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.
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.
inner_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = c(".x", ".y"),
...,
keep = NULL
)
# S3 method for class '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 class '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 class '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 class '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
)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.
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.
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.
Should two NA or two NaN values match?
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.
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.
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.
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.
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.
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(): dplyr (data.frame)
.
left_join(): dplyr (data.frame)
.
right_join(): dplyr (data.frame)
.
full_join(): dplyr (data.frame)
.
Other joins:
cross_join(),
filter-joins,
nest_join()
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