step_rm() creates a specification of a recipe step that will remove
selected variables.
step_rm(
recipe,
...,
role = NA,
trained = FALSE,
removals = NULL,
skip = FALSE,
id = rand_id("rm")
)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 character string that contains the names of
columns that should be removed. These values are not determined
until prep() is called.
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.
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.
When you tidy() this step, a tibble is returned with
columns terms and id:
character, the selectors or variables selected
character, id of this step
The underlying operation does not allow for case weights.
Other variable filter steps:
step_corr(),
step_filter_missing(),
step_lincomb(),
step_nzv(),
step_select(),
step_zv()
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
#> # A tibble: 80 × 3
#> carbon sulfur HHV
#> <dbl> <dbl> <dbl>
#> 1 46.4 0.22 18.3
#> 2 43.2 0.34 17.6
#> 3 42.7 0.3 17.2
#> 4 46.4 0.5 18.9
#> 5 48.8 0 20.5
#> 6 44.3 0.2 18.5
#> 7 38.9 0.51 15.1
#> 8 42.1 0.2 16.2
#> 9 29.2 4.9 11.1
#> 10 27.8 1.05 10.8
#> # ℹ 70 more rows
tidy(smaller_set, number = 1)
#> # A tibble: 3 × 2
#> terms id
#> <chr> <chr>
#> 1 hydrogen rm_SZcjw
#> 2 oxygen rm_SZcjw
#> 3 nitrogen rm_SZcjw