Join data frame tbls

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

# S3 method for data.frame
nest_join(x, y, by = NULL, copy = FALSE,
  keep = FALSE, name = NULL, ...)

# S3 method for tbl_df
inner_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...,
  na_matches = pkgconfig::get_config("dplyr::na_matches"))

# S3 method for tbl_df
nest_join(x, y, by = NULL, copy = FALSE,
  keep = FALSE, name = NULL, ...)

# S3 method for tbl_df
left_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...,
  na_matches = pkgconfig::get_config("dplyr::na_matches"))

# S3 method for tbl_df
right_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...,
  na_matches = pkgconfig::get_config("dplyr::na_matches"))

# S3 method for tbl_df
full_join(x, y, by = NULL, copy = FALSE,
  suffix = c(".x", ".y"), ...,
  na_matches = pkgconfig::get_config("dplyr::na_matches"))

# S3 method for tbl_df
semi_join(x, y, by = NULL, copy = FALSE, ...,
  na_matches = pkgconfig::get_config("dplyr::na_matches"))

# S3 method for tbl_df
anti_join(x, y, by = NULL, copy = FALSE, ...,
  na_matches = pkgconfig::get_config("dplyr::na_matches"))

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 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.
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.
...	included for compatibility with the generic; otherwise ignored.
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.
na_matches	Use `"never"` to always treat two `NA` or `NaN` values as different, like joins for database sources, similarly to `merge(incomparables = FALSE)`. The default, `"na"`, always treats two `NA` or `NaN` values as equal, like `merge()`. Users and package authors can change the default behavior by calling `pkgconfig::set_config("dplyr::na_matches" = "never")`.

Examples

if (require("Lahman")) {
batting_df <- tbl_df(Batting)
person_df <- tbl_df(Master)

uperson_df <- tbl_df(Master[!duplicated(Master$playerID), ])

# Inner join: match batting and person data
inner_join(batting_df, person_df)
inner_join(batting_df, uperson_df)

# Left join: match, but preserve batting data
left_join(batting_df, uperson_df)

# Anti join: find batters without person data
anti_join(batting_df, person_df)
# or people who didn't bat
anti_join(person_df, batting_df)
}
#> Loading required package: Lahman
#> Joining, by = "playerID"
#> Joining, by = "playerID"
#> Joining, by = "playerID"
#> Joining, by = "playerID"
#> Joining, by = "playerID"
#> # A tibble: 189 x 26
#>    playerID birthYear birthMonth birthDay birthCountry birthState birthCity
#>    <chr>        <int>      <int>    <int> <chr>        <chr>      <chr>    
#>  1 actama99      1969          1       11 D.R.         San Pedro… San Pedr…
#>  2 adairbi…      1913          2       10 USA          AL         Mobile   
#>  3 armoubi…      1869          9        3 USA          PA         Homestead
#>  4 bancrfr…      1846          5        9 USA          MA         Lancaster
#>  5 barlial…      1915          4        2 USA          IL         Springfi…
#>  6 barroed…      1868          5       10 USA          IL         Springfi…
#>  7 bellco99      1903          5       17 USA          MS         Starkvil…
#>  8 bevinte…      1956          7        7 USA          OH         Akron    
#>  9 bezdehu…      1883          4        1 Czech Repub… <NA>       Prague   
#> 10 bicke99       1848          8       NA USA          DC         Washingt…
#> # … with 179 more rows, and 19 more variables: deathYear <int>,
#> #   deathMonth <int>, deathDay <int>, deathCountry <chr>, deathState <chr>,
#> #   deathCity <chr>, nameFirst <chr>, nameLast <chr>, nameGiven <chr>,
#> #   weight <int>, height <int>, bats <fct>, throws <fct>, debut <chr>,
#> #   finalGame <chr>, retroID <chr>, bbrefID <chr>, deathDate <date>,
#> #   birthDate <date>

Arguments

Examples

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