Are there any functions in julia that is equivalent to separate() in R? I have a column with a long string with ":" as a delimiter and I want to split the string by those delimiter into 8 columns.
1 Answers
Assuming you use DataFrames.jl you can use the following.
First generate some test data:
julia> using DataFrames
julia> df = DataFrame(in = join.(eachrow(rand(1:9, 10, 8)), ":"))
10×1 DataFrame
Row │ in
│ String
─────┼─────────────────
1 │ 8:2:9:4:1:9:3:1
2 │ 9:6:9:9:8:1:9:5
3 │ 2:4:9:8:5:4:8:7
4 │ 8:2:2:9:5:3:7:7
5 │ 1:4:6:1:3:9:2:1
6 │ 8:6:1:5:1:4:8:8
7 │ 4:6:4:4:4:4:8:8
8 │ 4:3:3:5:1:4:3:4
9 │ 9:5:5:7:5:3:4:3
10 │ 4:5:8:5:2:5:7:4
The splitting could be done in many ways (we are fully flexible in how you can split your column). Here I just use the split function.
If you want auto-generated column names use:
julia> transform(df, :in => ByRow(x -> split(x, ":")) => AsTable)
10×9 DataFrame
Row │ in x1 x2 x3 x4 x5 x6 x7 x8
│ String SubStrin… SubStrin… SubStrin… SubStrin… SubStrin… SubStrin… SubStrin… SubStrin…
─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 8:2:9:4:1:9:3:1 8 2 9 4 1 9 3 1
2 │ 9:6:9:9:8:1:9:5 9 6 9 9 8 1 9 5
3 │ 2:4:9:8:5:4:8:7 2 4 9 8 5 4 8 7
4 │ 8:2:2:9:5:3:7:7 8 2 2 9 5 3 7 7
5 │ 1:4:6:1:3:9:2:1 1 4 6 1 3 9 2 1
6 │ 8:6:1:5:1:4:8:8 8 6 1 5 1 4 8 8
7 │ 4:6:4:4:4:4:8:8 4 6 4 4 4 4 8 8
8 │ 4:3:3:5:1:4:3:4 4 3 3 5 1 4 3 4
9 │ 9:5:5:7:5:3:4:3 9 5 5 7 5 3 4 3
10 │ 4:5:8:5:2:5:7:4 4 5 8 5 2 5 7 4
Alternatively pass your own column names:
julia> transform(df, :in => ByRow(x -> split(x, ":")) => "out" .* string.(1:8))
10×9 DataFrame
Row │ in out1 out2 out3 out4 out5 out6 out7 out8
│ String SubStrin… SubStrin… SubStrin… SubStrin… SubStrin… SubStrin… SubStrin… SubStrin…
─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 8:2:9:4:1:9:3:1 8 2 9 4 1 9 3 1
2 │ 9:6:9:9:8:1:9:5 9 6 9 9 8 1 9 5
3 │ 2:4:9:8:5:4:8:7 2 4 9 8 5 4 8 7
4 │ 8:2:2:9:5:3:7:7 8 2 2 9 5 3 7 7
5 │ 1:4:6:1:3:9:2:1 1 4 6 1 3 9 2 1
6 │ 8:6:1:5:1:4:8:8 8 6 1 5 1 4 8 8
7 │ 4:6:4:4:4:4:8:8 4 6 4 4 4 4 8 8
8 │ 4:3:3:5:1:4:3:4 4 3 3 5 1 4 3 4
9 │ 9:5:5:7:5:3:4:3 9 5 5 7 5 3 4 3
10 │ 4:5:8:5:2:5:7:4 4 5 8 5 2 5 7 4
Note that the benefit of allowing custom parser is that you can in one shot convert the parts of your original string to their numeric values like this:
julia> transform(df, :in => ByRow(x -> parse.(Int, split(x, ":"))) => AsTable)
10×9 DataFrame
Row │ in x1 x2 x3 x4 x5 x6 x7 x8
│ String Int64 Int64 Int64 Int64 Int64 Int64 Int64 Int64
─────┼─────────────────────────────────────────────────────────────────────────
1 │ 8:2:9:4:1:9:3:1 8 2 9 4 1 9 3 1
2 │ 9:6:9:9:8:1:9:5 9 6 9 9 8 1 9 5
3 │ 2:4:9:8:5:4:8:7 2 4 9 8 5 4 8 7
4 │ 8:2:2:9:5:3:7:7 8 2 2 9 5 3 7 7
5 │ 1:4:6:1:3:9:2:1 1 4 6 1 3 9 2 1
6 │ 8:6:1:5:1:4:8:8 8 6 1 5 1 4 8 8
7 │ 4:6:4:4:4:4:8:8 4 6 4 4 4 4 8 8
8 │ 4:3:3:5:1:4:3:4 4 3 3 5 1 4 3 4
9 │ 9:5:5:7:5:3:4:3 9 5 5 7 5 3 4 3
10 │ 4:5:8:5:2:5:7:4 4 5 8 5 2 5 7 4
Here is another way to do it. Not as composable as the above but showing you the flexibility of the ecosystem:
julia> using CSV
julia> CSV.read(IOBuffer(join(df.in, "\n")), DataFrame, header=false, delim=":")
10×8 DataFrame
Row │ Column1 Column2 Column3 Column4 Column5 Column6 Column7 Column8
│ Int64 Int64 Int64 Int64 Int64 Int64 Int64 Int64
─────┼────────────────────────────────────────────────────────────────────────
1 │ 8 2 9 4 1 9 3 1
2 │ 9 6 9 9 8 1 9 5
3 │ 2 4 9 8 5 4 8 7
4 │ 8 2 2 9 5 3 7 7
5 │ 1 4 6 1 3 9 2 1
6 │ 8 6 1 5 1 4 8 8
7 │ 4 6 4 4 4 4 8 8
8 │ 4 3 3 5 1 4 3 4
9 │ 9 5 5 7 5 3 4 3
10 │ 4 5 8 5 2 5 7 4
julia> CSV.read(IOBuffer(join(df.in, "\n")), DataFrame, header="out" .* string.(1:8), delim=":")
10×8 DataFrame
Row │ out1 out2 out3 out4 out5 out6 out7 out8
│ Int64 Int64 Int64 Int64 Int64 Int64 Int64 Int64
─────┼────────────────────────────────────────────────────────
1 │ 8 2 9 4 1 9 3 1
2 │ 9 6 9 9 8 1 9 5
3 │ 2 4 9 8 5 4 8 7
4 │ 8 2 2 9 5 3 7 7
5 │ 1 4 6 1 3 9 2 1
6 │ 8 6 1 5 1 4 8 8
7 │ 4 6 4 4 4 4 8 8
8 │ 4 3 3 5 1 4 3 4
9 │ 9 5 5 7 5 3 4 3
10 │ 4 5 8 5 2 5 7 4
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