Recode multiple columns using dplyr

I think it is related the column type. I added mutate_if to convert all integer columns to numeric, and then set the recode value to be NA_real_. It seems working.

library(dplyr)

y <- data.frame(y1=c(1,2,999,3,4), y2=c(1L, 2L, 999L, 3L, 4L), y3=c(T,T,F,F,T))

z <- y %>%
  mutate_if(is.integer, as.numeric) %>%
  mutate_at(vars(y1:y2), funs(recode(.,`999` = NA_real_)))
z
#   y1 y2    y3
# 1  1  1  TRUE
# 2  2  2  TRUE
# 3 NA NA FALSE
# 4  3  3 FALSE
# 5  4  4  TRUE

I'm having trouble understanding exactly what you want to accomplish, so let me know if this isn't quite it.

library(dplyr)

y <- data.frame(y1=c(1,2,999,3,4), y2=c(1L, 2L, 999L, 3L, 4L), y3=c(T,T,F,F,T))

y

#>    y1  y2    y3
#> 1   1   1  TRUE
#> 2   2   2  TRUE
#> 3 999 999 FALSE
#> 4   3   3 FALSE
#> 5   4   4  TRUE

z <- y %>%
  mutate_at(vars(y1:y2), ~ifelse(. == 999, NA, .))

z

#>   y1 y2    y3
#> 1  1  1  TRUE
#> 2  2  2  TRUE
#> 3 NA NA FALSE
#> 4  3  3 FALSE
#> 5  4  4  TRUE

Now that funs has been depreciated in dplyr, here's the new way to go:

z <- y %>%
  mutate_if(is.integer, as.numeric) %>%
  mutate_at(vars(y1:y2), list(~recode(.,`999` = NA_real_)))

Replace funs with list and insert a ~ before recode.