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These are methods for the dplyr join generics. They are translated to the following SQL queries:
inner_join(x, y): SELECT * FROM x JOIN y ON x.a = y.a
left_join(x, y): SELECT * FROM x LEFT JOIN y ON x.a = y.a
right_join(x, y): SELECT * FROM x RIGHT JOIN y ON x.a = y.a
full_join(x, y): SELECT * FROM x FULL JOIN y ON x.a = y.a
semi_join(x, y): SELECT * FROM x WHERE EXISTS (SELECT 1 FROM y WHERE x.a = y.a)
anti_join(x, y): SELECT * FROM x WHERE NOT EXISTS (SELECT 1 FROM y WHERE x.a = y.a)
# S3 method for tbl_lazy
inner_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = NULL,
...,
keep = NULL,
na_matches = c("never", "na"),
multiple = NULL,
unmatched = "drop",
relationship = NULL,
sql_on = NULL,
auto_index = FALSE,
x_as = NULL,
y_as = NULL
)# S3 method for tbl_lazy
left_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = NULL,
...,
keep = NULL,
na_matches = c("never", "na"),
multiple = NULL,
unmatched = "drop",
relationship = NULL,
sql_on = NULL,
auto_index = FALSE,
x_as = NULL,
y_as = NULL
)
# S3 method for tbl_lazy
right_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = NULL,
...,
keep = NULL,
na_matches = c("never", "na"),
multiple = NULL,
unmatched = "drop",
relationship = NULL,
sql_on = NULL,
auto_index = FALSE,
x_as = NULL,
y_as = NULL
)
# S3 method for tbl_lazy
full_join(
x,
y,
by = NULL,
copy = FALSE,
suffix = NULL,
...,
keep = NULL,
na_matches = c("never", "na"),
multiple = NULL,
relationship = NULL,
sql_on = NULL,
auto_index = FALSE,
x_as = NULL,
y_as = NULL
)
# S3 method for tbl_lazy
cross_join(
x,
y,
...,
copy = FALSE,
suffix = c(".x", ".y"),
x_as = NULL,
y_as = NULL
)
# S3 method for tbl_lazy
semi_join(
x,
y,
by = NULL,
copy = FALSE,
...,
na_matches = c("never", "na"),
sql_on = NULL,
auto_index = FALSE,
x_as = NULL,
y_as = NULL
)
# S3 method for tbl_lazy
anti_join(
x,
y,
by = NULL,
copy = FALSE,
...,
na_matches = c("never", "na"),
sql_on = NULL,
auto_index = FALSE,
x_as = NULL,
y_as = NULL
)
Another tbl_lazy. Use show_query() to see the generated
query, and use collect() to execute the query
and return data to R.
A pair of lazy data frames backed by database queries.
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 a
temporary table in same database as x. *_join() will automatically
run ANALYZE on the created table in the hope that this will make
you queries as efficient as possible by giving more data to the query
planner.
This allows you to join tables across srcs, but it's 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 NA (NULL) values match one another?
The default, "never", is how databases usually work. "na" makes
the joins behave like the dplyr join functions, merge(), match(),
and %in%.
Unsupported in database backends. As a workaround for multiple use a unique key and for unmatched a foreign key constraint.
Unsupported in database backends.
A custom join predicate as an SQL expression.
Usually joins use column equality, but you can perform more complex
queries by supply sql_on which should be a SQL expression that
uses LHS and RHS aliases to refer to the left-hand side or
right-hand side of the join respectively.
if copy is TRUE, automatically create
indices for the variables in by. This may speed up the join if
there are matching indexes in x.
Alias to use for x resp. y. Defaults to "LHS" resp.
"RHS"
library(dplyr, warn.conflicts = FALSE)
band_db <- tbl_memdb(dplyr::band_members)
instrument_db <- tbl_memdb(dplyr::band_instruments)
band_db %>% left_join(instrument_db) %>% show_query()
# Can join with local data frames by setting copy = TRUE
band_db %>%
left_join(dplyr::band_instruments, copy = TRUE)
# Unlike R, joins in SQL don't usually match NAs (NULLs)
db <- memdb_frame(x = c(1, 2, NA))
label <- memdb_frame(x = c(1, NA), label = c("one", "missing"))
db %>% left_join(label, by = "x")
# But you can activate R's usual behaviour with the na_matches argument
db %>% left_join(label, by = "x", na_matches = "na")
# By default, joins are equijoins, but you can use `sql_on` to
# express richer relationships
db1 <- memdb_frame(x = 1:5)
db2 <- memdb_frame(x = 1:3, y = letters[1:3])
db1 %>% left_join(db2) %>% show_query()
db1 %>% left_join(db2, sql_on = "LHS.x < RHS.x") %>% show_query()
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