mutate_joins {poorman}

Mutating Joins

Description

The mutating joins add columns from y to x, matching rows based on the keys:

• inner_join(): includes all rows in x and y.

• left_join(): includes all rows in x.

• right_join(): includes all rows in y.

• full_join(): includes all rows in x or y.

If a row in x matches multiple rows in y, all the rows in y will be returned once for each matching row in x.

Usage

inner_join(
  x,
  y,
  by = NULL,
  suffix = c(".x", ".y"),
  ...,
  na_matches = c("na", "never")
)

left_join(
  x,
  y,
  by = NULL,
  suffix = c(".x", ".y"),
  ...,
  keep = FALSE,
  na_matches = c("na", "never")
)

right_join(
  x,
  y,
  by = NULL,
  suffix = c(".x", ".y"),
  ...,
  keep = FALSE,
  na_matches = c("na", "never")
)

full_join(
  x,
  y,
  by = NULL,
  suffix = c(".x", ".y"),
  ...,
  keep = FALSE,
  na_matches = c("na", "never")
)

Arguments

x, y

The data.frames 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.

To join by multiple variables, use a vector with length > 1. For example, by = c("a", "b") will match x$a to y$a and x$b to y$b. Use a named vector to match different variables in x and y. For example, by = c("a" = "b", "c" = "d") will match x$a to y$b and x$c to y$d.

To perform a cross-join, generating all combinations of x and y, use by = character().

suffix

character(2). If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them.

...

Additional arguments to pass to merge()

na_matches

Should NA and NaN values match one another?

The default, "na", treats two NA or NaN values as equal, like %in%, match(), merge().

Use "never" to always treat two NA or NaN values as different, like joins for database sources, similarly to merge(incomparables = FALSE).

keep

logical(1). Should the join keys from both x and y be preserved in the output? Only applies to left_join(), right_join(), and full_join().

Value

A data.frame. The order of the rows and columns of x is preserved as much as possible. The output has the following properties:

• For inner_join(), a subset of x rows. For left_join(), all x rows. For right_join(), a subset of x rows, followed by unmatched y rows. For full_join(), all x rows, followed by unmatched y rows.

• For all joins, rows will be duplicated if one or more rows in x matches multiple rows in y.

• Output columns include all x columns and all y columns. If columns in x and y have the same name (and aren't included in by), suffixes are added to disambiguate.

• Output columns included in by are coerced to common type across x and y.

• Groups are taken from x.

Examples

# If a row in `x` matches multiple rows in `y`, all the rows in `y` will be
# returned once for each matching row in `x`
df1 <- data.frame(x = 1:3)
df2 <- data.frame(x = c(1, 1, 2), y = c("first", "second", "third"))
df1 %>% left_join(df2)

# By default, NAs match other NAs so that there are two
# rows in the output of this join:
df1 <- data.frame(x = c(1, NA), y = 2)
df2 <- data.frame(x = c(1, NA), z = 3)
left_join(df1, df2)

# You can optionally request that NAs don't match, giving a
# a result that more closely resembles SQL joins
left_join(df1, df2, na_matches = "never")