Joins

One really nice thing about the R tidyverse implementation of joins is that they support natural joins. If you don't specify which columns to join on, these column names are inferred from the overlapping columns. While you can override this behavior by specifying which columns to join on, it's convenient that this is not strictly required. We have adopted a similar approach to joins in TidierData.jl.

Here, we will only show examples of natural joins. For additional ways to join, take a look at the examples in the Reference.

using TidierData

Let's generate two data frames to join on. Here's the first one.

df1 = DataFrame(a = ["a", "b"], b = 1:2);

And here's the second one.

df2 = DataFrame(a = ["a", "c"], c = 3:4);

All the joins work similarly to R's tidyverse although the new join_by syntax for non-equijoins is not (yet) supported.

Left join

@left_join(df1, df2)

2×3 DataFrame

Row	a	b	c
	String	Int64	Int64?
1	a	1	3
2	b	2	missing

Right join

@right_join(df1, df2)

2×3 DataFrame

Row	a	b	c
	String	Int64?	Int64
1	a	1	3
2	c	missing	4

Inner join

@inner_join(df1, df2)

1×3 DataFrame

Row	a	b	c
	String	Int64	Int64
1	a	1	3

Full join

@full_join(df1, df2)

3×3 DataFrame

Row	a	b	c
	String	Int64?	Int64?
1	a	1	3
2	b	2	missing
3	c	missing	4

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