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Fix keyword argument bug #400

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merged 3 commits into from
Jul 16, 2024
Merged

Fix keyword argument bug #400

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71f31cb
fix
pdeffebach Jun 26, 2024
d2f3982
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pdeffebach Jun 29, 2024
bbe4ec2
add news and modify project.toml
pdeffebach Jul 16, 2024
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3 changes: 3 additions & 0 deletions NEWS.md
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Original file line number Diff line number Diff line change
@@ -1,3 +1,6 @@
# DataFramesMeta v0.15.3 Release Notes
* Fixed a bug ([#399](https://github.com/JuliaData/DataFramesMeta.jl/issues/399)) where keyword arguments were accidentally ignored ([#400](https://github.com/JuliaData/DataFramesMeta.jl/pull/400#pullrequestreview-2180944667))

# DataFramesMeta v0.15.2 Release notes
* Bumped the Chain.jl compat entry in the Project.toml ([#382](https://github.com/JuliaData/DataFramesMeta.jl/pull/391))

Expand Down
2 changes: 1 addition & 1 deletion Project.toml
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@@ -1,6 +1,6 @@
name = "DataFramesMeta"
uuid = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
version = "0.15.2"
version = "0.15.3"

[deps]
Chain = "8be319e6-bccf-4806-a6f7-6fae938471bc"
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1 change: 0 additions & 1 deletion src/macros.jl
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Expand Up @@ -759,7 +759,6 @@

function subset_helper(x, args...)
x, exprs, outer_flags, kw = get_df_args_kwargs(x, args...; wrap_byrow = false)

t = (fun_to_vec(ex; no_dest=true, outer_flags=outer_flags) for ex in exprs)
quote
$subset($x, $(t...); (skipmissing = true,)..., $(kw...))
Expand Down Expand Up @@ -860,7 +859,7 @@

### 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 +975,7 @@
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 +1127,7 @@

### 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 +1311,7 @@

### 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:1314-1386

```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 +1406,7 @@
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:1409-1446

```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 @@ -2530,7 +2529,7 @@

### Examples

```jldoctest

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doctest failure in ~/work/DataFramesMeta.jl/DataFramesMeta.jl/src/macros.jl:2532-2552

```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:2532-2552

```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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33 changes: 22 additions & 11 deletions src/parsing.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -499,22 +499,35 @@ end

function get_df_args_kwargs(x, args...; wrap_byrow = false)
kw = []
# x is normally a data frame. But if the call looks like
# transform(df, :x = 1; copycols = false)
# then x is actually Expr(:parameters, Expr(:kw, :copycole, false))
# When this happens, we assign x to the data frame, use only
# the rest of the args, and keep trask of the keyword argument.
if x isa Expr && x.head === :parameters
append!(kw, x.args)
x = first(args)
args = args[2:end]
end

transforms, outer_flags, kw = create_args_vector!(kw, args...; wrap_byrow = wrap_byrow)
if args isa Tuple
blockarg = Expr(:block, args...)
else
blockarg = args
end

return (x, transforms, outer_flags, kw)
end
# create_args_vector! has an exclamation point because
# we modify the keyword arguments kw
transforms, outer_flags, kw = create_args_vector!(kw, blockarg; wrap_byrow = wrap_byrow)

function create_args_vector!(kw, args...; wrap_byrow::Bool=false)
create_args_vector!(kw, Expr(:block, args...); wrap_byrow = wrap_byrow)
return (x, transforms, outer_flags, kw)
end

function get_kw_from_macro_call(e::Expr)
if length(e.args) != 3
throw(ArgumentError("Invalid @kwarg expression"))
end

nv = e.args[3]

return nv
Expand All @@ -532,8 +545,6 @@ the block as an array. If a simple expression,
wrap the expression in a one-element vector.
"""
function create_args_vector!(kw, arg; wrap_byrow::Bool=false)
# TODO: Pass vector of keyword arguments to this function
# and modify by detecting presence of `@kwarg`.
arg, outer_flags = extract_macro_flags(MacroTools.unblock(arg))

if wrap_byrow
Expand All @@ -545,18 +556,18 @@ function create_args_vector!(kw, arg; wrap_byrow::Bool=false)
end

# @astable means the whole block is one transformation

if arg isa Expr && arg.head == :block && !outer_flags[ASTABLE_SYM][]
x = MacroTools.rmlines(arg).args
kw = []
transforms = []
seen_kw = false
seen_kw_macro = false
for xi in x
if is_macro_head(xi, "@kwarg")
kw_item = get_kw_from_macro_call(xi)
push!(kw, kw_item)
seen_kw = true
seen_kw_macro = true
else
if seen_kw
if seen_kw_macro
throw(ArgumentError("@kwarg calls must be at end of block"))
end
push!(transforms, xi)
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24 changes: 24 additions & 0 deletions test/keyword_arguments.jl
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Expand Up @@ -401,4 +401,28 @@ end
@test df2 == correct
end

@testset "Multiple arguments #399" begin
correct = df[df.a .== 1, :]
correct_view = view(df, df.a .== 1, :)

df2 = @subset(df, :a .== 1, :b .== 3; view = true)
@test df2 ≈ correct_view
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Can you additionally check type of df2?

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Sorry, I missed this!


@test_throws ArgumentError @subset(df, :a .== 1, :b .== 3; skipmissing = false)
@test_throws ArgumentError @subset(df, :a .== 1, :b .== 3; skipmissing = false, view = true)

correct = transform(df, :a => ByRow(t -> t + 1) => :y, :b => ByRow(t -> t + 2) => :z)
df2 = @rtransform(df, :y = :a + 1, :z = :b + 2; copycols = false)
@test df2 ≅ correct
@test df.a === df2.a

correct = DataFrame(b_mean = [3.5, 5.0], b_first = [3, 5])
df2 = @combine(gd, :b_mean = mean(skipmissing(:b)), :b_first = first(:b); keepkeys = false)
@test df2 ≅ correct
end

@testset "@kwarg errors" begin

end

end # module
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