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Write the code to recode several factors

Source: R/utils.R
fct_recode_helper.Rd

Recoding a factor with forcats::fct_recode() means typing every level name exactly, and a typo is silently ignored. fct_recode_helper() writes that code for you: it prints a ready-to-paste mutate() call with one fct_recode() per variable, each level already written as "level" = "level". You then only edit the new names on the left, and delete the lines you keep.

With a few variables, each level carries its frequency and count as a comment, which is what tells you which small levels to merge. A column with a label attribute (data imported by haven) gets that label as a comment title.

Usage

fct_recode_helper(
  data,
  .cols = -where(is.numeric),
  name_in,
  name_out,
  freq = NULL,
  style = c("mutate", "base"),
  reminder = TRUE,
  cat = TRUE
)

Arguments

data

The data frame.

.cols

<tidy-select> The variables to recode. Default: every non-numeric column.

name_in

The input data frame's name (default: the expression given as data).

name_out

The output data frame's name, if different from name_in (used by style = "base").

freq

Print each level's frequency and count as a comment; defaults to TRUE when 5 or fewer variables are given.

style

"mutate" (default) writes a dplyr::mutate() call; "base" writes data$var <-.

reminder

Print a "new" = "old" syntax reminder. Default TRUE.

cat

Print to console, or open a temporary file when there are more than 5 variables; FALSE returns a data frame of the recode text instead.

Value

With cat = TRUE (default), the text printed to console (or written to a temp R file for more than 5 variables), returned invisibly. With cat = FALSE, a tibble of the recode text.

Examples

fct_recode_helper(forcats::gss_cat, c(marital, race))
#> forcats::gss_cat |>
#> mutate(
#>  marital = fct_recode(   # "new" = "old" 
#> marital,
#> "No answer"     = "No answer"    , #  0%     17
#> "Never married" = "Never married", # 25%  5 416
#> "Separated"     = "Separated"    , #  3%    743
#> "Divorced"      = "Divorced"     , # 16%  3 383
#> "Widowed"       = "Widowed"      , #  8%  1 807
#> "Married"       = "Married"      , # 47% 10 117
#> ),
#> 
#>  race = fct_recode(   # "new" = "old" 
#> race,
#> "Other" = "Other", #  9%  1 959
#> "Black" = "Black", # 15%  3 129
#> "White" = "White", # 76% 16 395
#> ),
#> 
#>  )