Skip to content

step_unorder() creates a specification of a recipe step that will turn ordered factor variables into unordered factor variables.


  role = NA,
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("unorder")



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.


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


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


A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.


A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() 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.


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


The factors level order is preserved during the transformation.


When you tidy() this step, a tibble with column terms (the columns that will be affected) is returned.

Case weights

The underlying operation does not allow for case weights.


lmh <- c("Low", "Med", "High")

examples <- data.frame(
  X1 = factor(rep(letters[1:4], each = 3)),
  X2 = ordered(rep(lmh, each = 4),
    levels = lmh

rec <- recipe(~ X1 + X2, data = examples)

factor_trans <- rec %>%

factor_obj <- prep(factor_trans, training = examples)

transformed_te <- bake(factor_obj, examples)
table(transformed_te$X2, examples$X2)
#>        Low Med High
#>   Low    4   0    0
#>   Med    0   4    0
#>   High   0   0    4

tidy(factor_trans, number = 1)
#> # A tibble: 1 × 2
#>   terms                    id           
#>   <chr>                    <chr>        
#> 1 all_nominal_predictors() unorder_uevI1
tidy(factor_obj, number = 1)
#> # A tibble: 2 × 2
#>   terms id           
#>   <chr> <chr>        
#> 1 X1    unorder_uevI1
#> 2 X2    unorder_uevI1