step_clean_levels
creates a specification of a recipe step that will
clean nominal data (character or factor) so the levels consist only of
letters, numbers, and the underscore.
Usage
step_clean_levels(
recipe,
...,
role = NA,
trained = FALSE,
clean = NULL,
skip = FALSE,
id = rand_id("clean_levels")
)
Arguments
- 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
recipes::selections()
for more details.- 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.
- clean
A named character vector to clean and recode categorical levels. This is
NULL
until computed byrecipes::prep.recipe()
. Note that if the original variable is a character vector, it will be converted to a factor.- skip
A logical. Should the step be skipped when the recipe is baked by
recipes::bake.recipe()
? While all operations are baked whenrecipes::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 usingskip = FALSE
.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe
with the new step added
to the sequence of existing steps (if any).
Details
The new levels are cleaned and then reset with dplyr::recode_factor()
. When
data to be processed contains novel levels (i.e., not contained in the
training set), they are converted to missing.
Tidying
When you tidy()
this step, a tibble with columns terms
(the selectors or variables selected), original
(the original levels) and
value
(the cleaned levels) is returned.
See also
step_clean_names()
, recipes::step_factor2string()
,
recipes::step_string2factor()
, recipes::step_regex()
,
recipes::step_unknown()
, recipes::step_novel()
, recipes::step_other()
Other Steps for Text Cleaning:
step_clean_names()
Examples
library(recipes)
library(modeldata)
data(Smithsonian)
smith_tr <- Smithsonian[1:15, ]
smith_te <- Smithsonian[16:20, ]
rec <- recipe(~., data = smith_tr)
if (requireNamespace("janitor", quietly = TRUE)) {
rec <- rec %>%
step_clean_levels(name)
rec <- prep(rec, training = smith_tr)
cleaned <- bake(rec, smith_tr)
tidy(rec, number = 1)
# novel levels are replaced with missing
bake(rec, smith_te)
}
#> # A tibble: 5 × 3
#> name latitude longitude
#> <fct> <dbl> <dbl>
#> 1 NA 38.9 -77.0
#> 2 NA 38.9 -77.0
#> 3 NA 38.9 -77.0
#> 4 NA 38.9 -77.0
#> 5 NA 38.9 -77.1