Convert Strings to Factors

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

step_string2factor(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  levels = NULL,
  ordered = FALSE,
  skip = FALSE,
  id = rand_id("string2factor")
)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	One or more selector functions to choose variables for this step. See selections() for more details.
role	Not used by this step since no new variables are created.
trained	A logical to indicate if the quantities for preprocessing have been estimated.
levels	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.
ordered	A single logical value; should the factor(s) be ordered?
skip	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.
id	A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

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).

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and ordered is returned.

See also

Other dummy variable and encoding steps: step_bin2factor(), step_count(), step_date(), step_dummy_multi_choice(), step_dummy(), step_factor2string(), step_holiday(), step_indicate_na(), step_integer(), step_novel(), step_num2factor(), step_ordinalscore(), step_other(), step_regex(), step_relevel(), step_unknown(), step_unorder()

Examples

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 × 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 × 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 × 3
#>   terms ordered id                 
#>   <chr> <lgl>   <chr>              
#> 1 diet  FALSE   string2factor_a3AY0