Simple transformation of a dataframe with grouped data

New to Julia
2

Yes, unlike dplyr, DataFramesMeta.jl does not allow using previously-created variables in the same call.

You would think the solution is to use the @astable macro-flag, which lets you create multiple intermediate variables and return a NamedTuple. But this is not the solution

julia> @by culture [:year, :ind] begin
           @astable begin
               :total = sum(:value)
               :w = :value ./ :total
           end
       end
ERROR: ArgumentError: mixing single values and vectors in a named tuple is not allowed

This is because :total is a scalar while :w is a Vector. When returning a NamedTuple you can’t mix scalars and vectors. See some discussion here.

I think what you want is a @chain with multiple @transform calls, the first one takes in a GroupedDataFrame but preserves the number of rows in the data frame (unlike dplyr, the output drops the grouping by default, which I think is very convenient).

julia> @chain culture begin
           @groupby [:year, :ind]
           @transform :total = sum(:value) # This gets "spread"
           @rtransform :w = :value / :total
       end
6×5 DataFrame
 Row │ ind    year   value  total  w        
     │ Int64  Int64  Int64  Int64  Float64  
─────┼──────────────────────────────────────
   1 │     1   2015     10     60  0.166667
   2 │     1   2015     20     60  0.333333
   3 │     1   2015     30     60  0.5
   4 │     2   2016     26    127  0.204724
   5 │     2   2016     34    127  0.267717
   6 │     2   2016     67    127  0.527559
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