join.tbl_sql {dbplyr}R Documentation

Join SQL tables

Description

These are methods for the dplyr join generics. They are translated to the following SQL queries:

Usage

## S3 method for class 'tbl_lazy'
inner_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = NULL,
  auto_index = FALSE,
  ...,
  sql_on = NULL,
  na_matches = c("never", "na")
)

## S3 method for class 'tbl_lazy'
left_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = NULL,
  auto_index = FALSE,
  ...,
  sql_on = NULL,
  na_matches = c("never", "na")
)

## S3 method for class 'tbl_lazy'
right_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = NULL,
  auto_index = FALSE,
  ...,
  sql_on = NULL,
  na_matches = c("never", "na")
)

## S3 method for class 'tbl_lazy'
full_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = NULL,
  auto_index = FALSE,
  ...,
  sql_on = NULL,
  na_matches = c("never", "na")
)

## S3 method for class 'tbl_lazy'
semi_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  auto_index = FALSE,
  ...,
  sql_on = NULL,
  na_matches = c("never", "na")
)

## S3 method for class 'tbl_lazy'
anti_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  auto_index = FALSE,
  ...,
  sql_on = NULL,
  na_matches = c("never", "na")
)

Arguments

x, y

A pair of lazy data frames backed by database queries.

by

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 by different variables on x and y, use a named vector. For example, by = c("a" = "b") will match x$a to y$b.

To join by multiple variables, use a vector with length > 1. For example, by = c("a", "b") will match x$a to y$a and x$b to y$b. Use a named vector to match different variables in x and y. For example, by = c("a" = "b", "c" = "d") will match x$a to y$b and x$c to y$d.

To perform a cross-join, generating all combinations of x and y, use by = character().

copy

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.

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.

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.

...

Other parameters passed onto methods.

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.

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

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 <- 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()

[Package dbplyr version 2.1.1 Index]