Julia tranpose grouped data in DataFrames? - r

Question

ds = Dataset(group = repeat(1:3, inner = 2),
b = repeat(1:2, inner = 3),
c = repeat(1:1, inner = 6),
d = repeat(1:6, inner = 1),
e = string.('a':'f'))
In inmemorydatasets package, we can transpose grouped data like this.
#transpose by group
transpose(groupby(ds, :group), 2:4)
How can do I do this in DataFrames packages?
How can do I do this in R?
result:
Row │ group variable 1 2
│ Int64 String Int64? Int64?
─────┼─────────────────────────────────
1 │ 1 b 1 1
2 │ 1 c 1 1
3 │ 1 d 1 2
4 │ 2 b 1 2
5 │ 2 c 1 1
6 │ 2 d 3 4
7 │ 3 b 2 2
8 │ 3 c 1 1
9 │ 3 d 5 6

Answer 1

Answer (attempt) regarding Julia DataFrames part of the question:
First creating the DataFrame:
df = DataFrame(group = repeat(1:3, inner = 2),
b = repeat(1:2, inner = 3),
c = repeat(1:1, inner = 6),
d = repeat(1:6, inner = 1),
e = string.('a':'f'))
Next, since the transpose operation depends on row ordering, we fix a row ordering in the groups:
julia> ordereddf = transform(DataFrames.groupby(df, :group),"group" => (x->1:length(x)) => "rn")[:,Not(:e)]
6×5 DataFrame
Row │ group b c d rn
│ Int64 Int64 Int64 Int64 Int64
─────┼───────────────────────────────────
1 │ 1 1 1 1 1
2 │ 1 1 1 2 2
3 │ 2 1 1 3 1
4 │ 2 2 1 4 2
5 │ 3 2 1 5 1
6 │ 3 2 1 6 2
Finally, the stack - unstack combo, does the transposing bit:
julia> sort!(unstack(stack(ordereddf,[:b,:c,:d]),:rn, :value),:group)
9×4 DataFrame
Row │ group variable 1 2
│ Int64 String Int64? Int64?
─────┼─────────────────────────────────
1 │ 1 b 1 1
2 │ 1 c 1 1
3 │ 1 d 1 2
4 │ 2 b 1 2
5 │ 2 c 1 1
6 │ 2 d 3 4
7 │ 3 b 2 2
8 │ 3 c 1 1
9 │ 3 d 5 6
Feels like there might be easier ways to do this, but in general, transpose is rarely appropriate for database-like tables, and if it is appropriate, then maybe a matrix should have been used to store information in the first place.
The R part is left for someone else to answer.