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, ...)

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

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

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

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

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

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 diambiguate 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

Implementation notes

Semi-joins are implemented using WHERE EXISTS, and anti-joins with WHERE NOT EXISTS. Support for semi-joins is somewhat partial: you can only create semi joins where the x and y columns are compared with = not with more general operators.

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) } })