Binding

Whereas joins are useful for combining data frames based on matching keys, another way to combine data frames is to bind them together, which can be done either by rows or by columns. TidierData.jl implements these actions using @bind_rows() and @bind_cols(), respectively.

Let's generate three data frames to combine.

using TidierData

df1 = DataFrame(a=1:3, b=1:3);

df2 = DataFrame(a=4:6, b=4:6);

df3 = DataFrame(a=7:9, c=7:9);

@bind_rows()

@bind_rows(df1, df2)

6×2 DataFrame

Row	a	b
	Int64	Int64
1	1	1
2	2	2
3	3	3
4	4	4
5	5	5
6	6	6

@bind_rows() keeps columns that are present in at least one of the provided data frames. Any missing columns will be filled with missing values.

@bind_rows(df1, df3)

6×3 DataFrame

Row	a	b	c
	Int64	Int64?	Int64?
1	1	1	missing
2	2	2	missing
3	3	3	missing
4	7	missing	7
5	8	missing	8
6	9	missing	9

There is an optional id argument to add an identifier for combined data frames. Note that both @bind_rows and @bind_cols accept multiple (i.e., more than 2) data frames, as in the example below.

@bind_rows(df1, df2, df3, id = "id")

9×4 DataFrame

Row	a	b	c	id
	Int64	Int64?	Int64?	Int64
1	1	1	missing	1
2	2	2	missing	1
3	3	3	missing	1
4	4	4	missing	2
5	5	5	missing	2
6	6	6	missing	2
7	7	missing	7	3
8	8	missing	8	3
9	9	missing	9	3

@bind_cols()

@bind_cols works similarly to R's tidyverse although the .name_repair argument is not supported.

@bind_cols(df1, df2)

3×4 DataFrame

Row	a	b	a_1	b_1
	Int64	Int64	Int64	Int64
1	1	1	4	4
2	2	2	5	5
3	3	3	6	6

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