# FilteredGroupbyMacro.jl

## Filtered split-apply-combine as in R's data.table

FilteredGroupbyMacro.jl offers a macro @by with which split-apply-combine operations on filtered data frames can be expressed concisely. It is very similar in nature to the [i,j,by] indexing that the well-known package data.table in the R ecosystem uses.

The order of arguments is slightly different. Here, you have to specify:

[filter, grouping_keys, new_column_keyword_args...]

An example with the well-known diamonds dataset:

using RDatasets
using FilteredGroupbyMacro
using StatsBase

diamonds = dataset("ggplot2", "diamonds")

# filter by Price and Carat
# then group by Cut
# finally compute new columns with keyword names
@by diamonds[(:Price .> 3000) .& (:Carat .> 0.3), :Cut,
MeanPricePerCarat = mean(:Price) / mean(:Carat),
MostFreqColor = sort(collect(countmap(:Color)), by = last)[end][1]]

5 rows × 3 columns

CutMeanPricePerCaratMostFreqColor
Categorical…Float64Categorical…
1Fair4791.49H
2Good5434.26G
3Very Good5926.21G
5Ideal6308.08G

Internally, the macro transforms the indexing syntax to the functional equivalent of the following standard DataFrames function calls:

by(diamonds[(diamonds.Price .> 3000) .& (diamonds.Carat .> 0.3), :], :Cut,
MeanPricePerCarat = (:Price, :Carat) => x -> mean(x.Price) / mean(x.Carat),
MostFreqColor = :Color => x -> sort(collect(countmap(x)), by = last)[end][1])

5 rows × 3 columns

CutMeanPricePerCaratMostFreqColor
Categorical…Float64Categorical…
1Fair4791.49H
2Good5434.26G
3Very Good5926.21G
5Ideal6308.08G

As you can see there are a couple of redundancies in the default syntax. Especially for computations using multiple columns, the standard new_column = columns => function syntax is much more verbose and less readable.

## Assignment syntax

You can also use assignment syntax with the := operator. This is not a mutating operation as in R's data.table but returns a new DataFrame, in which the result of the split-apply-combine operation is joined with the original data. This is handy if you want to keep working with a full dataset after calculating group-wise summary statistics which are then repeated for each group row.

df = DataFrame(a = repeat(1:3, 3), b = repeat('a':'c', 3))
@by df[!, :b, sum_a := sum(:a)]

9 rows × 3 columns

absum_a
Int64CharInt64
11'a'3
22'b'6
33'c'9
41'a'3
52'b'6
63'c'9
71'a'3
82'b'6
93'c'9

Compare this to the non-assignment syntax:

@by df[!, :b, sum_a = sum(:a)]

3 rows × 2 columns

bsum_a
CharInt64
1'a'3
2'b'6
3'c'9