I'm looking to find a simple way to do something like the following but with dplyr, essentially just replacing the values in 3 columns with NA when the condition is met.
dta[dta$na.ind == 1, c('x1', 'x2', 'x3')] <- NA
The only method I can think of using dplyr is the following, but I feel there should be a simpler way
dta <- dta %>%
mutate(x1 = ifelse(na.ind == 1, NA, x1),
x2 = ifelse(na.ind == 1, NA, x2),
x3 = ifelse(na.ind == 1, NA, x3))
Thanks!
You can use mutate_at and pass the columns x1,x2,x3 to .vars parameter:
dta <- data.frame(na.ind = 1:3, x1 = 2:4, x2 = 2:4, x3 = 2:4, x4 = 2:4)
dta
# na.ind x1 x2 x3 x4
#1 1 2 2 2 2
#2 2 3 3 3 3
#3 3 4 4 4 4
dta %>% mutate_at(.vars = c("x1", "x2", "x3"), funs(ifelse(na.ind == 1, NA, .)))
# na.ind x1 x2 x3 x4
#1 1 NA NA NA 2
#2 2 3 3 3 3
#3 3 4 4 4 4
Since scoped verbs like mutate_at, mutate_all etc. are being replaced by across, you could now make the following adjustments to Psidom's answer:
> dta <- data.frame(na.ind = 1:3, x1 = 2:4, x2 = 2:4, x3 = 2:4, x4 = 2:4)
> dta
na.ind x1 x2 x3 x4
1 1 2 2 2 2
2 2 3 3 3 3
3 3 4 4 4 4
> dta %>% mutate(across(c(x1, x2, x3), ~ ifelse(na.ind == 1, NA, .)))
na.ind x1 x2 x3 x4
1 1 NA NA NA 2
2 2 3 3 3 3
3 3 4 4 4 4
See https://dplyr.tidyverse.org/reference/across.html for more information on across.
