FilteredGroupbyMacro.jl offers a macro @by with which a concise syntax for filtered
split-apply-combine operations 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 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]]Compare this to the default DataFrames syntax:
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])You can also use assignment syntax to join the groupby result with the filtered table:
using FilteredGroupbyMacro
using DataFrames
df = DataFrame(a = repeat(1:3, 3), b = repeat('a':'c', 3))
# the result of this will be df with a new column sum_a
# that contains the same sum_a for every row in each group based on :b
@by df[!, :b, sum_a := sum(:a)]