recipes (version 1.0.0)

step_rm: General Variable Filter

Description

step_rm creates a specification of a recipe step that will remove variables based on their name, type, or role.

Usage

step_rm(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  removals = NULL,
  skip = FALSE,
  id = rand_id("rm")
)

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

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 variables for this step. See 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.

removals

A character string that contains the names of columns that should be removed. These values are not determined until prep() is called.

skip

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 = TRUE as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it.

Tidying

When you tidy() this step, a tibble with column terms (the columns that will be removed) is returned.

Case weights

The underlying operation does not allow for case weights.

Details

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.

See Also

Other variable filter steps: step_corr(), step_filter_missing(), step_lincomb(), step_nzv(), step_select(), step_zv()

Examples

data(biomass, package = "modeldata")

biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]

rec <- recipe(
  HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
  data = biomass_tr
)

library(dplyr)
smaller_set <- rec %>%
  step_rm(contains("gen"))

smaller_set <- prep(smaller_set, training = biomass_tr)

filtered_te <- bake(smaller_set, biomass_te)
filtered_te

tidy(smaller_set, number = 1)