Typed NAs and dplyr's `case_when()`

case_when.R

# Dplyr's `case_when` function is great for doing nested `ifelse()` functions:
#   it requires less keystrokes and is somewhat faster as well.
# The only "problem" is that it requires "strict typing," which can pose a problem because of NAs.
# Let's see how this works

# A data frame 
df <- data.frame(
  x = runif(100),
  y = runif(100)+0.2 # So we have a few values > 1
)

# Let's try `case_when()`
df$x_factor <- factor(dplyr::case_when(df$x < 0.33 ~ "one",
                                       df$x >= 0.33 & df$x < 0.66 ~ "two",
                                       TRUE ~ "three")) # The default value

# Won't work because `case_when` needs all values to be the same type!
df$x_char <- dplyr::case_when(df$x < 0.33 ~ "uno",
                              df$x >= 0.33 & df$x < 0.66 ~ "dos",
                              TRUE ~ NA) # This value isn't a character, it's an NA, which by default is of type "logical"!

# See:
is.character(NA)
is.logical(NA)

# However, there are TYPED NAs in R!
# `NA_character_`, `NA_integer_`, `NA_real_`, `NA_complex_`
# See:
is.character(NA_character_)

is.numeric(NA_integer_)
is.integer(NA_integer_)

is.numeric(NA_real_)
is.integer(NA_real_)

# Therefore:
df %>%
  # `case_when` can also be used in `tidyverse` code, e.g., `mutate`, etc.
  mutate(y_char = dplyr::case_when(y < 0.33 ~ "one",  # Arguments will be evaluated in order
                                   y < 0.66 ~ "two",  #   so in this case we don't need the >'s.
                                   y < 1 ~ "three",     # `case_when` needs all the elements to
                                   TRUE ~ NA_character_)) # be the same type, so normal NAs won't do