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") ) # S3 method for step_unorder tidy(x, ...)
recipe | 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 are affected by the 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. |
x | A |
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).
The factors level order is preserved during the transformation.
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_predictors()) factor_obj <- prep(factor_trans, training = examples)#> Warning: `step_unorder` requires ordered factors. Variables X1 will be ignored.#> #> Low Med High #> Low 4 0 0 #> Med 0 4 0 #> High 0 0 4#> # A tibble: 1 x 2 #> terms id #> <chr> <chr> #> 1 all_predictors() unorder_NRDyG#> # A tibble: 1 x 2 #> terms id #> <chr> <chr> #> 1 X2 unorder_NRDyG