has_role(), all_predictors(), and all_outcomes() can be used to
select variables in a formula that have certain roles.
In most cases, the right approach for users will be use to use the
predictor-specific selectors such as all_numeric_predictors() and
all_nominal_predictors(). In general you should be careful about using
-all_outcomes() if a *_predictors() selector would do what you want.
Similarly, has_type(), all_numeric(), all_integer(), all_double(),
all_nominal(), all_ordered(), all_unordered(), all_factor(),
all_string(), all_date() and all_datetime() are used to select columns
based on their data type.
all_factor() captures ordered and unordered factors, all_string()
captures characters, all_unordered() captures unordered factors and
characters, all_ordered() captures ordered factors, all_nominal()
captures characters, unordered and ordered factors.
all_integer() captures integers, all_double() captures doubles,
all_numeric() captures all kinds of numeric.
all_date() captures Date() variables, all_datetime() captures
POSIXct() variables.
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.
Usage
has_role(match = "predictor")
has_type(match = "numeric")
all_outcomes()
all_predictors()
all_date()
all_date_predictors()
all_datetime()
all_datetime_predictors()
all_double()
all_double_predictors()
all_factor()
all_factor_predictors()
all_integer()
all_integer_predictors()
all_logical()
all_logical_predictors()
all_nominal()
all_nominal_predictors()
all_numeric()
all_numeric_predictors()
all_ordered()
all_ordered_predictors()
all_string()
all_string_predictors()
all_unordered()
all_unordered_predictors()
current_info()Arguments
- match
A single character string for the query. Exact matching is used (i.e. regular expressions won't work).
Value
Selector functions return an integer vector.
current_info() returns an environment with objects vars and data.
Examples
data(biomass, package = "modeldata")
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 × 4
#> variable type role source
#> <chr> <list> <chr> <chr>
#> 1 sample <chr [3]> id variable original
#> 2 dataset <chr [3]> splitting indicator original
#> 3 carbon <chr [2]> predictor original
#> 4 hydrogen <chr [2]> predictor original
#> 5 oxygen <chr [2]> predictor original
#> 6 nitrogen <chr [2]> predictor original
#> 7 sulfur <chr [2]> predictor original
#> 8 HHV <chr [2]> outcome original
# Centering on all predictors except carbon
rec %>%
step_center(all_predictors(), -carbon) %>%
prep(training = biomass) %>%
bake(new_data = NULL)
#> # A tibble: 536 × 8
#> sample dataset carbon hydro…¹ oxygen nitro…² sulfur HHV
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Akhrot Shell Traini… 49.8 0.181 4.37 -0.667 -0.234 20.0
#> 2 Alabama Oak Wood W… Traini… 49.5 0.241 2.73 -0.877 -0.234 19.2
#> 3 Alder Traini… 47.8 0.341 7.68 -0.967 -0.214 18.3
#> 4 Alfalfa Traini… 45.1 -0.489 -2.97 2.22 -0.0736 18.2
#> 5 Alfalfa Seed Straw Traini… 46.8 -0.0586 2.15 -0.0772 -0.214 18.4
#> 6 Alfalfa Stalks Traini… 45.4 0.291 1.63 0.963 -0.134 18.5
#> 7 Alfalfa Stems Traini… 47.2 0.531 -0.383 1.60 -0.0336 18.7
#> 8 Alfalfa Straw Traini… 45.7 0.241 1.13 0.623 -0.0336 18.3
#> 9 Almond Traini… 48.8 0.0414 2.33 -0.277 -0.234 18.6
#> 10 Almond Hull Traini… 47.1 0.441 1.43 0.123 -0.134 18.9
#> # … with 526 more rows, and abbreviated variable names ¹hydrogen,
#> # ²nitrogen