join.tbl_sql {dplyr} | R Documentation

## Join sql tbls.

### Description

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

### Usage

    ## S3 method for class 'tbl_sql'
    inner_join(x, y, by = NULL, copy = FALSE,
      auto_index = FALSE, ...)

    ## S3 method for class 'tbl_sql'
    left_join(x, y, by = NULL, copy = FALSE,
      auto_index = FALSE, ...)

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

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

### Arguments

`x,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 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.

`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:
    if (require("RSQLite") && 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)
    }

    ## End(Not run)

[Package dplyr version 0.4.3 Index]