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Remove @with, add @attach #367

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4e11ffd
no more with
pdeffebach Oct 31, 2023
abbe1ae
no more with
pdeffebach Oct 31, 2023
25302da
deprecation
pdeffebach Nov 1, 2023
a0bdcca
tests pass
pdeffebach Nov 1, 2023
fd16687
Update src/macros.jl
pdeffebach Nov 1, 2023
d274cc6
Update src/macros.jl
pdeffebach Nov 1, 2023
aee6b50
exporting with
pdeffebach Nov 1, 2023
2e70ec6
Merge branch 'with_to_attach' of https://github.com/JuliaData/DataFra…
pdeffebach Nov 1, 2023
875517c
update doctests
pdeffebach Nov 7, 2023
cd227ac
fix doctes failures
pdeffebach Nov 7, 2023
49c7970
version bump
pdeffebach Dec 14, 2023
92b33cb
fix merge
pdeffebach Dec 14, 2023
750e881
add news entry
pdeffebach Dec 14, 2023
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11 changes: 11 additions & 0 deletions src/macros.jl
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Expand Up @@ -663,6 +663,17 @@
esc(attach_helper(d, body))
end

"""
@with(x, args...)

Deprecated version of `@attach`, see `?@with` for details.
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"""
macro with(x, args...)
@warn "`@with is deprecated, use `@with` instead."
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esc(attach_helper(d, body))
end


ASTABLE_RHS_ORDERBY_DOCS = """
In operations, it is also allowed to use `AsTable(cols)` to work with
multiple columns at once, where the columns are grouped together in a
Expand Down Expand Up @@ -860,7 +871,7 @@

### Examples

```jldoctest

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```jldoctest
julia> using DataFramesMeta, Statistics

julia> df = DataFrame(x = 1:3, y = [2, 1, 2]);

julia> globalvar = [2, 1, 0];

julia> @subset(df, :x .> 1)
2×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     2      1
   2 │     3      2

julia> @subset(df, :x .> globalvar)
2×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     2      1
   2 │     3      2

julia> @subset df begin
           :x .> globalvar
           :y .== 3
       end
0×2 DataFrame

julia> df = DataFrame(n = 1:20, x = [3, 3, 3, 3, 1, 1, 1, 2, 1, 1,
                                    2, 1, 1, 2, 2, 2, 3, 1, 1, 2]);

julia> g = groupby(df, :x);

julia> @subset(g, :n .> mean(:n))
8×2 DataFrame
 Row │ n      x
     │ Int64  Int64
─────┼──────────────
   1 │    12      1
   2 │    13      1
   3 │    15      2
   4 │    16      2
   5 │    17      3
   6 │    18      1
   7 │    19      1
   8 │    20      2

julia> @subset g begin
           :n .> mean(:n)
           :n .< 20
       end
7×2 DataFrame
 Row │ n      x
     │ Int64  Int64
─────┼──────────────
   1 │    12      1
   2 │    13      1
   3 │    15      2
   4 │    16      2
   5 │    17      3
   6 │    18      1
   7 │    19      1

julia> df = DataFrame(a = [1, 2, missing], b = ["x", "y", missing]);

julia> @subset(df, :a .== 1)
1×2 DataFrame
 Row │ a       b
     │ Int64?  String?
─────┼─────────────────
   1 │      1  x

julia> @subset(df, :a .< 3; view = true)
2×2 SubDataFrame
 Row │ a       b
     │ Int64?  String?
─────┼─────────────────
   1 │      1  x
   2 │      2  y

julia> @subset df begin
           :a .< 3
           @kwarg view = true
       end
2×2 SubDataFrame
 Row │ a       b
     │ Int64?  String?
─────┼─────────────────
   1 │      1  x
   2 │      2  y
```

Subexpression:

@subset df begin
    :x .> globalvar
    :y .== 3
end

Evaluated output:

0×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┴──────────────

Expected output:

0×2 DataFrame


  diff = 
    Warning: Diff output requires color.
    0×2 DataFrameDataFrame
     Row │ x      y
         │ Int64  Int64
    ─────┴──────────────
julia> using DataFramesMeta, Statistics

julia> df = DataFrame(x = 1:3, y = [2, 1, 2]);
Expand Down Expand Up @@ -976,7 +987,7 @@
Use this function as an alternative to placing the `.` to broadcast row-wise operations.

### Examples
```jldoctest

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```jldoctest
julia> using DataFramesMeta

julia> df = DataFrame(A=1:5, B=["apple", "pear", "apple", "orange", "pear"])
5×2 DataFrame
 Row │ A      B
     │ Int64  String
─────┼───────────────
   1 │     1  apple
   2 │     2  pear
   3 │     3  apple
   4 │     4  orange
   5 │     5  pear

julia> @rsubset df :A > 3
2×2 DataFrame
 Row │ A      B
     │ Int64  String
─────┼───────────────
   1 │     4  orange
   2 │     5  pear

julia> @rsubset df :A > 3 || :B == "pear"
3×2 DataFrame
  Row │ A      B
      │ Int64  String
 ─────┼───────────────
    1 │     2  pear
    2 │     4  orange
    3 │     5  pear
```

Subexpression:

@rsubset df :A > 3 || :B == "pear"

Evaluated output:

3×2 DataFrame
 Row │ A      B
     │ Int64  String
─────┼───────────────
   1 │     2  pear
   2 │     4  orange
   3 │     5  pear

Expected output:

3×2 DataFrame
  Row │ A      B
      │ Int64  String
 ─────┼───────────────
    1 │     2  pear
    2 │     4  orange
    3 │     5  pear


  diff = 
    Warning: Diff output requires color.
    3×2 DataFrame
      DataFrame
     Row │ A      B
          B
         │ Int64  String
     ─────┼───────────────
        String
    ─────┼───────────────
       1 │     2  pear
        pear
       2 │     4  orange
        orange
       3 │     5  pear
julia> using DataFramesMeta

julia> df = DataFrame(A=1:5, B=["apple", "pear", "apple", "orange", "pear"])
Expand Down Expand Up @@ -1128,7 +1139,7 @@

### Examples

```jldoctest

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```jldoctest
julia> using DataFramesMeta, Statistics

julia> df = DataFrame(x = 1:3, y = [2, 1, 2]);

julia> globalvar = [2, 1, 0];

julia> @subset!(copy(df), :x .> 1)
2×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     2      1
   2 │     3      2

julia> @subset!(copy(df), :x .> globalvar)
2×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     2      1
   2 │     3      2

julia> @subset! copy(df) begin
           :x .> globalvar
           :y .== 3
       end
0×2 DataFrame

julia> df = DataFrame(n = 1:20, x = [3, 3, 3, 3, 1, 1, 1, 2, 1, 1,
                                    2, 1, 1, 2, 2, 2, 3, 1, 1, 2]);

julia> g = groupby(copy(df), :x);

julia> @subset!(g, :n .> mean(:n))
8×2 DataFrame
 Row │ n      x
     │ Int64  Int64
─────┼──────────────
   1 │    12      1
   2 │    13      1
   3 │    15      2
   4 │    16      2
   5 │    17      3
   6 │    18      1
   7 │    19      1
   8 │    20      2

julia> g = groupby(copy(df), :x);

julia> @subset! g begin
           :n .> mean(:n)
           :n .< 20
       end
7×2 DataFrame
 Row │ n      x
     │ Int64  Int64
─────┼──────────────
   1 │    12      1
   2 │    13      1
   3 │    15      2
   4 │    16      2
   5 │    17      3
   6 │    18      1
   7 │    19      1

julia> d = DataFrame(a = [1, 2, missing], b = ["x", "y", missing]);

julia> @subset!(d, :a .== 1)
1×2 DataFrame
 Row │ a       b
     │ Int64?  String?
─────┼─────────────────
   1 │      1  x
```

Subexpression:

@subset! copy(df) begin
    :x .> globalvar
    :y .== 3
end

Evaluated output:

0×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┴──────────────

Expected output:

0×2 DataFrame


  diff = 
    Warning: Diff output requires color.
    0×2 DataFrameDataFrame
     Row │ x      y
         │ Int64  Int64
    ─────┴──────────────
julia> using DataFramesMeta, Statistics

julia> df = DataFrame(x = 1:3, y = [2, 1, 2]);
Expand Down Expand Up @@ -1312,7 +1323,7 @@

### Examples

```jldoctest

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```jldoctest
julia> using DataFramesMeta, Statistics

julia> d = DataFrame(x = [3, 3, 3, 2, 1, 1, 1, 2, 1, 1], n = 1:10,
                     c = ["a", "c", "b", "e", "d", "g", "f", "i", "j", "h"]);

julia> @orderby(d, -:n)
10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1     10  h
   2 │     1      9  j
   3 │     2      8  i
   4 │     1      7  f
   5 │     1      6  g
   6 │     1      5  d
   7 │     2      4  e
   8 │     3      3  b
   9 │     3      2  c
  10 │     3      1  a

julia> @orderby(d, invperm(sortperm(:c, rev = true)))
10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      9  j
   2 │     2      8  i
   3 │     1     10  h
   4 │     1      6  g
   5 │     1      7  f
   6 │     2      4  e
   7 │     1      5  d
   8 │     3      2  c
   9 │     3      3  b
  10 │     3      1  a

julia> @orderby d begin
           :x
           abs.(:n .- mean(:n))
       end
10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      5  e
   2 │     1      6  f
   3 │     1      7  g
   4 │     1      9  i
   5 │     1     10  j
   6 │     2      4  d
   7 │     2      8  h
   8 │     3      3  c
   9 │     3      2  b
  10 │     3      1  a

julia> @orderby d @byrow :x^2
10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      5  e
   2 │     1      6  f
   3 │     1      7  g
   4 │     1      9  i
   5 │     1     10  j
   6 │     2      4  d
   7 │     2      8  h
   8 │     3      1  a
   9 │     3      2  b
  10 │     3      3  c
```

Subexpression:

@orderby d begin
    :x
    abs.(:n .- mean(:n))
end

Evaluated output:

10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      5  d
   2 │     1      6  g
   3 │     1      7  f
   4 │     1      9  j
   5 │     1     10  h
   6 │     2      4  e
   7 │     2      8  i
   8 │     3      3  b
   9 │     3      2  c
  10 │     3      1  a

Expected output:

10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      5  e
   2 │     1      6  f
   3 │     1      7  g
   4 │     1      9  i
   5 │     1     10  j
   6 │     2      4  d
   7 │     2      8  h
   8 │     3      3  c
   9 │     3      2  b
  10 │     3      1  a


  diff = 
    Warning: Diff output requires color.
    10×3 DataFrame
     Row │ x      n      c
         │ Int64  Int64  String
    ─────┼──────────────────────
       1 │     1      5  e
       d
       2 │     1      6  f
       g
       3 │     1      7  g
       f
       4 │     1      9  i
       j
       5 │     1     10  j
       h
       6 │     2      4  d
       e
       7 │     2      8  h
       i
       8 │     3      3  c
       b
       9 │     3      2  b
      c
      10 │     3      1  a

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doctest failure in ~/work/DataFramesMeta.jl/DataFramesMeta.jl/src/macros.jl:1326-1398

```jldoctest
julia> using DataFramesMeta, Statistics

julia> d = DataFrame(x = [3, 3, 3, 2, 1, 1, 1, 2, 1, 1], n = 1:10,
                     c = ["a", "c", "b", "e", "d", "g", "f", "i", "j", "h"]);

julia> @orderby(d, -:n)
10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1     10  h
   2 │     1      9  j
   3 │     2      8  i
   4 │     1      7  f
   5 │     1      6  g
   6 │     1      5  d
   7 │     2      4  e
   8 │     3      3  b
   9 │     3      2  c
  10 │     3      1  a

julia> @orderby(d, invperm(sortperm(:c, rev = true)))
10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      9  j
   2 │     2      8  i
   3 │     1     10  h
   4 │     1      6  g
   5 │     1      7  f
   6 │     2      4  e
   7 │     1      5  d
   8 │     3      2  c
   9 │     3      3  b
  10 │     3      1  a

julia> @orderby d begin
           :x
           abs.(:n .- mean(:n))
       end
10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      5  e
   2 │     1      6  f
   3 │     1      7  g
   4 │     1      9  i
   5 │     1     10  j
   6 │     2      4  d
   7 │     2      8  h
   8 │     3      3  c
   9 │     3      2  b
  10 │     3      1  a

julia> @orderby d @byrow :x^2
10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      5  e
   2 │     1      6  f
   3 │     1      7  g
   4 │     1      9  i
   5 │     1     10  j
   6 │     2      4  d
   7 │     2      8  h
   8 │     3      1  a
   9 │     3      2  b
  10 │     3      3  c
```

Subexpression:

@orderby d @byrow :x^2

Evaluated output:

10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      5  d
   2 │     1      6  g
   3 │     1      7  f
   4 │     1      9  j
   5 │     1     10  h
   6 │     2      4  e
   7 │     2      8  i
   8 │     3      1  a
   9 │     3      2  c
  10 │     3      3  b

Expected output:

10×3 DataFrame
 Row │ x      n      c
     │ Int64  Int64  String
─────┼──────────────────────
   1 │     1      5  e
   2 │     1      6  f
   3 │     1      7  g
   4 │     1      9  i
   5 │     1     10  j
   6 │     2      4  d
   7 │     2      8  h
   8 │     3      1  a
   9 │     3      2  b
  10 │     3      3  c


  diff = 
    Warning: Diff output requires color.
    10×3 DataFrame
     Row │ x      n      c
         │ Int64  Int64  String
    ─────┼──────────────────────
       1 │     1      5  e
       d
       2 │     1      6  f
       g
       3 │     1      7  g
       f
       4 │     1      9  i
       j
       5 │     1     10  j
       h
       6 │     2      4  d
       e
       7 │     2      8  h
       i
       8 │     3      1  a
       9 │     3      2  b
      c
      10 │     3      3  cb
julia> using DataFramesMeta, Statistics

julia> d = DataFrame(x = [3, 3, 3, 2, 1, 1, 1, 2, 1, 1], n = 1:10,
Expand Down Expand Up @@ -1407,7 +1418,7 @@
Use this function as an alternative to placing the `.` to broadcast row-wise operations.

### Examples
```jldoctest

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doctest failure in ~/work/DataFramesMeta.jl/DataFramesMeta.jl/src/macros.jl:1421-1458

```jldoctest
julia> using DataFramesMeta

julia> df = DataFrame(x = [8,8,-8,7,7,-7], y = [-1, 1, -2, 2, -3, 3])
6×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     8     -1
   2 │     8      1
   3 │    -8     -2
   4 │     7      2
   5 │     7     -3
   6 │    -7      3

julia> @rorderby df abs(:x) (:x * :y^3)
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     7     -3
   2 │    -7      3
   3 │     7      2
   4 │     8     -1
   5 │     8      1
   6 │    -8     -2

julia>  @rorderby df :y == 2 ? -:x : :y
6×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     7      2
   2 │     7     -3
   3 │    -8     -2
   4 │     8     -1
   5 │     8      1
   6 │    -7      3
```

Subexpression:

@rorderby df abs(:x) (:x * :y^3)

Evaluated output:

6×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     7     -3
   2 │    -7      3
   3 │     7      2
   4 │     8     -1
   5 │     8      1
   6 │    -8     -2

Expected output:

 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     7     -3
   2 │    -7      3
   3 │     7      2
   4 │     8     -1
   5 │     8      1
   6 │    -8     -2


  diff = 
    Warning: Diff output requires color.
     6×2 DataFrame
     Row │ x      y
         │ Int64  Int64
    ─────┼──────────────
       1 │     7     -3
       2 │    -7      3
       3 │     7      2
       4 │     8     -1
       5 │     8      1
       6 │    -8     -2
julia> using DataFramesMeta

julia> df = DataFrame(x = [8,8,-8,7,7,-7], y = [-1, 1, -2, 2, -3, 3])
Expand Down Expand Up @@ -2510,7 +2521,7 @@

### Examples

```jldoctest

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doctest failure in ~/work/DataFramesMeta.jl/DataFramesMeta.jl/src/macros.jl:2524-2544

```jldoctest
julia> using DataFramesMeta;

julia> df = DataFrame(x = 1:10, y = 10:-1:1);

julia> @distinct(df, :x .+ :y)
1×2 DataFrame
 Row │ x      y      
     │ Int64  Int64  
─────┼───────────────
   1 │     1      10   

julia> @distinct df begin
            :x .+ :y
        end
1×2 DataFrame
 Row │ x      y      
     │ Int64  Int64  
─────┼───────────────
   1 │     1      10   
```

Subexpression:

@distinct(df, :x .+ :y)

Evaluated output:

1×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     1     10

Expected output:

1×2 DataFrame
 Row │ x      y      
     │ Int64  Int64  
─────┼───────────────
   1 │     1      10


  diff = 
    Warning: Diff output requires color.
    1×2 DataFrame
     Row │ x      y      
         y
         │ Int64  Int64  
    ─────┼───────────────
       Int64
    ─────┼──────────────
       1 │     1      1     10

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doctest failure in ~/work/DataFramesMeta.jl/DataFramesMeta.jl/src/macros.jl:2524-2544

```jldoctest
julia> using DataFramesMeta;

julia> df = DataFrame(x = 1:10, y = 10:-1:1);

julia> @distinct(df, :x .+ :y)
1×2 DataFrame
 Row │ x      y      
     │ Int64  Int64  
─────┼───────────────
   1 │     1      10   

julia> @distinct df begin
            :x .+ :y
        end
1×2 DataFrame
 Row │ x      y      
     │ Int64  Int64  
─────┼───────────────
   1 │     1      10   
```

Subexpression:

@distinct df begin
     :x .+ :y
 end

Evaluated output:

1×2 DataFrame
 Row │ x      y
     │ Int64  Int64
─────┼──────────────
   1 │     1     10

Expected output:

1×2 DataFrame
 Row │ x      y      
     │ Int64  Int64  
─────┼───────────────
   1 │     1      10


  diff = 
    Warning: Diff output requires color.
    1×2 DataFrame
     Row │ x      y      
         y
         │ Int64  Int64  
    ─────┼───────────────
       Int64
    ─────┼──────────────
       1 │     1      1     10
julia> using DataFramesMeta;

julia> df = DataFrame(x = 1:10, y = 10:-1:1);
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2 changes: 1 addition & 1 deletion test/dataframes.jl
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Expand Up @@ -553,7 +553,7 @@ cr = "c"
@test_throws MethodError @eval @select(df, :n = sum(Between(:i, :t)))
end

@testset "with" begin
@testset "attach" begin
df = DataFrame(A = 1:3, B = [2, 1, 2])

x = [2, 1, 0]
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34 changes: 34 additions & 0 deletions test/deprecated.jl
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Expand Up @@ -198,4 +198,38 @@ end
@test (@combine(gd, n = first(:i))) ≅ newdf
end

@testset "with" begin
df = DataFrame(A = 1:3, B = [2, 1, 2])

x = [2, 1, 0]

@test @with(df, :A .+ 1) == df.A .+ 1
@test @with(df, :A .+ :B) == df.A .+ df.B
@test @with(df, :A .+ x) == df.A .+ x

x = @with df begin
res = 0.0
for i in 1:length(:A)
res += :A[i] * :B[i]
end
res
end
idx = :A
@test @with(df, $idx .+ :B) == df.A .+ df.B
idx2 = :B
@test @with(df, $idx .+ $idx2) == df.A .+ df.B
@test @with(df, $:A .+ $"B") == df.A .+ df.B

@test_throws ArgumentError @with(df, :A + $2)

@test x == sum(df.A .* df.B)
@test @with(df, df[:A .> 1, ^([:B, :A])]) == df[df.A .> 1, [:B, :A]]
@test @with(df, DataFrame(a = :A * 2, b = :A .+ :B)) == DataFrame(a = df.A * 2, b = df.A .+ df.B)

@test @with(df, :A) === df.A
@test @with(df, $:A) === df.A
@test @with(df, $"A") === df.A
end


end # module
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