Update README

README.md

@@ -20,50 +20,71 @@ Load in the dependencies and construct some toy data.
 ```julia
 julia> using DataFrames, FeatureTransforms
 
-julia> df = DataFrame(:a=>[1, 2, 3, 4, 5], :b=>[5, 4, 3, 2, 1], :c=>[0, 1, 0, 1, 0])
+julia> df = DataFrame(:a=>[1, 2, 3, 4, 5], :b=>[5, 4, 3, 2, 1], :c=>[2, 1, 3, 1, 3])
 5×3 DataFrame
- Row │ a      b      c
-     │ Int64  Int64  Int64
+ Row │ a      b      c     
+     │ Int64  Int64  Int64 
 ─────┼─────────────────────
-   1 │     1      5      0
+   1 │     1      5      2
    2 │     2      4      1
-   3 │     3      3      0
+   3 │     3      3      3
    4 │     4      2      1
-   5 │     5      1      0
+   5 │     5      1      3
 ```
 
-We construct the transformations that we want to `apply` to the data, which can be non-mutating (`apply`) or mutating (`apply!`) if supported.
-Note that non-mutating transformations do not necessarily return the same type, even when applied to all the elements.
+Next, we construct the `Transform` that we want to `apply` to the data, which can either be non-mutating (`apply`) or mutating (`apply!`).
+All `Transforms` support the non-mutating `apply` method any `Transform` that changes the type or dimension of the input does not support mutation.
+
+In either case, the return will be the same type as the input.
+So if you provide an `Array` you get back an `Array`, and if you provide a `Table` you will get back a `Table`.
+Here we are working with a `DataFrame`, so the return will always be a `DataFrame`:
 ```julia
 julia> p = Power(3);
 
-julia> FeatureTransforms.apply(df, p; cols=[:a])
-1-element Array{Array{Int64,1},1}:
- [1, 8, 27, 64, 125]
+julia> FeatureTransforms.apply(df, p; cols=[:a], header=[:a3])
+5×1 DataFrame
+ Row │ a3    
+     │ Int64 
+─────┼───────
+   1 │     1
+   2 │     8
+   3 │    27
+   4 │    64
+   5 │   125
 
 julia> FeatureTransforms.apply!(df, p; cols=[:a])
 5×3 DataFrame
  Row │ a      b      c
      │ Int64  Int64  Int64
 ─────┼─────────────────────
-   1 │     1      5      0
+   1 │     1      5      2
    2 │     8      4      1
-   3 │    27      3      0
+   3 │    27      3      3
    4 │    64      2      1
-   5 │   125      1      0
+   5 │   125      1      3
 ```
 
-Also note that some transformations, such as those applying a reduction operation, do not support mutation.
-But users may append the output to their data if they so wish.
+
+`Transform`s that don't support mutation must be called using `apply` and appended.
+To help with this, you can call the `Transform` type directly:
 ```julia
+julia> ohe = OneHotEncoding(1:3);
+
 julia> lc = LinearCombination([1, -10]);
 
-julia> FeatureTransforms.apply(df, lc; cols=[:b, :c])
-5-element Array{Int64,1}:
-  5
- -6
-  3
- -8
-  1
+julia> ohe_df = ohe(df; cols=[:c], header=[:cat1, :cat2, :cat3])
+
+julia> lc_df = lc(df; cols=[:a, :b], header=[:ab]);
+
+julia> df = hcat(df, lc_df, ohe_df)
+5×7 DataFrame
+ Row │ a      b      c      ab     cat1   cat2   cat3  
+     │ Int64  Int64  Int64  Int64  Bool   Bool   Bool  
+─────┼─────────────────────────────────────────────────
+   1 │     1      5      2    -49  false   true  false
+   2 │     8      4      1    -32   true  false  false
+   3 │    27      3      3     -3  false  false   true
+   4 │    64      2      1     44   true  false  false
+   5 │   125      1      3    115  false  false   true
 
 ```