join {dplyr} | R Documentation

## Join two tbls together

### Description

These are generic functions that dispatch to individual tbl methods - see the method documentation for details of individual data sources. `x` and `y` should usually be from the same data source, but if `copy` is `TRUE`, `y` will automatically be copied to the same source as `x`.

### Usage

    inner_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"),
      ...)

    left_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

    right_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"),
      ...)

    full_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

    semi_join(x, y, by = NULL, copy = FALSE, ...)

    nest_join(x, y, by = NULL, copy = FALSE, keep = FALSE, name = NULL,
      ...)

    anti_join(x, y, by = NULL, copy = 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 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 the same src as `x`. This allows you to join tables across srcs, but it is a 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.
`...` | other parameters passed onto methods, for instance, `na_matches` to control how `NA` values are matched. See join.tbl_df for more.
`keep` | If `TRUE` the by columns are kept in the nesting joins.
`name` | the name of the list column nesting joins create. If `NULL` the name of `y` is used.

### Join types

Currently dplyr supports four types of mutating joins, two types of filtering joins, and a nesting join.

Mutating joins combine variables from the two data.frames:

`inner_join()`

  return all rows from `x` where there are matching values in `y`, and all columns from `x` and `y`. If there are multiple matches between `x` and `y`, all combination of the matches are returned.

`left_join()`

  return all rows from `x`, and all columns from `x` and `y`. Rows in `x` with no match in `y` will have `NA` values in the new columns. If there are multiple matches between `x` and `y`, all combinations of the matches are returned.

`right_join()`

  return all rows from `y`, and all columns from `x` and y. Rows in `y` with no match in `x` will have `NA` values in the new columns. If there are multiple matches between `x` and `y`, all combinations of the matches are returned.

`full_join()`

  return all rows and all columns from both `x` and `y`. Where there are not matching values, returns `NA` for the one missing.

Filtering joins keep cases from the left-hand data.frame:

`semi_join()`

  return all rows from `x` where there are matching values in `y`, keeping just columns from `x`.

  A semi join differs from an inner join because an inner join will return one row of `x` for each matching row of `y`, where a semi join will never duplicate rows of `x`.

`anti_join()`

  return all rows from `x` where there are not matching values in `y`, keeping just columns from `x`.

Nesting joins create a list column of data.frames:

`nest_join()`

  return all rows and all columns from `x`. Adds a list column of tibbles. Each tibble contains all the rows from `y` that match that row of `x`. When there is no match, the list column is a 0-row tibble with the same column names and types as `y`.

  `nest_join()` is the most fundamental join since you can recreate the other joins from it. An `inner_join()` is a `nest_join()` plus an `tidyr::unnest()`, and `left_join()` is a `nest_join()` plus an `unnest(.drop = FALSE)`. A `semi_join()` is a `nest_join()` plus a `filter()` where you check that every element of data has at least one row, and an `anti_join()` is a `nest_join()` plus a `filter()` where you check every element has zero rows.

### Grouping

Groups are ignored for the purpose of joining, but the result preserves the grouping of `x`.

### Examples

    # "Mutating" joins combine variables from the LHS and RHS
    band_members %>% inner_join(band_instruments)
    band_members %>% left_join(band_instruments)
    band_members %>% right_join(band_instruments)
    band_members %>% full_join(band_instruments)

    # "Filtering" joins keep cases from the LHS
    band_members %>% semi_join(band_instruments)
    band_members %>% anti_join(band_instruments)

    # "Nesting" joins keep cases from the LHS and nests the RHS
    band_members %>% nest_join(band_instruments)

    # To suppress the message, supply by
    band_members %>% inner_join(band_instruments, by = "name")
    # This is good practice in production code

    # Use a named `by` if the join variables have different names
    band_members %>% full_join(band_instruments2, by = c("name" = "artist"))
    # Note that only the key from the LHS is kept

[Package dplyr version 0.8.0.1 Index]