visual reference point*, i.e. the greypoint marking perfectly balanced proportions.
```{r}
tric_lf_non_centered <- Tricolore(euro_example, breaks = Inf,
'lf_pri', 'lf_sec', 'lf_ter')
euro_example$rgb_lf_non_centered <- tric_lf_non_centered$rgb
ggplot(euro_example) +
geom_sf(aes(fill = rgb_lf_non_centered), size = 0.1) +
scale_fill_identity() +
annotation_custom(
ggplotGrob(tric_lf_non_centered$key +
labs(L = '% Primary', T = '% Secondary', R = '% Tertiary')),
xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5
) +
theme_void() +
coord_sf(datum = NA) +
labs(title = 'European inequalities in labor force composition',
subtitle = 'Regional distribution of labor force across the three sectors in 2016.',
caption = 'Data by eurostat (lfst_r_lfe2en2).')
```
A remedy for analyzing data which shows little variation in relation to some reference point is to *change the point of reference*. The map below yet again shows the European regional labor force composition in 2016 but the color scale has been altered so that its greypoint -- the visual point of reference -- is positioned at the European annual average. Consequently the colors now show direction and magnitude of the deviation from the European average labor force composition. Pink, Green and Blue hues show a higher than average share of workers in the primary, secondary and tertiary sector respectively. The saturation of the colors show the magnitude of that deviation with perfect grey marking a region that has a labor force composition equal to the European average, i.e. the reference point.
Centering the color scale over the labor-force composition of the average European NUTS-2 region shows various patterns of deviations from the average. Metropolitan regions (Hamburg, Stockholm, Paris, Madrid) have a higher than average share of tertiary workers. Large parts of France are quite grey, indicating a labor-force composition close to the average, while Eastern Europe, the south of Spain and Italy have a higher than average share of workers active in the primary sector.
```{r}
tric_lf_centered <-
Tricolore(euro_example,
'lf_pri', 'lf_sec', 'lf_ter',
center = NA, crop = TRUE)
euro_example$rgb_lf_centered <- tric_lf_centered$rgb
ggplot(euro_example) +
geom_sf(aes(fill = rgb_lf_centered), size = 0.1) +
scale_fill_identity() +
annotation_custom(
ggplotGrob(
tric_lf_centered$key +
labs(L = '% Primary', T = '% Secondary', R = '% Tertiary')),
xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5
) +
theme_void() +
coord_sf(datum = NA) +
labs(title = 'European inequalities in labor force composition',
subtitle = 'Regional distribution of labor force across the three sectors in 2016.',
caption = 'Data by eurostat (lfst_r_lfe2en2).')
```
Contributing
------------
This software is an academic project. We welcome any issues and pull requests.
Please report any bugs you find by submitting an issue on github.com/jschoeley/tricolore/issues.
If you wish to contribute, please submit a pull request following the guidelines stated in [CONTRIBUTING.md](https://github.com/jschoeley/tricolore/blob/devel/CONTRIBUTING.md).