Missing Value Column FilterSource:
step_filter_missing creates a specification of a recipe
step that will potentially remove variables that have too many missing
step_filter_missing( recipe, ..., role = NA, trained = FALSE, threshold = 0.1, removals = NULL, skip = FALSE, id = rand_id("filter_missing") )
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 value for the threshold of missing values in column. The step will remove the columns where the proportion of missing values exceeds the threshold.
A character string that contains the names of columns that should be removed. These values are not determined until
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 = TRUEas 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.
This step can potentially remove columns from the data set. This may cause issues for subsequent steps in your recipe if the missing columns are specifically referenced by name. To avoid this, see the advice in the Tips for saving recipes and filtering columns section of selections.
This step will remove variables if the proportion of missing
values exceeds the
All variables with missing values will be removed for
threshold = 0.
tidy() this step, a tibble with column
terms (the columns that will be removed) is returned.
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in case_weights and the examples on
data(credit_data, package = "modeldata") rec <- recipe(Status ~ ., data = credit_data) %>% step_filter_missing(all_predictors(), threshold = 0) filter_obj <- prep(rec) filtered_te <- bake(filter_obj, new_data = NULL) tidy(rec, number = 1) #> # A tibble: 1 × 2 #> terms id #> <chr> <chr> #> 1 all_predictors() filter_missing_IYaDd tidy(filter_obj, number = 1) #> # A tibble: 6 × 2 #> terms id #> <chr> <chr> #> 1 Home filter_missing_IYaDd #> 2 Marital filter_missing_IYaDd #> 3 Job filter_missing_IYaDd #> 4 Income filter_missing_IYaDd #> 5 Assets filter_missing_IYaDd #> 6 Debt filter_missing_IYaDd