filter-joins: Filtering joins
In javifar/TIDYVERSE-DPLYR: A Grammar of Data Manipulation

Description Usage Arguments Value Methods See Also Examples

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.
`by`	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 by different variables on `x` and `y`, use a named vector. For example, `by = c("a" = "b")` will match `x$a` to `y$b`. To join by multiple variables, use a vector with length > 1. For example, `by = c("a", "b")` will match `x$a` to `y$a` and `x$b` to `y$b`. Use a named vector to match different variables in `x` and `y`. For example, `by = c("a" = "b", "c" = "d")` will match `x$a` to `y$b` and `x$c` to `y$d`. To perform a cross-join, generating all combinations of `x` and `y`, use `by = character()`.
`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.
`...`	Other parameters passed onto methods.
`na_matches`	Should `NA` and `NaN` values match one another? The default, `"na"`, treats two `NA` or `NaN` values as equal, like `%in%`, `match()`, `merge()`. Use `"never"` to always treat two `NA` or `NaN` values as different, like joins for database sources, similarly to `merge(incomparables = FALSE)`.

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")}.

Other joins: 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 = "name")
# This is good practice in production code