Mention DataFrameMacros.jl in the docs

docs/Project.toml

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

docs/src/index.md

@@ -120,7 +120,10 @@ integrated they are with DataFrames.jl.
       DataFrames.jl, other tabular data libraries (more on those below), and even
       non-tabular data. Provides many convenience functions analogous to those in
       dplyr in R or [LINQ](https://en.wikipedia.org/wiki/Language_Integrated_Query).
-    - You can find more on both of these packages in the
+    - [DataFrameMacros.jl](https://github.com/jkrumbiegel/DataFrameMacros.jl):
+      Provides macro versions of the common DataFrames functions similar to DataFramesMeta,
+      with convenient syntax for the manipulation of multiple columns at once.
+    - You can find more information on these packages in the
       [Data manipulation frameworks](@ref) section of this manual.
 - **And More!**
     - [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl): A pure-Julia,

docs/src/man/querying_frameworks.md

@@ -1,7 +1,7 @@
 # Data manipulation frameworks
 
-Two popular frameworks provide convenience methods to manipulate `DataFrame`s:
-DataFramesMeta.jl and Query.jl. They implement a functionality similar to
+Three frameworks provide convenience methods to manipulate `DataFrame`s:
+DataFramesMeta.jl, Query.jl and DataFrameMacros.jl. They implement a functionality similar to
 [dplyr](https://dplyr.tidyverse.org/) or
 [LINQ](https://en.wikipedia.org/wiki/Language_Integrated_Query).
 
@@ -247,3 +247,81 @@ These examples only scratch the surface of what one can do with
 referred to the [Query.jl
 documentation](http://www.queryverse.org/Query.jl/stable/) for more
 information.
+
+## DataFrameMacros.jl
+
+[DataFrameMacros.jl](https://github.com/jkrumbiegel/DataFrameMacros.jl) is
+an alternative to `DataFramesMeta.jl` with an additional focus on convenient
+solutions for the transformation of multiple columns at once.
+The instructions below are for version 0.3 of DataFrameMacros.jl.
+
+First, install the DataFrameMacros.jl package:
+
+```julia
+using Pkg
+Pkg.add("DataFrameMacros")
+```
+
+In DataFrameMacros.jl, all but the `@combine` macro are row-wise by default.
+There is also a `@groupby` that works like a `@transform` with `groupby` together,
+for grouping by new columns without writing them out twice.
+
+In the below example, you can also see some of DataFrameMacros' multi-column
+features, where `mean` is applied to both age columns at once by selecting
+them with the `r"age"` regex. The new column names are then derived using the
+`"{}"` shortcut which splices the transformed column names into a string.
+
+```jldoctest dataframemacros
+using DataFrames, DataFrameMacros, Chain, Statistics
+
+julia> df = DataFrame(name=["John", "Sally", "Roger"],
+                             age=[54.0, 34.0, 79.0],
+                             children=[0, 2, 4])
+3×3 DataFrame
+ Row │ name    age      children 
+     │ String  Float64  Int64    
+─────┼───────────────────────────
+   1 │ John       54.0         0
+   2 │ Sally      34.0         2
+   3 │ Roger      79.0         4
+
+julia> @chain df begin
+           @transform :age_months = :age * 12
+           @groupby :has_child = :children > 0
+           @combine "mean_{}" = mean({r"age"})
+       end
+2×3 DataFrame
+ Row │ has_child  mean_age  mean_age_months 
+     │ Bool       Float64   Float64         
+─────┼──────────────────────────────────────
+   1 │     false      54.0            648.0
+   2 │      true      56.5            678.0
+```
+
+There's also the capability to reference a group of multiple columns as a single unit,
+for example to run aggregations over them, with the `{{ }}` syntax.
+In the following example, the first quarter is compared to the maximum of the other three:
+
+```jldoctest dataframemacros
+julia> df = DataFrame(
+           q1 = [12.0, 0.4, 42.7],
+           q2 = [6.4, 2.3, 40.9],
+           q3 = [9.5, 0.2, 13.6],
+           q4 = [6.3, 5.4, 39.3])
+3×4 DataFrame
+ Row │ q1       q2       q3       q4      
+     │ Float64  Float64  Float64  Float64 
+─────┼────────────────────────────────────
+   1 │    12.0      6.4      9.5      6.3
+   2 │     0.4      2.3      0.2      5.4
+   3 │    42.7     40.9     13.6     39.3
+
+julia> @transform df :q1_best = :q1 > maximum({{Not(:q1)}})
+3×5 DataFrame
+ Row │ q1       q2       q3       q4       q1_best 
+     │ Float64  Float64  Float64  Float64  Bool    
+─────┼─────────────────────────────────────────────
+   1 │    12.0      6.4      9.5      6.3     true
+   2 │     0.4      2.3      0.2      5.4    false
+   3 │    42.7     40.9     13.6     39.3     true
+```

docs/src/man/working_with_dataframes.md

@@ -738,6 +738,9 @@ operations:
 - the [DataFramesMeta.jl](https://github.com/JuliaStats/DataFramesMeta.jl)
   package provides interfaces similar to LINQ and
   [dplyr](https://dplyr.tidyverse.org)
+- the [DataFrameMacros.jl](https://github.com/jkrumbiegel/DataFrameMacros.jl)
+  package provides macros for most standard functions from DataFrames.jl,
+  with convenient syntax for the manipulation of multiple columns at once.
 
 See the [Data manipulation frameworks](@ref) section for more information.