Separating

Follwing the tidyverse syntax, the @separate() macro in TidierData.jl separates a single column into multiple columns. This is particularly useful for splitting a column containing delimited values into individual columns.

using TidierData

df = DataFrame(a = ["1-1", "2-2", "3-3-3"]);

@separate

Separate the "a" column into "b", "c", and "d" columns based on the dash delimiter

@chain df begin
    @separate(a, (b, c, d), "-")
end

3×3 DataFrame

Row	b	c	d
	SubStrin…	SubStrin…	SubStrin…?
1	1	1	missing
2	2	2	missing
3	3	3	3

The into columns can also be designated as follows:

new_names = ["x$(i)" for i in 1:3]; # or new_names = ["b", "c", "d"], or new_names = [:b, :c, :d]

@separate(df, a, !!new_names, "-")

3×3 DataFrame

Row	x1	x2	x3
	SubStrin…	SubStrin…	SubStrin…?
1	1	1	missing
2	2	2	missing
3	3	3	3

@unite

The @unite macro brings together multiple columns into one, separate the characters by a user specified delimiter Here, the @unite macro combines the "b", "c", and "d" columns columns into a single new "new_col" column using the "/" delimiter

df = DataFrame(
       b = ["1", "2", "3"],
       c = ["1", "2", "3"],
       d = [missing, missing, "3"]);

@chain df begin
    @unite(new_col, (b, c, d), "/")
end

3×4 DataFrame

Row	b	c	d	new_col
	String	String	String?	String
1	1	1	missing	1/1
2	2	2	missing	2/2
3	3	3	3	3/3/3

@separate_rows

Separate rows into multiple rows based on a chosen delimiter.

df = DataFrame(
       a = 1:3,
       b = ["a", "aa;bb;cc", "dd;ee"],
       c = ["1", "2;3;4", "5;6"],
       d = ["7", "8;9;10", "11;12"],
       e = ["11", "22;33;44", "55;66"]);

@separate_rows(df, b:e, ";")

6×5 DataFrame

Row	a	b	c	d	e
	Int64	SubStrin…	SubStrin…	SubStrin…	SubStrin…
1	1	a	1	7	11
2	2	aa	2	8	22
3	2	bb	3	9	33
4	2	cc	4	10	44
5	3	dd	5	11	55
6	3	ee	6	12	66

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