Add transformation and renaming to select and select!

docs/src/lib/types.md

@@ -49,6 +49,9 @@ The `RepeatedVector` and `StackedVector` types are subtypes of `AbstractVector`
 with the exception that they are read only. Note that they are not exported and should not be constructed directly,
 but they are columns of a `DataFrame` returned by `stack` with `view=true`.
 
+The `ByRow` type is a special type used for selection operations to signal that the wrapped function should be applied
+to each element (row) of the selection.
+
 ## [The design of handling of columns of a `DataFrame`](@id man-columnhandling)
 
 When a `DataFrame` is constructed columns are copied by default. You can disable
@@ -103,6 +106,7 @@ without caution because:
 
 ```@docs
 AbstractDataFrame
+ByRow
 DataFrame
 DataFrameRow
 GroupedDataFrame

docs/src/man/getting_started.md

@@ -522,34 +522,64 @@ julia> df[in.(df.A, Ref([1, 5, 601])), :]
 │ 3   │ 601   │ 7     │ 301   │
 ```
 
-Equivalently, the `in` function can be called with a single argument to create a function object that tests whether each value belongs to the subset (partial application of `in`): `df[in([1, 5, 601]).(df.A), :]`.
+Equivalently, the `in` function can be called with a single argument to create
+a function object that tests whether each value belongs to the subset
+(partial application of `in`): `df[in([1, 5, 601]).(df.A), :]`.
 
 #### Column selection using `select` and `select!`
 
-You can also use the [`select`](@ref) and [`select!`](@ref) functions to select columns in a data frame.
+You can also use the [`select`](@ref) and [`select!`](@ref) functions to select,
+rename and transform columns in a data frame.
 
 The `select` function creates a new data frame:
 ```jldoctest dataframe
-julia> df = DataFrame(x1=1, x2=2, y=3)
-1×3 DataFrame
+julia> df = DataFrame(x1=[1, 2], x2=[3, 4], y=[5, 6])
+2×3 DataFrame
 │ Row │ x1    │ x2    │ y     │
 │     │ Int64 │ Int64 │ Int64 │
 ├─────┼───────┼───────┼───────┤
-│ 1   │ 1     │ 2     │ 3     │
+│ 1   │ 1     │ 3     │ 5     │
+│ 2   │ 2     │ 4     │ 6     │
 
 julia> select(df, Not(:x1)) # drop column :x1 in a new data frame
-1×2 DataFrame
+2×2 DataFrame
 │ Row │ x2    │ y     │
 │     │ Int64 │ Int64 │
 ├─────┼───────┼───────┤
-│ 1   │ 2     │ 3     │
+│ 1   │ 3     │ 5     │
+│ 2   │ 4     │ 6     │
 
 julia> select(df, r"x") # select columns containing 'x' character
-1×2 DataFrame
+2×2 DataFrame
 │ Row │ x1    │ x2    │
 │     │ Int64 │ Int64 │
 ├─────┼───────┼───────┤
-│ 1   │ 1     │ 2     │
+│ 1   │ 1     │ 3     │
+│ 2   │ 2     │ 4     │
+
+julia> select(df, :x1 => :a1, :x2 => :a2) # rename columns
+2×2 DataFrame
+│ Row │ a1    │ a2    │
+│     │ Int64 │ Int64 │
+├─────┼───────┼───────┤
+│ 1   │ 1     │ 3     │
+│ 2   │ 2     │ 4     │
+
+julia> select(df, :x1, :x2 => (x -> x .- minimum(x)) => :x2) # transform columns
+2×2 DataFrame
+│ Row │ x1    │ x2    │
+│     │ Int64 │ Int64 │
+├─────┼───────┼───────┤
+│ 1   │ 1     │ 0     │
+│ 2   │ 2     │ 1     │
+
+julia> select(df, :x2, :x2 => ByRow(sqrt)) # transform columns by row
+2×2 DataFrame
+│ Row │ x2    │ x2_sqrt │
+│     │ Int64 │ Float64 │
+├─────┼───────┼─────────┤
+│ 1   │ 3     │ 1.73205 │
+│ 2   │ 4     │ 2.0     │
 ```
 
 It is important to note that `select` always returns a data frame,

src/DataFrames.jl

@@ -17,6 +17,7 @@ import DataAPI,
 export AbstractDataFrame,
        All,
        Between,
+       ByRow,
        DataFrame,
        DataFrame!,
        DataFrameRow,
@@ -83,6 +84,7 @@ include("dataframerow/utils.jl")
 
 include("other/broadcasting.jl")
 
+include("abstractdataframe/selection.jl")
 include("abstractdataframe/iteration.jl")
 include("abstractdataframe/join.jl")
 include("abstractdataframe/reshape.jl")