step_unorder() creates a specification of a recipe step that will turn
ordered factor variables into unordered factor variables.
step_unorder(
recipe,
...,
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 is returned with
columns terms and id:
character, the selectors or variables selected
character, id of this step
The underlying operation does not allow for case weights.
Other dummy variable and encoding steps:
step_bin2factor(),
step_count(),
step_date(),
step_dummy(),
step_dummy_extract(),
step_dummy_multi_choice(),
step_factor2string(),
step_holiday(),
step_indicate_na(),
step_integer(),
step_novel(),
step_num2factor(),
step_ordinalscore(),
step_other(),
step_regex(),
step_relevel(),
step_string2factor(),
step_time(),
step_unknown()
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 |>
step_unorder(all_nominal_predictors())
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