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.
Arguments
- ...
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 == 0is treated as a variant ofn != 1.NULLinputs are ignored.
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.
Examples
x <- 1:50
case_when(
x %% 35 == 0 ~ "fizz buzz",
x %% 5 == 0 ~ "fizz",
x %% 7 == 0 ~ "buzz",
TRUE ~ as.character(x)
)
#> [1] "1" "2" "3" "4" "fizz" "6"
#> [7] "buzz" "8" "9" "fizz" "11" "12"
#> [13] "13" "buzz" "fizz" "16" "17" "18"
#> [19] "19" "fizz" "buzz" "22" "23" "24"
#> [25] "fizz" "26" "27" "buzz" "29" "fizz"
#> [31] "31" "32" "33" "34" "fizz buzz" "36"
#> [37] "37" "38" "39" "fizz" "41" "buzz"
#> [43] "43" "44" "fizz" "46" "47" "48"
#> [49] "buzz" "fizz"
# 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"
)
#> [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15"
#> [16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
#> [31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44" "45"
#> [46] "46" "47" "48" "49" "50"
# If none of the cases match, NA is used:
case_when(
x %% 5 == 0 ~ "fizz",
x %% 7 == 0 ~ "buzz",
x %% 35 == 0 ~ "fizz buzz"
)
#> [1] NA NA NA NA "fizz" NA "buzz" NA NA "fizz"
#> [11] NA NA NA "buzz" "fizz" NA NA NA NA "fizz"
#> [21] "buzz" NA NA NA "fizz" NA NA "buzz" NA "fizz"
#> [31] NA NA NA NA "fizz" NA NA NA NA "fizz"
#> [41] NA "buzz" NA NA "fizz" NA NA NA "buzz" "fizz"
# 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)
)
#> [1] "1" "nope" "nope" "nope" "fizz" "6"
#> [7] "buzz" "8" "9" "fizz" "11" "12"
#> [13] "13" "buzz" "fizz" "16" "17" "18"
#> [19] "19" "fizz" "buzz" "22" "23" "24"
#> [25] "fizz" "26" "27" "buzz" "29" "fizz"
#> [31] "31" "32" "33" "34" "fizz buzz" "36"
#> [37] "37" "38" "39" "fizz" "41" "buzz"
#> [43] "43" "44" "fizz" "46" "47" "48"
#> [49] "buzz" "fizz"
# 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)
)
#> [1] "1" NA NA NA "fizz" "6" "buzz" "8" "9" "fizz"
#> [11] "11" "12" "13" "buzz" "fizz" "16" "17" "18" "19" "fizz"
#> [21] "buzz" "22" "23" "24" "fizz" "26" "27" "buzz" "29" "fizz"
#> [31] "31" "32" "33" "34" NA "36" "37" "38" "39" "fizz"
#> [41] "41" "buzz" "43" "44" "fizz" "46" "47" "48" "buzz" "fizz"
case_when(
x %% 35 == 0 ~ 35,
x %% 5 == 0 ~ 5,
x %% 7 == 0 ~ 7,
TRUE ~ NA_real_
)
#> [1] NA NA NA NA 5 NA 7 NA NA 5 NA NA NA 7 5 NA NA NA NA 5 7 NA NA NA 5
#> [26] NA NA 7 NA 5 NA NA NA NA 35 NA NA NA NA 5 NA 7 NA NA 5 NA NA NA 7 5
# case_when() evaluates all RHS expressions, and then constructs its
# result by extracting the selected (via the LHS expressions) parts.
# In particular NaN are produced in this case:
y <- seq(-2, 2, by = .5)
case_when(
y >= 0 ~ sqrt(y),
TRUE ~ y
)
#> Warning: NaNs produced
#> [1] -2.0000000 -1.5000000 -1.0000000 -0.5000000 0.0000000 0.7071068 1.0000000
#> [8] 1.2247449 1.4142136
if (FALSE) {
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
mtcars %>%
mutate(
efficient = case_when(
mpg > 25 ~ TRUE,
TRUE ~ FALSE
)
)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> efficient
#> Mazda RX4 FALSE
#> Mazda RX4 Wag FALSE
#> Datsun 710 FALSE
#> Hornet 4 Drive FALSE
#> Hornet Sportabout FALSE
#> Valiant FALSE
#> Duster 360 FALSE
#> Merc 240D FALSE
#> Merc 230 FALSE
#> Merc 280 FALSE
#> Merc 280C FALSE
#> Merc 450SE FALSE
#> Merc 450SL FALSE
#> Merc 450SLC FALSE
#> Cadillac Fleetwood FALSE
#> Lincoln Continental FALSE
#> Chrysler Imperial FALSE
#> Fiat 128 TRUE
#> Honda Civic TRUE
#> Toyota Corolla TRUE
#> Toyota Corona FALSE
#> Dodge Challenger FALSE
#> AMC Javelin FALSE
#> Camaro Z28 FALSE
#> Pontiac Firebird FALSE
#> Fiat X1-9 TRUE
#> Porsche 914-2 TRUE
#> Lotus Europa TRUE
#> Ford Pantera L FALSE
#> Ferrari Dino FALSE
#> Maserati Bora FALSE
#> Volvo 142E FALSE