left_join | R Documentation |
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.
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
.
## S3 method for class 'Seurat'
left_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
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 If To join on different variables between To join by multiple variables, use a
For simple equality joins, you can alternatively specify a character vector
of variable names to join by. For example, To perform a cross-join, generating all combinations of |
copy |
If |
suffix |
If there are non-joined duplicate variables in |
... |
Other parameters passed onto methods. |
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, suffix
es are added
to disambiguate. If keep = TRUE
and key columns in x
and y
have
the same name, suffix
es 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()
: \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("inner_join")}.
left_join()
: \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("left_join")}.
right_join()
: \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("right_join")}.
full_join()
: \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("full_join")}.
Other joins:
cross_join()
,
filter-joins
,
nest_join()
data(pbmc_small)
tt <- pbmc_small
tt |> left_join(tt |>
distinct(groups) |>
mutate(new_column=1:2))
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