Zero Variance FilterSource:
step_zv creates a specification of a recipe step
that will remove variables that contain only a single value.
step_zv( recipe, ..., role = NA, trained = FALSE, group = NULL, removals = NULL, skip = FALSE, id = rand_id("zv") )
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.
An optional character string or call to
dplyr::vars()that can be used to specify a group(s) within which to identify variables that contain only a single value. If the grouping variables are contained in terms selector, they will not be considered for removal.
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.
tidy() this step, a tibble with column
terms (the columns that will be removed) is returned.
library(modeldata) data(biomass) biomass$one_value <- 1 biomass_tr <- biomass[biomass$dataset == "Training",] biomass_te <- biomass[biomass$dataset == "Testing",] rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur + one_value, data = biomass_tr) zv_filter <- rec %>% step_zv(all_predictors()) filter_obj <- prep(zv_filter, training = biomass_tr) filtered_te <- bake(filter_obj, biomass_te) any(names(filtered_te) == "one_value") #>  FALSE tidy(zv_filter, number = 1) #> # A tibble: 1 × 2 #> terms id #> <chr> <chr> #> 1 all_predictors() zv_mQoHJ tidy(filter_obj, number = 1) #> # A tibble: 1 × 2 #> terms id #> <chr> <chr> #> 1 one_value zv_mQoHJ