avoid use of dplyr::coalesce in favour of faster alternative dplyr::if_else

[whipson]

library(microbenchmark)
library(dplyr)

df_w_missing <- data.frame(
  r1 = 1:100,
  r2 = sample(letters, 100, replace = TRUE),
  r3 = sample(LETTERS, 100, replace = TRUE),
  r4 = Sys.time() - runif(100, -1e6, 1e6)
) |> 
  mutate(across(everything(), ~if_else(row_number() %in% sample(1:100, size = 25), NA, .x)))

microbenchmark(
  coalesce = {
    df_w_missing |> 
      mutate(
        r1 = coalesce(r1, 0),
        r2 = coalesce(r2, "a"),
        r3 = coalesce(r3, "A"),
        r4 = coalesce(r4, as.POSIXct("2024-05-01 10:00:00"))
      )
  },
  if_else = {
    df_w_missing |> 
      mutate(
        r1 = if_else(is.na(r1), 0, r1),
        r2 = if_else(is.na(r2), "a", r2),
        r3 = if_else(is.na(r3), "A", r3),
        r4 = if_else(is.na(r4), as.POSIXct("2024-05-01 10:00:00"), r4)
      )
  }
)

Unit: microseconds
     expr     min       lq     mean   median      uq      max neval
 coalesce 765.921 785.7035 960.2544 798.2905 811.595 8736.075   100
  if_else 680.518 696.0365 712.3824 708.9105 721.928  830.496   100