dplyr if_else() vs base R ifelse()

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

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).

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