Join SQL tables — join.tbl

These are methods for the dplyr 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)

Usage

# S3 method for class 'tbl_lazy'
inner_join(
  x,
  y,
  by = NULL,
  copy = "none",
  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 class 'tbl_lazy'
left_join(
  x,
  y,
  by = NULL,
  copy = "none",
  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 class 'tbl_lazy'
right_join(
  x,
  y,
  by = NULL,
  copy = "none",
  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 class 'tbl_lazy'
full_join(
  x,
  y,
  by = NULL,
  copy = "none",
  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 class 'tbl_lazy'
cross_join(
  x,
  y,
  ...,
  copy = "none",
  suffix = c(".x", ".y"),
  x_as = NULL,
  y_as = NULL
)

# S3 method for class 'tbl_lazy'
semi_join(
  x,
  y,
  by = NULL,
  copy = "none",
  ...,
  na_matches = c("never", "na"),
  sql_on = NULL,
  auto_index = FALSE,
  x_as = NULL,
  y_as = NULL
)

# S3 method for class 'tbl_lazy'
anti_join(
  x,
  y,
  by = NULL,
  copy = "none",
  ...,
  na_matches = c("never", "na"),
  sql_on = NULL,
  auto_index = FALSE,
  x_as = NULL,
  y_as = NULL
)

Arguments

x, y

A pair of lazy data frames backed by database queries.

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, copy controls how y is copied into the same source as x. There are three options:

"none", the default, will error if y needs to be copied. This ensures that you don't accidentally copy large datasets from R to the database.
"temp-table": copies y into a temporary table in the same database as x. *_join() will automatically run ANALYZE on the created table in the hope that this will make your queries as efficient as possible by giving more data to the query planner.
"inline": y will be inlined into the query using copy_inline(). This is should faster for small datasets and doesn't require write access.

TRUE ("temp-table") and FALSE ("none") are also accepted for backward compatibility.

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 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(), bit64::match(), and %in%.

multiple, unmatched

Unsupported in database backends. As a workaround for multiple use a unique key and for unmatched a foreign key constraint.

relationship

Unsupported in database backends.

sql_on

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.

auto_index

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.

x_as, y_as

Alias to use for x resp. y. Defaults to "LHS" resp. "RHS"

Value

Another tbl_lazy. Use show_query() to see the generated query, and use collect() to execute the query and return data to R.

Examples

library(dplyr, warn.conflicts = FALSE)

band_db <- copy_to(memdb(), dplyr::band_members)
instrument_db <- copy_to(memdb(), dplyr::band_instruments)
band_db |> left_join(instrument_db) |> show_query()
#> Joining with `by = join_by(name)`
#> <SQL>
#> SELECT `dplyr::band_members`.*, `plays`
#> FROM `dplyr::band_members`
#> LEFT JOIN `dplyr::band_instruments`
#>   ON (`dplyr::band_members`.`name` = `dplyr::band_instruments`.`name`)

# Can join with local data frames by setting copy = TRUE
band_db |>
  left_join(dplyr::band_instruments, copy = TRUE)
#> Joining with `by = join_by(name)`
#> # A query:  ?? x 3
#> # Database: sqlite 3.51.1 [:memory:]
#>   name  band    plays 
#>   <chr> <chr>   <chr> 
#> 1 Mick  Stones  NA    
#> 2 John  Beatles guitar
#> 3 Paul  Beatles bass  

# 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")
#> # A query:  ?? x 2
#> # Database: sqlite 3.51.1 [:memory:]
#>       x label
#>   <dbl> <chr>
#> 1     1 one  
#> 2     2 NA   
#> 3    NA NA   
# But you can activate R's usual behaviour with the na_matches argument
db |> left_join(label, by = "x", na_matches = "na")
#> # A query:  ?? x 2
#> # Database: sqlite 3.51.1 [:memory:]
#>       x label  
#>   <dbl> <chr>  
#> 1     1 one    
#> 2     2 NA     
#> 3    NA missing

# 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()
#> Joining with `by = join_by(x)`
#> <SQL>
#> SELECT `dbplyr_tmp_GAnL54XlxB`.`x` AS `x`, `y`
#> FROM `dbplyr_tmp_GAnL54XlxB`
#> LEFT JOIN `dbplyr_tmp_m9HzOkF05U`
#>   ON (`dbplyr_tmp_GAnL54XlxB`.`x` = `dbplyr_tmp_m9HzOkF05U`.`x`)
db1 |> left_join(db2, sql_on = "LHS.x < RHS.x") |> show_query()
#> <SQL>
#> SELECT `LHS`.`x` AS `x.x`, `RHS`.`x` AS `x.y`, `y`
#> FROM `dbplyr_tmp_GAnL54XlxB` AS `LHS`
#> LEFT JOIN `dbplyr_tmp_m9HzOkF05U` AS `RHS`
#>   ON (LHS.x < RHS.x)