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Add @when for conditional transformations #376

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pdeffebach Apr 9, 2023
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Merge remote-tracking branch 'origin/master' into when_feature
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Merge remote-tracking branch 'origin/master' into when_feature
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1 change: 1 addition & 0 deletions docs/src/index.md
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@ In addition, DataFramesMeta provides
* `@byrow` for applying functions to each row of a data frame (only supported inside other macros).
* `@passmissing` for propagating missing values inside row-wise DataFramesMeta.jl transformations.
* `@astable` to create multiple columns within a single transformation.
* `@when` to non-destructively work with a subset of observations (Similar to Stata's `if`)
* `@chain`, from [Chain.jl](https://github.com/jkrumbiegel/Chain.jl) for piping the above macros together, similar to [magrittr](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html)'s
`%>%` in R.
* `@label!` and `@note!` for attaching metadata to columns.
Expand Down
2 changes: 1 addition & 1 deletion src/DataFramesMeta.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@ export @with,
@rtransform, @rselect, @rtransform!, @rselect!,
@distinct, @rdistinct, @distinct!, @rdistinct!,
@eachrow, @eachrow!,
@byrow, @passmissing, @astable, @kwarg,
@byrow, @passmissing, @astable, @kwarg, @when,
@label!, @note!, printlabels, printnotes,
@groupby,
@based_on, @where # deprecated
Expand Down
202 changes: 152 additions & 50 deletions src/macros.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -547,11 +547,33 @@
"is currently allowed with $DOLLAR"))

function with_helper(d, body)
# Get rid of the leading @byrow, @passmissing etc.
# but otherwise leave body untouched
body, outer_flags = extract_macro_flags(body)
if outer_flags[ASTABLE_SYM][]
throw(ArgumentError("@astable macro-flag cannot be used inside of @with"))
end
# If we have a begin...end somewhere, we might
# have a @when.
# Remove the @when statements, recording that they
# exist. To do this we also have to de-construct
# body into a vector expressions.
es, when = get_when_statements(MacroTools.rmlines(MacroTools.block(body)).args)
newbody = Expr(:block, es...)
# Make body an expression to force the
# complicated method of fun_to_vec
# in the case of QuoteNode
t = fun_to_vec(Expr(:block, body); no_dest=true)
:($exec($d, $t))
t = fun_to_vec(newbody; no_dest=true, outer_flags = outer_flags)
if !isnothing(when)
w = fun_to_vec(when; no_dest = true, gensym_names=false, outer_flags = outer_flags)
z = gensym()
quote
$z = $subset($d, $w; view = true, skipmissing = true)
$exec($z, $t)
end
else
:($exec($d, $t))
end
end

"""
Expand Down Expand Up @@ -663,6 +685,83 @@
esc(with_helper(d, body))
end

"""
@when(args...)

Perform operations on a subset of `df`, but still
return a data frame with the same number of rows as `df`. `@when` can be used
with the `@transform` macros, `@select` macros, and `@with`.

`@when` is not a "real" macro. It is only functional inside DataFramesMeta.jl macros.
A motivating example:

```
@rtransform df begin
@when :a == 1
:y = :y - mean(:y)
end
```

The above block generates the column `:y` which is de-meaned with respect to observations where
`:a == 1`. If `:y` already exists in `df`, then new values over-write old values only
when `:a == 1`. If `:y` does not already exist in `df`, then new values are written
when `:a == 1`, and remaining values are filled with `missing`.

Only one `@when` statement is allowed per transformation macro and it must be the
first argument in the transformation.

`@when` inherits `@byrow` and `@passmissing` from the transformation. As an example:

```
@transform df @byrow begin
@when :a == 1
...
end
```

In the above, the condition inside `@when` operates row-wise. However, `@byrow` and `@passmissing` can
also be passed independently, such as `@byrow @when :a == 1`.

Like `@subset`, `@when` drops rows where `missing` values are returned. Unlike `@subset`,
there is currently no way to control this behavior.

## Details

`@when` operates by calling `select` with the `view = true` keyword argument,
followed by a `transform!` call. See `?transform!` for more details. Roughly,
the expression

```
@transform df begin
@when :a .== 1
:y = 5
end
```

translates to

```
df1 = @subset(copy(df), :a .== 1; view = true)
df2 = @transform! df1 :y = 5
parent(df2)
```

Unlike the other macro-flags, such as `@passmissing` and `@byrow`, `@when` cannot be
used at the top-level.
```
@transform df @byrow @when(:a == 1) begin
:x = 1
:y = 2
end
```
is not supported.

"""
macro when(args...)
throw(ArgumentError("@passmissing only works inside DataFramesMeta macros."))
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 +959,7 @@

### 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 +1075,7 @@
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:1078-1108

```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 +1227,7 @@

### 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 +1411,7 @@

### 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:1414-1486

```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 +1506,7 @@
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:1509-1546

```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 @@ -1456,15 +1555,53 @@
## transform & @transform
##
##############################################################################
copy_gd(x::GroupedDataFrame) = transform(x; ungroup = false)
copy_gd(x::AbstractDataFrame) = copy(x)
function generic_transform_select_helper(x, args...; wrap_byrow::Bool = false, modify::Bool = false, selectfun::Bool = false)
if selectfun
secondstagefun = select!
if modify
transformfun = select!
else
transformfun = select
end
else
secondstagefun = transform!
if modify
transformfun = transform!
else
transformfun = transform
end
end

x, exprs, outer_flags, kw = get_df_args_kwargs(x, args...; wrap_byrow = wrap_byrow)
exprs, when = get_when_statements(exprs)
# Main.@infiltrate
if !isnothing(when)
w = fun_to_vec(when; no_dest = true, gensym_names=false, outer_flags=outer_flags)
t = (fun_to_vec(ex; gensym_names=false, outer_flags=outer_flags) for ex in exprs)
z = gensym()
if modify
quote
$z = $subset($x, $w; view = true, skipmissing = true)
$parent($secondstagefun($z, $(t...); $(kw...)))
end
else
quote
$z = $subset($copy_gd($x), $w; view = true, skipmissing = true)
$parent($secondstagefun($z, $(t...); $(kw...)))
end
end
else
t = (fun_to_vec(ex; gensym_names=false, outer_flags=outer_flags) for ex in exprs)
quote
$transformfun($x, $(t...); $(kw...))
end
end
end

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

t = (fun_to_vec(ex; gensym_names = false, outer_flags = outer_flags) for ex in exprs)
quote
$transform($x, $(t...); $(kw...))
end
generic_transform_select_helper(x, args...; wrap_byrow = false, modify = false)
end

"""
Expand Down Expand Up @@ -1593,12 +1730,7 @@
end

function rtransform_helper(x, args...)
x, exprs, outer_flags, kw = get_df_args_kwargs(x, args...; wrap_byrow = true)

t = (fun_to_vec(ex; gensym_names=false, outer_flags=outer_flags) for ex in exprs)
quote
$transform($x, $(t...); $(kw...))
end
generic_transform_select_helper(x, args...; wrap_byrow = true, modify = false)
end

"""
Expand Down Expand Up @@ -1646,12 +1778,7 @@


function transform!_helper(x, args...)
x, exprs, outer_flags, kw = get_df_args_kwargs(x, args...; wrap_byrow = false)

t = (fun_to_vec(ex; gensym_names = false, outer_flags = outer_flags) for ex in exprs)
quote
$transform!($x, $(t...); $(kw...))
end
generic_transform_select_helper(x, args...; wrap_byrow = false, modify = true)
end

"""
Expand Down Expand Up @@ -1760,12 +1887,7 @@
end

function rtransform!_helper(x, args...)
x, exprs, outer_flags, kw = get_df_args_kwargs(x, args...; wrap_byrow = true)

t = (fun_to_vec(ex; gensym_names=false, outer_flags=outer_flags) for ex in exprs)
quote
$transform!($x, $(t...); $(kw...))
end
generic_transform_select_helper(x, args...; wrap_byrow = true, modify = true)
end

"""
Expand All @@ -1784,12 +1906,7 @@
##############################################################################

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

t = (fun_to_vec(ex; gensym_names = false, outer_flags = outer_flags, allow_multicol = true) for ex in exprs)
quote
$select($x, $(t...); $(kw...))
end
generic_transform_select_helper(x, args...; wrap_byrow = false, modify = false, selectfun = true)
end

"""
Expand Down Expand Up @@ -1929,12 +2046,7 @@
end

function rselect_helper(x, args...)
x, exprs, outer_flags, kw = get_df_args_kwargs(x, args...; wrap_byrow = true)

t = (fun_to_vec(ex; gensym_names=false, outer_flags=outer_flags) for ex in exprs)
quote
$select($x, $(t...); $(kw...))
end
generic_transform_select_helper(x, args...; wrap_byrow = true, modify = false, selectfun = true)
end

"""
Expand Down Expand Up @@ -1982,12 +2094,7 @@
##############################################################################

function select!_helper(x, args...)
x, exprs, outer_flags, kw = get_df_args_kwargs(x, args...; wrap_byrow = false)

t = (fun_to_vec(ex; gensym_names = false, outer_flags = outer_flags) for ex in exprs)
quote
$select!($x, $(t...); $(kw...))
end
generic_transform_select_helper(x, args...; wrap_byrow = false, modify = true, selectfun = true)
end

"""
Expand Down Expand Up @@ -2118,12 +2225,7 @@
end

function rselect!_helper(x, args...)
x, exprs, outer_flags, kw = get_df_args_kwargs(x, args...; wrap_byrow = true)

t = (fun_to_vec(ex; gensym_names=false, outer_flags=outer_flags) for ex in exprs)
quote
$select!($x, $(t...); $(kw...))
end
generic_transform_select_helper(x, args...; wrap_byrow = true, modify = true, selectfun = true)
end

"""
Expand Down Expand Up @@ -2530,7 +2632,7 @@

### Examples

```jldoctest

Check failure on line 2635 in src/macros.jl

View workflow job for this annotation

GitHub Actions / build 

doctest failure in ~/work/DataFramesMeta.jl/DataFramesMeta.jl/src/macros.jl:2635-2655

```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

Check failure on line 2635 in src/macros.jl

View workflow job for this annotation

GitHub Actions / build 

doctest failure in ~/work/DataFramesMeta.jl/DataFramesMeta.jl/src/macros.jl:2635-2655

```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);
Expand Down
54 changes: 54 additions & 0 deletions src/parsing.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -179,6 +179,7 @@ is_macro_head(ex::Expr, name) = ex.head == :macrocall && ex.args[1] == Symbol(na
const BYROW_SYM = Symbol("@byrow")
const PASSMISSING_SYM = Symbol("@passmissing")
const ASTABLE_SYM = Symbol("@astable")
const WHEN_SYM = Symbol("@when")
const DEFAULT_FLAGS = (;BYROW_SYM => Ref(false), PASSMISSING_SYM => Ref(false), ASTABLE_SYM => Ref(false))

extract_macro_flags(ex, exprflags = deepcopy(DEFAULT_FLAGS)) = (ex, exprflags)
Expand All @@ -191,6 +192,9 @@ function extract_macro_flags(ex::Expr, exprflags = deepcopy(DEFAULT_FLAGS))
throw(ArgumentError("Redundant flag $macroname used."))
end
exprflag[] = true
if length(ex.args) > 3
throw(ArgumentError("Too many arguments passed to $macroname"))
end
return extract_macro_flags(MacroTools.unblock(ex.args[3]), exprflags)
else
return (ex, exprflags)
Expand All @@ -199,6 +203,56 @@ function extract_macro_flags(ex::Expr, exprflags = deepcopy(DEFAULT_FLAGS))
return (ex, exprflags)
end

"""
omit_nested_when(ex::Expr, when = Ref(false))

For a statement of the form `@passmissing @when x` return `@passmissing x` and
a flag signifying a `@when` statement was present.
"""
function omit_nested_when(ex::Expr, when = Ref(false))
if ex.head == :macrocall && ex.args[1] in keys(DEFAULT_FLAGS) || is_macro_head(ex, "@when")
macroname = ex.args[1]
if length(ex.args) > 3
throw(ArgumentError("Too many arguments passed to $macroname"))
end
if macroname == Symbol("@when")
when[] = true
return omit_nested_when(MacroTools.unblock(ex.args[3]), when)
else
new_expr, when = omit_nested_when(MacroTools.unblock(ex.args[3]), when)
ex.args[3] = new_expr
end
end
return ex, when
end
omit_nested_when(ex, when = Ref(false)) = ex, when

function get_when_statements(exprs)
new_exprs = []
when_statement = nothing
seen_non_when = false
seen_when = false
for expr in exprs
e, when = omit_nested_when(expr)
if when[]
if seen_when
throw(ArgumentError("Only one @when statement allowed at a time"))
end
if seen_non_when
throw(ArgumentError("All @when statements must come first"))
end
seen_when = true
when_statement = e
else
seen_non_when = true
push!(new_exprs, expr)
end
end

new_exprs, when_statement
end


"""
check_macro_flags_consistency(exprflags)

Expand Down
1 change: 1 addition & 0 deletions test/runtests.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@ my_tests = ["dataframes.jl",
"astable.jl",
"astable_flag.jl",
"passmissing.jl",
"when.jl",
"multicol.jl"]

println("Running tests:")
Expand Down
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