step_unorder {recipes} | R Documentation |
step_unorder
creates a specification of a recipe
step that will transform the data.
step_unorder( recipe, ..., role = NA, trained = FALSE, columns = NULL, skip = FALSE, id = rand_id("unorder") )
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 |
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. |
columns |
A character string of variable names that will
be populated (eventually) by the |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
The factors level order is preserved during the transformation.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with column
terms
(the columns that will be affected) is returned.
The underlying operation does not allow for case weights.
Other dummy variable and encoding steps:
step_bin2factor()
,
step_count()
,
step_date()
,
step_dummy_extract()
,
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_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) tidy(factor_trans, number = 1) tidy(factor_obj, number = 1)