Mutating joins add columns from y to x, matching observations based on the keys. There are four mutating joins: the inner join, and the three outer joins.

### Inner join

An inner_join() only keeps observations from x that have a matching key in y.

The most important property of an inner join is that unmatched rows in either input are not included in the result. This means that generally inner joins are not appropriate in most analyses, because it is too easy to lose observations.

### Outer joins

The three outer joins keep observations that appear in at least one of the data frames:

• A left_join() keeps all observations in x.

• A right_join() keeps all observations in y.

• A full_join() keeps all observations in x and y.

## Usage

inner_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep = NULL
)

# S3 method for data.frame
inner_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep = NULL,
na_matches = c("na", "never"),
multiple = "all",
unmatched = "drop",
relationship = NULL
)

left_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep = NULL
)

# S3 method for data.frame
left_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep = NULL,
na_matches = c("na", "never"),
multiple = "all",
unmatched = "drop",
relationship = NULL
)

right_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep = NULL
)

# S3 method for data.frame
right_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep = NULL,
na_matches = c("na", "never"),
multiple = "all",
unmatched = "drop",
relationship = NULL
)

full_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep = NULL
)

# S3 method for data.frame
full_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep = NULL,
na_matches = c("na", "never"),
multiple = "all",
relationship = NULL
)

## 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.

suffix

If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.

...

Other parameters passed onto methods.

keep

Should the join keys from both x and y be preserved in the output?

• If NULL, the default, joins on equality retain only the keys from x, while joins on inequality retain the keys from both inputs.

• If TRUE, all keys from both inputs are retained.

• If FALSE, only keys from x are retained. For right and full joins, the data in key columns corresponding to rows that only exist in y are merged into the key columns from x. Can't be used when joining on inequality conditions.

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).

multiple

Handling of rows in x with multiple matches in y. For each row of x:

• "all", the default, returns every match detected in y. This is the same behavior as SQL.

• "any" returns one match detected in y, with no guarantees on which match will be returned. It is often faster than "first" and "last" if you just need to detect if there is at least one match.

• "first" returns the first match detected in y.

• "last" returns the last match detected in y.

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.

relationship

Handling of the expected relationship between the keys of x and y. If the expectations chosen from the list below are invalidated, an error is thrown.

• NULL, the default, doesn't expect there to be any relationship between x and y. However, for equality joins it will check for a many-to-many relationship (which is typically unexpected) and will warn if one occurs, encouraging you to either take a closer look at your inputs or make this relationship explicit by specifying "many-to-many".

See the Many-to-many relationships section for more details.

• "one-to-one" expects:

• Each row in x matches at most 1 row in y.

• Each row in y matches at most 1 row in x.

• "one-to-many" expects:

• Each row in y matches at most 1 row in x.

• "many-to-one" expects:

• Each row in x matches at most 1 row in y.

• "many-to-many" doesn't perform any relationship checks, but is provided to allow you to be explicit about this relationship if you know it exists.

relationship doesn't handle cases where there are zero matches. For that, see unmatched.

## Value

An object of the same type as x (including the same groups). The order of the rows and columns of x is preserved as much as possible. The output has the following properties:

• The rows are affect by the join type.

• inner_join() returns matched x rows.

• left_join() returns all x rows.

• right_join() returns matched of x rows, followed by unmatched y rows.

• full_join() returns all x rows, followed by unmatched y rows.

• Output columns include all columns from x and all non-key columns from y. If keep = TRUE, the key columns from y are included as well.

• If non-key columns in x and y have the same name, suffixes are added to disambiguate. If keep = TRUE and key columns in x and y have the same name, suffixes are added to disambiguate these as well.

• If keep = FALSE, output columns included in by are coerced to their common type between x and y.

## Many-to-many relationships

By default, dplyr guards against many-to-many relationships in equality joins by throwing a warning. These occur when both of the following are true:

• A row in x matches multiple rows in y.

• A row in y matches multiple rows in x.

This is typically surprising, as most joins involve a relationship of one-to-one, one-to-many, or many-to-one, and is often the result of an improperly specified join. Many-to-many relationships are particularly problematic because they can result in a Cartesian explosion of the number of rows returned from the join.

If a many-to-many relationship is expected, silence this warning by explicitly setting relationship = "many-to-many".

In production code, it is best to preemptively set relationship to whatever relationship you expect to exist between the keys of x and y, as this forces an error to occur immediately if the data doesn't align with your expectations.

Inequality joins typically result in many-to-many relationships by nature, so they don't warn on them by default, but you should still take extra care when specifying an inequality join, because they also have the capability to return a large number of rows.

Rolling joins don't warn on many-to-many relationships either, but many rolling joins follow a many-to-one relationship, so it is often useful to set relationship = "many-to-one" to enforce this.

Note that in SQL, most database providers won't let you specify a many-to-many relationship between two tables, instead requiring that you create a third junction table that results in two one-to-many relationships instead.

## Methods

These functions 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:

• inner_join(): dbplyr (tbl_lazy), dplyr (data.frame) .

• left_join(): dbplyr (tbl_lazy), dplyr (data.frame) .

• right_join(): dbplyr (tbl_lazy), dplyr (data.frame) .

• full_join(): dbplyr (tbl_lazy), dplyr (data.frame) .

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

## Examples

band_members %>% inner_join(band_instruments)
#> Joining with by = join_by(name)
#> # A tibble: 2 × 3
#>   name  band    plays
#>   <chr> <chr>   <chr>
#> 1 John  Beatles guitar
#> 2 Paul  Beatles bass
band_members %>% left_join(band_instruments)
#> Joining with by = join_by(name)
#> # A tibble: 3 × 3
#>   name  band    plays
#>   <chr> <chr>   <chr>
#> 1 Mick  Stones  NA
#> 2 John  Beatles guitar
#> 3 Paul  Beatles bass
band_members %>% right_join(band_instruments)
#> Joining with by = join_by(name)
#> # A tibble: 3 × 3
#>   name  band    plays
#>   <chr> <chr>   <chr>
#> 1 John  Beatles guitar
#> 2 Paul  Beatles bass
#> 3 Keith NA      guitar
band_members %>% full_join(band_instruments)
#> Joining with by = join_by(name)
#> # A tibble: 4 × 3
#>   name  band    plays
#>   <chr> <chr>   <chr>
#> 1 Mick  Stones  NA
#> 2 John  Beatles guitar
#> 3 Paul  Beatles bass
#> 4 Keith NA      guitar

# To suppress the message about joining variables, supply by
band_members %>% inner_join(band_instruments, by = join_by(name))
#> # A tibble: 2 × 3
#>   name  band    plays
#>   <chr> <chr>   <chr>
#> 1 John  Beatles guitar
#> 2 Paul  Beatles bass
# This is good practice in production code

# Use an equality expression if the join variables have different names
band_members %>% full_join(band_instruments2, by = join_by(name == artist))
#> # A tibble: 4 × 3
#>   name  band    plays
#>   <chr> <chr>   <chr>
#> 1 Mick  Stones  NA
#> 2 John  Beatles guitar
#> 3 Paul  Beatles bass
#> 4 Keith NA      guitar
# By default, the join keys from x and y are coalesced in the output; use
# keep = TRUE to keep the join keys from both x and y
band_members %>%
full_join(band_instruments2, by = join_by(name == artist), keep = TRUE)
#> # A tibble: 4 × 4
#>   name  band    artist plays
#>   <chr> <chr>   <chr>  <chr>
#> 1 Mick  Stones  NA     NA
#> 2 John  Beatles John   guitar
#> 3 Paul  Beatles Paul   bass
#> 4 NA    NA      Keith  guitar

# If a row in x matches multiple rows in y, all the rows in y will be
# returned once for each matching row in x.
df1 <- tibble(x = 1:3)
df2 <- tibble(x = c(1, 1, 2), y = c("first", "second", "third"))
df1 %>% left_join(df2)
#> Joining with by = join_by(x)
#> # A tibble: 4 × 2
#>       x y
#>   <dbl> <chr>
#> 1     1 first
#> 2     1 second
#> 3     2 third
#> 4     3 NA

# If a row in y also matches multiple rows in x, this is known as a
# many-to-many relationship, which is typically a result of an improperly
# specified join or some kind of messy data. In this case, a warning is
# thrown by default:
df3 <- tibble(x = c(1, 1, 1, 3))
df3 %>% left_join(df2)
#> Joining with by = join_by(x)
#> Warning: Detected an unexpected many-to-many relationship between x and y.
#> ℹ Row 1 of x matches multiple rows in y.
#> ℹ Row 1 of y matches multiple rows in x.
#> ℹ If a many-to-many relationship is expected, set relationship =
#>   "many-to-many" to silence this warning.
#> # A tibble: 7 × 2
#>       x y
#>   <dbl> <chr>
#> 1     1 first
#> 2     1 second
#> 3     1 first
#> 4     1 second
#> 5     1 first
#> 6     1 second
#> 7     3 NA

# In the rare case where a many-to-many relationship is expected, set
# relationship = "many-to-many" to silence this warning
df3 %>% left_join(df2, relationship = "many-to-many")
#> Joining with by = join_by(x)
#> # A tibble: 7 × 2
#>       x y
#>   <dbl> <chr>
#> 1     1 first
#> 2     1 second
#> 3     1 first
#> 4     1 second
#> 5     1 first
#> 6     1 second
#> 7     3 NA

# Use join_by() with a condition other than == to perform an inequality
# join. Here we match on every instance where df1$x > df2$x.
df1 %>% left_join(df2, join_by(x > x))
#> # A tibble: 6 × 3
#>     x.x   x.y y
#>   <int> <dbl> <chr>
#> 1     1    NA NA
#> 2     2     1 first
#> 3     2     1 second
#> 4     3     1 first
#> 5     3     1 second
#> 6     3     2 third

# By default, NAs match other NAs so that there are two
# rows in the output of this join:
df1 <- data.frame(x = c(1, NA), y = 2)
df2 <- data.frame(x = c(1, NA), z = 3)
left_join(df1, df2)
#> Joining with by = join_by(x)
#>    x y z
#> 1  1 2 3
#> 2 NA 2 3

# You can optionally request that NAs don't match, giving a
# a result that more closely resembles SQL joins
left_join(df1, df2, na_matches = "never")
#> Joining with by = join_by(x)
#>    x y  z
#> 1  1 2  3
#> 2 NA 2 NA