dplyr (version 1.0.10)

case_when: A general vectorised if

Description

This function allows you to vectorise multiple if_else() statements. It is an R equivalent of the SQL CASE WHEN statement. If no cases match, NA is returned.

Usage

case_when(...)

Value

A vector of length 1 or n, matching the length of the logical input or output vectors, with the type (and attributes) of the first RHS. Inconsistent lengths or types will generate an error.

Arguments

...

<dynamic-dots> A sequence of two-sided formulas. The left hand side (LHS) determines which values match this case. The right hand side (RHS) provides the replacement value.

The LHS must evaluate to a logical vector. The RHS does not need to be logical, but all RHSs must evaluate to the same type of vector.

Both LHS and RHS may have the same length of either 1 or n. The value of n must be consistent across all cases. The case of n == 0 is treated as a variant of n != 1.

NULL inputs are ignored.

Examples

x <- 1:50
case_when(
  x %% 35 == 0 ~ "fizz buzz",
  x %% 5 == 0 ~ "fizz",
  x %% 7 == 0 ~ "buzz",
  TRUE ~ as.character(x)
)

# Like an if statement, the arguments are evaluated in order, so you must
# proceed from the most specific to the most general. This won't work:
case_when(
  TRUE ~ as.character(x),
  x %%  5 == 0 ~ "fizz",
  x %%  7 == 0 ~ "buzz",
  x %% 35 == 0 ~ "fizz buzz"
)

# If none of the cases match, NA is used:
case_when(
  x %%  5 == 0 ~ "fizz",
  x %%  7 == 0 ~ "buzz",
  x %% 35 == 0 ~ "fizz buzz"
)

# Note that NA values in the vector x do not get special treatment. If you want
# to explicitly handle NA values you can use the `is.na` function:
x[2:4] <- NA_real_
case_when(
  x %% 35 == 0 ~ "fizz buzz",
  x %% 5 == 0 ~ "fizz",
  x %% 7 == 0 ~ "buzz",
  is.na(x) ~ "nope",
  TRUE ~ as.character(x)
)

# All RHS values need to be of the same type. Inconsistent types will throw an error.
# This applies also to NA values used in RHS: NA is logical, use
# typed values like NA_real_, NA_complex, NA_character_, NA_integer_ as appropriate.
case_when(
  x %% 35 == 0 ~ NA_character_,
  x %% 5 == 0 ~ "fizz",
  x %% 7 == 0 ~ "buzz",
  TRUE ~ as.character(x)
)
case_when(
  x %% 35 == 0 ~ 35,
  x %% 5 == 0 ~ 5,
  x %% 7 == 0 ~ 7,
  TRUE ~ NA_real_
)

# case_when() evaluates all RHS expressions, and then constructs its
# result by extracting the selected (via the LHS expressions) parts.
# In particular NaNs are produced in this case:
y <- seq(-2, 2, by = .5)
case_when(
  y >= 0 ~ sqrt(y),
  TRUE   ~ y
)

# This throws an error as NA is logical not numeric
try(case_when(
  x %% 35 == 0 ~ 35,
  x %% 5 == 0 ~ 5,
  x %% 7 == 0 ~ 7,
  TRUE ~ NA
))

# case_when is particularly useful inside mutate when you want to
# create a new variable that relies on a complex combination of existing
# variables
starwars %>%
  select(name:mass, gender, species) %>%
  mutate(
    type = case_when(
      height > 200 | mass > 200 ~ "large",
      species == "Droid"        ~ "robot",
      TRUE                      ~ "other"
    )
  )


# `case_when()` is not a tidy eval function. If you'd like to reuse
# the same patterns, extract the `case_when()` call in a normal
# function:
case_character_type <- function(height, mass, species) {
  case_when(
    height > 200 | mass > 200 ~ "large",
    species == "Droid"        ~ "robot",
    TRUE                      ~ "other"
  )
}

case_character_type(150, 250, "Droid")
case_character_type(150, 150, "Droid")

# Such functions can be used inside `mutate()` as well:
starwars %>%
  mutate(type = case_character_type(height, mass, species)) %>%
  pull(type)

# `case_when()` ignores `NULL` inputs. This is useful when you'd
# like to use a pattern only under certain conditions. Here we'll
# take advantage of the fact that `if` returns `NULL` when there is
# no `else` clause:
case_character_type <- function(height, mass, species, robots = TRUE) {
  case_when(
    height > 200 | mass > 200      ~ "large",
    if (robots) species == "Droid" ~ "robot",
    TRUE                           ~ "other"
  )
}

starwars %>%
  mutate(type = case_character_type(height, mass, species, robots = FALSE)) %>%
  pull(type)