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Mention DataFrameMacros.jl in the docs (#3195)
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‎docs/Project.toml

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[deps]
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CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
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CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
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Chain = "8be319e6-bccf-4806-a6f7-6fae938471bc"
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DataFrameMacros = "75880514-38bc-4a95-a458-c2aea5a3a702"
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DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
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Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
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Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"

‎docs/src/index.md

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A range of convenience functions for DataFrames.jl that augment `select` and
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`transform` to provide a user experience similar to that provided by
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[dplyr](https://dplyr.tidyverse.org/) in R.
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- [DataFrameMacros.jl](https://github.com/jkrumbiegel/DataFrameMacros.jl):
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Provides macro versions of the common DataFrames.jl functions similar to DataFramesMeta.jl,
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with convenient syntax for the manipulation of multiple columns at once.
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- [Query.jl](https://github.com/queryverse/Query.jl): Query.jl provides a single
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framework for data wrangling that works with a range of libraries, including
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DataFrames.jl, other tabular data libraries (more on those below), and even
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non-tabular data. Provides many convenience functions analogous to those in
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dplyr in R or [LINQ](https://en.wikipedia.org/wiki/Language_Integrated_Query).
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- You can find more on both of these packages in the
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- You can find more information on these packages in the
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[Data manipulation frameworks](@ref) section of this manual.
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- **And More!**
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- [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl): A pure-Julia,

‎docs/src/man/querying_frameworks.md

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# Data manipulation frameworks
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Two popular frameworks provide convenience methods to manipulate `DataFrame`s:
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DataFramesMeta.jl and Query.jl. They implement a functionality similar to
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Three frameworks provide convenience methods to manipulate `DataFrame`s:
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DataFramesMeta.jl, DataFrameMacros.jl and Query.jl. They implement a functionality similar to
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[dplyr](https://dplyr.tidyverse.org/) or
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[LINQ](https://en.wikipedia.org/wiki/Language_Integrated_Query).
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You can find more details about how this package can be used on the
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[DataFramesMeta.jl GitHub page](https://github.com/JuliaData/DataFramesMeta.jl).
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## DataFrameMacros.jl
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[DataFrameMacros.jl](https://github.com/jkrumbiegel/DataFrameMacros.jl) is
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an alternative to DataFramesMeta.jl with an additional focus on convenient
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solutions for the transformation of multiple columns at once.
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The instructions below are for version 0.3 of DataFrameMacros.jl.
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First, install the DataFrameMacros.jl package:
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```julia
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using Pkg
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Pkg.add("DataFrameMacros")
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```
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In DataFrameMacros.jl, all but the `@combine` macro are row-wise by default.
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There is also a `@groupby` which allows creating grouping columns on the fly
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using the same syntax as `@transform`, for grouping by new columns
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without writing them out twice.
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In the example below, you can also see some of DataFrameMacros.jl's multi-column
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features, where `mean` is applied to both age columns at once by selecting
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them with the `r"age"` regex. The new column names are then derived using the
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`"{}"` shortcut which splices the transformed column names into a string.
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```jldoctest dataframemacros
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julia> using DataFrames, DataFrameMacros, Chain, Statistics
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julia> df = DataFrame(name=["John", "Sally", "Roger"],
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age=[54.0, 34.0, 79.0],
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children=[0, 2, 4])
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3×3 DataFrame
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Row │ name age children
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│ String Float64 Int64
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─────┼───────────────────────────
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1 │ John 54.0 0
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2 │ Sally 34.0 2
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3 │ Roger 79.0 4
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julia> @chain df begin
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@transform :age_months = :age * 12
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@groupby :has_child = :children > 0
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@combine "mean_{}" = mean({r"age"})
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end
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2×3 DataFrame
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Row │ has_child mean_age mean_age_months
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│ Bool Float64 Float64
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─────┼──────────────────────────────────────
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1 │ false 54.0 648.0
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2 │ true 56.5 678.0
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```
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There's also the capability to reference a group of multiple columns as a single unit,
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for example to run aggregations over them, with the `{{ }}` syntax.
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In the following example, the first quarter is compared to the maximum of the other three:
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```jldoctest dataframemacros
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julia> df = DataFrame(q1 = [12.0, 0.4, 42.7],
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q2 = [6.4, 2.3, 40.9],
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q3 = [9.5, 0.2, 13.6],
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q4 = [6.3, 5.4, 39.3])
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3×4 DataFrame
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Row │ q1 q2 q3 q4
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│ Float64 Float64 Float64 Float64
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─────┼────────────────────────────────────
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1 │ 12.0 6.4 9.5 6.3
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2 │ 0.4 2.3 0.2 5.4
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3 │ 42.7 40.9 13.6 39.3
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julia> @transform df :q1_best = :q1 > maximum({{Not(:q1)}})
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3×5 DataFrame
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Row │ q1 q2 q3 q4 q1_best
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│ Float64 Float64 Float64 Float64 Bool
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─────┼─────────────────────────────────────────────
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1 │ 12.0 6.4 9.5 6.3 true
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2 │ 0.4 2.3 0.2 5.4 false
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3 │ 42.7 40.9 13.6 39.3 true
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```
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## Query.jl
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The [Query.jl](https://github.com/queryverse/Query.jl) package provides advanced

‎docs/src/man/working_with_dataframes.md

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- the [DataFramesMeta.jl](https://github.com/JuliaStats/DataFramesMeta.jl)
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package provides interfaces similar to LINQ and
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[dplyr](https://dplyr.tidyverse.org)
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- the [DataFrameMacros.jl](https://github.com/jkrumbiegel/DataFrameMacros.jl)
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package provides macros for most standard functions from DataFrames.jl,
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with convenient syntax for the manipulation of multiple columns at once.
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See the [Data manipulation frameworks](@ref) section for more information.
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