step_holiday() creates a specification of a recipe step that will convert
date data into one or more binary indicator variables for common holidays.
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. The selected variables should have class
DateorPOSIXct. Seeselections()for more details.- role
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from 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
timeDatepackage. SeetimeDate::listHolidays()for a complete list.- columns
A character string of the selected variable names. This field is a placeholder and will be populated once
prep()is used.- sparse
A single string. Should the columns produced be sparse vectors. Can take the values
"yes","no", and"auto". Ifsparse = "auto"then workflows can determine the best option. Defaults to"auto".- 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 whenprep()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 usingskip = TRUEas 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 step added to the
sequence of any existing operations.
Details
Unlike some other steps, step_holiday does not
remove the original date variables by default. Set keep_original_cols
to FALSE to remove them.
Tidying
When you tidy() this step, a tibble is returned with
columns terms, holiday , and id:
- terms
character, the selectors or variables selected
- holiday
character, name of holidays
- id
character, id of this step
Sparse data
This step produces sparse columns if sparse = "yes" is being set. The
default value "auto" won't trigger production fo sparse columns if a recipe
is prep()ed, but allows for a workflow to toggle to "yes" or "no"
depending on whether the model supports sparse_data and if the model is
is expected to run faster with the data.
The mechanism for determining how much sparsity is produced isn't perfect,
and there will be times when you want to manually overwrite by setting
sparse = "yes" or sparse = "no".
See also
Other dummy variable and encoding steps:
step_bin2factor(),
step_count(),
step_date(),
step_dummy(),
step_dummy_extract(),
step_dummy_multi_choice(),
step_factor2string(),
step_indicate_na(),
step_integer(),
step_novel(),
step_num2factor(),
step_ordinalscore(),
step_other(),
step_regex(),
step_relevel(),
step_string2factor(),
step_time(),
step_unknown(),
step_unorder()
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 × 4
#> someday someday_LaborDay someday_NewYearsDay someday_ChristmasDay
#> <date> <int> <int> <int>
#> 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
#> # ℹ 31 more rows