# \[ANN\] FilteredGroupbyMacro.jl

**URL:** https://discourse.julialang.org/t/ann-filteredgroupbymacro-jl/34373
**Category:** Package Announcements
**Created:** [February 9, 2020, 12:18pm UTC](https://discourse.julialang.org/t/ann-filteredgroupbymacro-jl/34373 "2020-02-09T12:18:22Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![jules](https://avatars.discourse-cdn.com/v4/letter/j/41988e/32.png) [@jules](https://discourse.julialang.org/u/jules)
#### Post date: [February 9, 2020, 12:18pm UTC](https://discourse.julialang.org/t/ann-filteredgroupbymacro-jl/34373/1 "2020-02-09T12:18:22Z")

</div>

This is a really simple package (my first macro package) that does only one thing. It exports the macro `@by` which gives you the combination of filtering and split-apply-combine approach of R’s data.table (or something approximating that at least), with `DataFrames`’ function `by` doing the work behind the scenes.

I’ve missed having this concise syntax for a while, as it ticks the most common boxes for what I do with DataFrames and needs no redundant column or dataframe variable names, or complex signatures like `newcol = (:columnA, :columnB) => x -> x.columnA .+ x.columnB`.

The package is waiting for inclusion in the GeneralRegistry right now, so until then you need to install it via:

```julia
]add https://github.com/jkrumbiegel/FilteredGroupbyMacro.jl

```

Here are the docs: [Home · FilteredGroupbyMacro.jl](https://jkrumbiegel.github.io/FilteredGroupbyMacro.jl/dev/)

But here’s also already a short example from the README:

```julia
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:

```julia
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:

```julia
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)]

```