dplyr if_else() vs base R ifelse()

Question

I am fairly proficient within the Tidyverse, but have always used ifelse() instead of dplyr if_else(). I want to switch this behavior and default t

Answer 1

I'd also add that if_else() can attribute a value in case of NA, which is a handy way of adding an extra condition.

df <- data_frame(val = c(80, 90, NA, 110))
df %>% mutate(category = if_else(val < 100, 1, 2, missing = 9))

#     val category
#   <dbl>    <dbl>
# 1    80        1
# 2    90        1
# 3    NA        9
# 4   110        2

Answer 2

Another reason to choose if_else over ifelse is that ifelse turns Date into numeric objects

Dates <- as.Date(c('2018-10-01', '2018-10-02', '2018-10-03'))
new_Dates <- ifelse(Dates == '2018-10-02', Dates + 1, Dates)
str(new_Dates)

#>  num [1:3] 17805 17807 17807

if_else is also faster than ifelse.

Note that when testing multiple conditions, the code would be more readable and less error-prone if we use case_when.

library(dplyr)

case_when(
  Dates == '2018-10-01' ~ Dates - 1,
  Dates == '2018-10-02' ~ Dates + 1,
  Dates == '2018-10-03' ~ Dates + 2,
  TRUE ~ Dates
)

#> [1] "2018-09-30" "2018-10-03" "2018-10-05"

Created on 2018-06-01 by the reprex package (v0.2.0).

Answer 3

Another important reason for preferring if_else() to ifelse() is checking for consistency in lengths. See this dangerous gotcha:

> tibble(x = 1:3, y = ifelse(TRUE, x, 4:6))
# A tibble: 3 x 2
      x     y
  <int> <int>
1     1     1
2     2     1
3     3     1

Compare with

> tibble(x = 1:3, y = if_else(TRUE, x, 4:6))
    Error: `true` must be length 1 (length of `condition`), not 3.

The intention in both cases is clearly for column y to equal x or to equal 4:6 acording to the value of a single (scalar) logical variable; ifelse() silently truncates its output to length 1, which is then silently recycled. if_else() catches what is almost certainly an error at source.

Answer 4

if_else is more strict. It checks that both alternatives are of the same type and otherwise throws an error, while ifelse will promote types as necessary. This may be a benefit in some circumstances, but may otherwise break scripts if you don't check for errors or explicitly force type conversion. For example:

ifelse(c(TRUE,TRUE,FALSE),"a",3)
[1] "a" "a" "3"
if_else(c(TRUE,TRUE,FALSE),"a",3)
Error: `false` must be type character, not double