Indicate Recurrent Date Time Event

step_time_event() creates a specification of a recipe step that will create new columns indicating if the date fall on recurrent event.

Usage

step_time_event(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rules = list(),
  columns = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("time_event")
)

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.

rules

Named list of almanac rules.

columns

A character string of variables that will be used as inputs. This field is a placeholder and will be populated once recipes::prep.recipe() is used.

keep_original_cols

A logical to keep the original variables in the output. Defaults to TRUE.

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.

Value

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

Details

Unlike some other steps step_time_event does not remove the original date variables by default. Set keep_original_cols to FALSE to remove them.

Examples

library(recipes)
library(extrasteps)
library(almanac)
library(modeldata)

data(Chicago)

on_easter <- yearly() %>% recur_on_easter()
on_weekend <- weekly() %>% recur_on_weekends()

rules <- list(easter = on_easter, weekend = on_weekend)

rec_spec <- recipe(ridership ~ date, data = Chicago) %>%
  step_time_event(date, rules = rules)

rec_spec_preped <- prep(rec_spec)

bake(rec_spec_preped, new_data = NULL)
#> # A tibble: 5,698 × 3
#>    ridership date_easter date_weekend
#>        <dbl>       <int>        <int>
#>  1     15.7            0            0
#>  2     15.8            0            0
#>  3     15.9            0            0
#>  4     15.9            0            0
#>  5     15.4            0            0
#>  6      2.42           0            1
#>  7      1.47           0            1
#>  8     15.5            0            0
#>  9     15.9            0            0
#> 10     15.9            0            0
#> # ℹ 5,688 more rows