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A nest join leaves x almost unchanged, except that it adds a new list-column, where each element contains the rows from y that match the corresponding row in x.

Usage

nest_join(x, y, by = NULL, copy = FALSE, keep = NULL, name = NULL, ...)

# S3 method for data.frame
nest_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  keep = NULL,
  name = NULL,
  ...,
  na_matches = c("na", "never"),
  unmatched = "drop"
)

Arguments

x, y

A pair of data frames, data frame extensions (e.g. a tibble), or lazy data frames (e.g. from dbplyr or dtplyr). See Methods, below, for more details.

by

A join specification created with join_by(), or a character vector of variables to join by.

If NULL, the default, *_join() will perform a natural join, using all variables in common across x and y. A message lists the variables so that you can check they're correct; suppress the message by supplying by explicitly.

To join on different variables between x and y, use a join_by() specification. For example, join_by(a == b) will match x$a to y$b.

To join by multiple variables, use a join_by() specification with multiple expressions. For example, join_by(a == b, c == d) will match x$a to y$b and x$c to y$d. If the column names are the same between x and y, you can shorten this by listing only the variable names, like join_by(a, c).

join_by() can also be used to perform inequality, rolling, and overlap joins. See the documentation at ?join_by for details on these types of joins.

For simple equality joins, you can alternatively specify a character vector of variable names to join by. For example, by = c("a", "b") joins x$a to y$a and x$b to y$b. If variable names differ between x and y, use a named character vector like by = c("x_a" = "y_a", "x_b" = "y_b").

To perform a cross-join, generating all combinations of x and y, see cross_join().

copy

If x and y are not from the same data source, and copy is TRUE, then y will be copied into the same src as x. This allows you to join tables across srcs, but it is a potentially expensive operation so you must opt into it.

keep

Should the new list-column contain join keys? The default will preserve the join keys for inequality joins.

name

The name of the list-column created by the join. If NULL, the default, the name of y is used.

...

Other parameters passed onto methods.

na_matches

Should two NA or two NaN values match?

  • "na", the default, treats two NA or two NaN values as equal, like %in%, match(), and merge().

  • "never" treats two NA or two NaN values as different, and will never match them together or to any other values. This is similar to joins for database sources and to base::merge(incomparables = NA).

unmatched

How should unmatched keys that would result in dropped rows be handled?

  • "drop" drops unmatched keys from the result.

  • "error" throws an error if unmatched keys are detected.

unmatched is intended to protect you from accidentally dropping rows during a join. It only checks for unmatched keys in the input that could potentially drop rows.

  • For left joins, it checks y.

  • For right joins, it checks x.

  • For inner joins, it checks both x and y. In this case, unmatched is also allowed to be a character vector of length 2 to specify the behavior for x and y independently.

Value

The output:

  • Is same type as x (including having the same groups).

  • Has exactly the same number of rows as x.

  • Contains all the columns of x in the same order with the same values. They are only modified (slightly) if keep = FALSE, when columns listed in by will be coerced to their common type across x and y.

  • Gains one new column called {name} on the far right, a list column containing data frames the same type as y.

Relationship to other joins

You can recreate many other joins from the result of a nest join:

Methods

This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages: dplyr (data.frame) .

See also

Examples

df1 <- tibble(x = 1:3)
df2 <- tibble(x = c(2, 3, 3), y = c("a", "b", "c"))

out <- nest_join(df1, df2)
#> Joining with `by = join_by(x)`
out
#> # A tibble: 3 × 2
#>       x df2             
#>   <dbl> <list>          
#> 1     1 <tibble [0 × 1]>
#> 2     2 <tibble [1 × 1]>
#> 3     3 <tibble [2 × 1]>
out$df2
#> [[1]]
#> # A tibble: 0 × 1
#> # … with 1 variable: y <chr>
#> 
#> [[2]]
#> # A tibble: 1 × 1
#>   y    
#>   <chr>
#> 1 a    
#> 
#> [[3]]
#> # A tibble: 2 × 1
#>   y    
#>   <chr>
#> 1 b    
#> 2 c    
#>