step_holiday creates a a specification of a recipe step that will convert date data into one or more binary indicator variables for common holidays.

step_holiday(recipe, ..., role = "predictor", trained = FALSE,
  holidays = c("LaborDay", "NewYearsDay", "ChristmasDay"),
  columns = NULL, skip = FALSE, id = rand_id("holiday"))

# S3 method for step_holiday
tidy(x, ...)

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 which variables will be used to create the new variables. The selected variables should have class Date or POSIXct. See selections() for more details. For the tidy method, these are not currently used.

role

For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new variable columns created by the original variables will be used as predictors in a model.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

holidays

A character string that includes at least one holiday supported by the timeDate package. See timeDate::listHolidays() for a complete list.

columns

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

skip

A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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.

x

A step_holiday object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms which is the columns that will be affected and holiday.

Details

Unlike other steps, step_holiday does not remove the original date variables. step_rm() can be used for this purpose.

See also

Examples

library(lubridate) examples <- data.frame(someday = ymd("2000-12-20") + days(0:40)) holiday_rec <- recipe(~ someday, examples) %>% step_holiday(all_predictors()) holiday_rec <- prep(holiday_rec, training = examples) holiday_values <- bake(holiday_rec, new_data = examples) holiday_values
#> # A tibble: 41 x 4 #> someday someday_LaborDay someday_NewYearsDay someday_ChristmasDay #> <date> <dbl> <dbl> <dbl> #> 1 2000-12-20 0 0 0 #> 2 2000-12-21 0 0 0 #> 3 2000-12-22 0 0 0 #> 4 2000-12-23 0 0 0 #> 5 2000-12-24 0 0 0 #> 6 2000-12-25 0 0 1 #> 7 2000-12-26 0 0 0 #> 8 2000-12-27 0 0 0 #> 9 2000-12-28 0 0 0 #> 10 2000-12-29 0 0 0 #> # … with 31 more rows