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.
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_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#> # A tibble: 8 x 4 #> variable type role source #> <chr> <chr> <chr> <chr> #> 1 sample nominal id variable original #> 2 dataset nominal splitting indicator original #> 3 carbon numeric predictor original #> 4 hydrogen numeric predictor original #> 5 oxygen numeric predictor original #> 6 nitrogen numeric predictor original #> 7 sulfur numeric predictor original #> 8 HHV numeric outcome original# Centering on all predictors except carbon rec %>% step_center(all_predictors(), -carbon) %>% prep(training = biomass) %>% juice()#> # A tibble: 536 x 8 #> sample dataset carbon hydrogen oxygen nitrogen sulfur HHV #> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Akhrot Shell Training 49.8 0.181 4.37 -0.667 -0.234 20.0 #> 2 Alabama Oak Wood Waste Training 49.5 0.241 2.73 -0.877 -0.234 19.2 #> 3 Alder Training 47.8 0.341 7.68 -0.967 -0.214 18.3 #> 4 Alfalfa Training 45.1 -0.489 -2.97 2.22 -0.0736 18.2 #> 5 Alfalfa Seed Straw Training 46.8 -0.0586 2.15 -0.0772 -0.214 18.4 #> 6 Alfalfa Stalks Training 45.4 0.291 1.63 0.963 -0.134 18.5 #> 7 Alfalfa Stems Training 47.2 0.531 -0.383 1.60 -0.0336 18.7 #> 8 Alfalfa Straw Training 45.7 0.241 1.13 0.623 -0.0336 18.3 #> 9 Almond Training 48.8 0.0414 2.33 -0.277 -0.234 18.6 #> 10 Almond Hull Training 47.1 0.441 1.43 0.123 -0.134 18.9 #> # … with 526 more rows