step_unknown creates a specification of a recipe step that will assign a missing value in a factor level to"unknown".

  role = NA,
  trained = FALSE,
  new_level = "unknown",
  objects = NULL,
  skip = FALSE,
  id = rand_id("unknown")

# S3 method for step_unknown
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 that will be affected by the step. These variables should be character or factor types. 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.


A single character value that will be assigned to new factor levels.


A list of objects that contain the information on factor levels that will be determined by prep.recipe().


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_unknown 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 columns that will be affected) and value (the factor levels that is used for the new value)


The selected variables are adjusted to have a new level (given by new_level) that is placed in the last position.

Note that if the original columns are character, they will be converted to factors by this step.

If new_level is already in the data given to prep, an error is thrown.

See also


library(modeldata) data(okc) rec <- recipe(~ diet + location, data = okc) %>% step_unknown(diet, new_level = "unknown diet") %>% step_unknown(location, new_level = "unknown location") %>% prep() table(bake(rec, new_data = NULL) %>% pull(diet), okc %>% pull(diet), useNA = "always") %>% %>% dplyr::filter(Freq > 0)
#> Var1 Var2 Freq #> 1 anything anything 6174 #> 2 halal halal 11 #> 3 kosher kosher 11 #> 4 mostly anything mostly anything 16562 #> 5 mostly halal mostly halal 48 #> 6 mostly kosher mostly kosher 86 #> 7 mostly other mostly other 1004 #> 8 mostly vegan mostly vegan 335 #> 9 mostly vegetarian mostly vegetarian 3438 #> 10 other other 331 #> 11 strictly anything strictly anything 5107 #> 12 strictly halal strictly halal 18 #> 13 strictly kosher strictly kosher 18 #> 14 strictly other strictly other 450 #> 15 strictly vegan strictly vegan 227 #> 16 strictly vegetarian strictly vegetarian 874 #> 17 vegan vegan 136 #> 18 vegetarian vegetarian 665 #> 19 unknown diet <NA> 24360
tidy(rec, number = 1)
#> # A tibble: 1 x 3 #> terms value id #> <chr> <chr> <chr> #> 1 diet unknown diet unknown_ZB2lW