filter-joins: Filtering joins

filter-joinsR Documentation

Filtering joins


Filtering joins filter rows from x based on the presence or absence of matches in y:

  • semi_join() return all rows from x with a match in y.

  • anti_join() return all rows from x without a match in y.


semi_join(x, y, by = NULL, copy = FALSE, ...)

## S3 method for class 'data.frame'
semi_join(x, y, by = NULL, copy = FALSE, ..., na_matches = c("na", "never"))

anti_join(x, y, by = NULL, copy = FALSE, ...)

## S3 method for class 'data.frame'
anti_join(x, y, by = NULL, copy = FALSE, ..., na_matches = c("na", "never"))


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.


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().


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.


Other parameters passed onto methods.


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).


An object of the same type as x. The output has the following properties:

  • Rows are a subset of the input, but appear in the same order.

  • Columns are not modified.

  • Data frame attributes are preserved.

  • Groups are taken from x. The number of groups may be reduced.


These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

  • semi_join(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("semi_join")}.

  • anti_join(): \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("anti_join")}.

See Also

Other joins: cross_join(), mutate-joins, nest_join()


# "Filtering" joins keep cases from the LHS
band_members %>% semi_join(band_instruments)
band_members %>% anti_join(band_instruments)

# To suppress the message about joining variables, supply `by`
band_members %>% semi_join(band_instruments, by = join_by(name))
# This is good practice in production code

dplyr documentation built on Nov. 17, 2023, 5:08 p.m.