Role Selection
has_role()
, all_predictors()
, and all_outcomes()
can be used to
select variables in a formula that have certain roles.
Similarly, has_type()
, all_numeric()
, and all_nominal()
are used to
select columns based on their data type. Nominal variables include both
character and factor.
In most cases, the selectors all_numeric_predictors()
and
all_nominal_predictors()
, which select on role and type, will be the right
approach for users.
See selections for more details.
current_info()
is an internal function.
All of these functions have have limited utility outside of column selection in step functions.
has_role(match = "predictor") all_predictors() all_numeric_predictors() all_nominal_predictors() all_outcomes() has_type(match = "numeric") all_numeric() all_nominal() current_info()
match |
A single character string for the query. Exact matching is used (i.e. regular expressions won't work). |
Selector functions return an integer vector.
current_info()
returns an environment with objects vars
and data
.
library(modeldata) data(biomass) rec <- recipe(biomass) %>% update_role( carbon, hydrogen, oxygen, nitrogen, sulfur, new_role = "predictor" ) %>% update_role(HHV, new_role = "outcome") %>% update_role(sample, new_role = "id variable") %>% update_role(dataset, new_role = "splitting indicator") recipe_info <- summary(rec) recipe_info # Centering on all predictors except carbon rec %>% step_center(all_predictors(), -carbon) %>% prep(training = biomass) %>% bake(new_data = NULL)
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