Block usage of nested ifelse() calls

Calling ifelse() in nested calls is problematic for two main reasons:

1. It can be hard to read – mapping the code to the expected output for such code can be a messy task/require a lot of mental bandwidth, especially for code that nests more than once

2. It is inefficient – ifelse() can evaluate all of its arguments at both yes and no (see https://stackoverflow.com/q/16275149); this issue is exacerbated for nested calls

Usage

nested_ifelse_linter()

Details

Users can instead rely on a more readable alternative modeled after SQL CASE WHEN statements.

Let's say this is our original code:

ifelse(
  x == "a",
  2L,
  ifelse(x == "b", 3L, 1L)
)

Here are a few ways to avoid nesting and make the code more readable:

• Use data.table::fcase()

  data.table::fcase(
    x == "a", 2L,
    x == "b", 3L,
    default = 1L
  )

• Use dplyr::case_match()

  dplyr::case_match(
    x,
    "a" ~ 2L,
    "b" ~ 3L,
    .default = 1L
  )

• Use a look-up-and-merge approach (build a mapping table between values and outputs and merge this to the input)

  default <- 1L
  values <- data.frame(
    a = 2L,
    b = 3L
  )
  found_value <- values[[x]]
  ifelse(is.null(found_value), default, found_value)

See also

linters for a complete list of linters available in lintr.

Tags

efficiency, readability

Examples

# will produce lints
lint(
  text = 'ifelse(x == "a", 1L, ifelse(x == "b", 2L, 3L))',
  linters = nested_ifelse_linter()
)
#> <text>:1:22: warning: [nested_ifelse_linter] Don't use nested ifelse() calls; instead, try (1) data.table::fcase; (2) dplyr::case_when; or (3) using a lookup table.
#> ifelse(x == "a", 1L, ifelse(x == "b", 2L, 3L))
#>                      ^~~~~~~~~~~~~~~~~~~~~~~~

# okay
lint(
  text = 'dplyr::case_when(x == "a" ~ 1L, x == "b" ~ 2L, TRUE ~ 3L)',
  linters = nested_ifelse_linter()
)
#> ℹ No lints found.

lint(
  text = 'data.table::fcase(x == "a", 1L, x == "b", 2L, default = 3L)',
  linters = nested_ifelse_linter()
)
#> ℹ No lints found.