step_string2factor will convert one or more character vectors to factors (ordered or unordered).

  role = NA,
  trained = FALSE,
  levels = NULL,
  ordered = FALSE,
  skip = FALSE,
  id = rand_id("string2factor")

# S3 method for step_string2factor
tidy(x, ...)



A recipe object. The step will be added to the sequence of operations for this recipe.


One or more selector functions to choose which variables will be converted to factors. See selections() for more details. For the tidy method, these are not currently used.


Not used by this step since no new variables are created.


A logical to indicate if the quantities for preprocessing have been estimated.


An options specification of the levels to be used for the new factor. If left NULL, the sorted unique values present when bake is called will be used.


A single logical value; should the factor(s) be ordered?


A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations


A character string that is unique to this step to identify it.


A step_string2factor object.


An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms (the selectors or variables selected) and ordered.


If levels is given, step_string2factor will convert all variables affected by this step to have the same levels.

Also, note that prep has an option strings_as_factors that defaults to TRUE. This should be changed so that raw character data will be applied to step_string2factor. However, this step can also take existing factors (but will leave them as-is).

See also


library(modeldata) data(okc) rec <- recipe(~ diet + location, data = okc) make_factor <- rec %>% step_string2factor(diet) make_factor <- prep(make_factor, training = okc, strings_as_factors = FALSE) # note that `diet` is a factor bake(make_factor, new_data = NULL) %>% head
#> # A tibble: 6 x 2 #> diet location #> <fct> <chr> #> 1 strictly anything south san francisco #> 2 mostly other oakland #> 3 anything san francisco #> 4 vegetarian berkeley #> 5 NA san francisco #> 6 mostly anything san francisco
okc %>% head
#> # A tibble: 6 x 6 #> age diet height location date Class #> <int> <chr> <int> <chr> <date> <fct> #> 1 22 strictly anything 75 south san francisco 2012-06-28 other #> 2 35 mostly other 70 oakland 2012-06-29 other #> 3 38 anything 68 san francisco 2012-06-27 other #> 4 23 vegetarian 71 berkeley 2012-06-28 other #> 5 29 NA 66 san francisco 2012-06-27 other #> 6 29 mostly anything 67 san francisco 2012-06-29 stem
tidy(make_factor, number = 1)
#> # A tibble: 1 x 3 #> terms ordered id #> <chr> <lgl> <chr> #> 1 diet FALSE string2factor_a3AY0