See join for a description of the general purpose of the functions.

# S3 method for tbl_lazy
inner_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), auto_index = FALSE, ..., sql_on = NULL)

# S3 method for tbl_lazy
left_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), auto_index = FALSE, ..., sql_on = NULL)

# S3 method for tbl_lazy
right_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), auto_index = FALSE, ..., sql_on = NULL)

# S3 method for tbl_lazy
full_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), auto_index = FALSE, ..., sql_on = NULL)

# S3 method for tbl_lazy
semi_join(x, y, by = NULL, copy = FALSE,
  auto_index = FALSE, ..., sql_on = NULL)

# S3 method for tbl_lazy
anti_join(x, y, by = NULL, copy = FALSE,
  auto_index = FALSE, ..., sql_on = NULL)

Arguments

x

tbls to join

y

tbls to join

by

a character vector of variables to join by. If NULL, the default, *_join() will do a natural join, using all variables with common names across the two tables. A message lists the variables so that you can check they're right (to suppress the message, simply explicitly list the variables that you want to join).

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.

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, for instance, na_matches to control how NA values are matched. See join.tbl_df for more.

sql_on

A custom join predicate as an SQL expression. The SQL can refer to the LHS and RHS aliases to disambiguate column names.

Implementation notes

Semi-joins are implemented using WHERE EXISTS, and anti-joins with WHERE NOT EXISTS.

All joins use column equality by default. An arbitrary join predicate can be specified by passing an SQL expression to the sql_on argument. Use LHS and RHS to refer to the left-hand side or right-hand side table, respectively.

Examples

# NOT RUN {
library(dplyr)
if (has_lahman("sqlite")) {

# Left joins ----------------------------------------------------------------
lahman_s <- lahman_sqlite()
batting <- tbl(lahman_s, "Batting")
team_info <- select(tbl(lahman_s, "Teams"), yearID, lgID, teamID, G, R:H)

# Combine player and whole team statistics
first_stint <- select(filter(batting, stint == 1), playerID:H)
both <- left_join(first_stint, team_info, type = "inner", by = c("yearID", "teamID", "lgID"))
head(both)
explain(both)

# Join with a local data frame
grid <- expand.grid(
  teamID = c("WAS", "ATL", "PHI", "NYA"),
  yearID = 2010:2012)
top4a <- left_join(batting, grid, copy = TRUE)
explain(top4a)

# Indices don't really help here because there's no matching index on
# batting
top4b <- left_join(batting, grid, copy = TRUE, auto_index = TRUE)
explain(top4b)

# Semi-joins ----------------------------------------------------------------

people <- tbl(lahman_s, "Master")

# All people in half of fame
hof <- tbl(lahman_s, "HallOfFame")
semi_join(people, hof)

# All people not in the hall of fame
anti_join(people, hof)

# Find all managers
manager <- tbl(lahman_s, "Managers")
semi_join(people, manager)

# Find all managers in hall of fame
famous_manager <- semi_join(semi_join(people, manager), hof)
famous_manager
explain(famous_manager)

# Anti-joins ----------------------------------------------------------------

# batters without person covariates
anti_join(batting, people)

# Arbitrary predicates ------------------------------------------------------

# Find all pairs of awards given to the same player
# with at least 18 years between the awards:
awards_players <- tbl(lahman_s, "AwardsPlayers")
inner_join(
  awards_players, awards_players,
  sql_on = paste0(
    "(LHS.playerID = RHS.playerID) AND ",
    "(LHS.yearID < RHS.yearID - 18)"
  )
)
}
# }