View source: R/step_hcai_missing.R
step_missing | R Documentation |
step_missing
creates a specification of a recipe that
will replace NA values with a new factor level, missing
.
step_missing( recipe, ..., role = NA, trained = FALSE, na_percentage = NULL, skip = FALSE, id = rand_id("bagimpute") ) ## S3 method for class 'step_missing' 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 number of NA values have been counted in preprocessing. |
na_percentage |
A named numeric vector of NA percentages. This
is |
skip |
A logical. Should the step be skipped when the recipe is baked? |
id |
a unique step id that will be used to unprep |
x |
A 'step_missing' object. |
NA values are counted when the recipe is trained using
prep.recipe
. bake.recipe
then fills in the missing values for
the new data.
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
selectors or variables selected) and value
(the
NA counts).
library(recipes) n = 100 d <- tibble::tibble(encounter_id = 1:n, patient_id = sample(1:20, size = n, replace = TRUE), hemoglobin_count = rnorm(n, mean = 15, sd = 1), hemoglobin_category = sample(c("Low", "Normal", "High", NA), size = n, replace = TRUE), disease = ifelse(hemoglobin_count < 15, "Yes", "No") ) # Initialize my_recipe <- recipe(disease ~ ., data = d) # Create recipe my_recipe <- my_recipe %>% step_missing(all_nominal()) my_recipe # Train recipe trained_recipe <- prep(my_recipe, training = d) # Apply recipe data_modified <- bake(trained_recipe, new_data = d)
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