step_date creates a specification of a recipe step that will convert date data into one or more factor or numeric variables.

step_date(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  features = c("dow", "month", "year"),
  abbr = TRUE,
  label = TRUE,
  ordinal = FALSE,
  columns = NULL,
  skip = FALSE,
  id = rand_id("date")
)

# S3 method for step_date
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 that 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.

features

A character string that includes at least one of the following values: month, dow (day of week), doy (day of year), week, month, decimal (decimal date, e.g. 2002.197), quarter, semester, year.

abbr

A logical. Only available for features month or dow. FALSE will display the day of the week as an ordered factor of character strings, such as "Sunday". TRUE will display an abbreviated version of the label, such as "Sun". abbr is disregarded if label = FALSE.

label

A logical. Only available for features month or dow. TRUE will display the day of the week as an ordered factor of character strings, such as "Sunday." FALSE will display the day of the week as a number.

ordinal

A logical: should factors be ordered? Only available for features month or dow.

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_date object.

Value

For step_date, 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 (the selectors or variables selected), value (the feature names), and ordinal (a logical).

Details

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

See also

Examples

#> #> Attaching package: ‘lubridate’
#> The following objects are masked from ‘package:base’: #> #> date, intersect, setdiff, union
examples <- data.frame(Dan = ymd("2002-03-04") + days(1:10), Stefan = ymd("2006-01-13") + days(1:10)) date_rec <- recipe(~ Dan + Stefan, examples) %>% step_date(all_predictors()) tidy(date_rec, number = 1)
#> # A tibble: 3 x 4 #> terms value ordinal id #> <chr> <chr> <lgl> <chr> #> 1 all_predictors() dow FALSE date_4kbnH #> 2 all_predictors() month FALSE date_4kbnH #> 3 all_predictors() year FALSE date_4kbnH
date_rec <- prep(date_rec, training = examples) date_values <- bake(date_rec, new_data = examples) date_values
#> # A tibble: 10 x 8 #> Dan Stefan Dan_dow Dan_month Dan_year Stefan_dow Stefan_month #> <date> <date> <fct> <fct> <dbl> <fct> <fct> #> 1 2002-03-05 2006-01-14 Tue Mar 2002 Sat Jan #> 2 2002-03-06 2006-01-15 Wed Mar 2002 Sun Jan #> 3 2002-03-07 2006-01-16 Thu Mar 2002 Mon Jan #> 4 2002-03-08 2006-01-17 Fri Mar 2002 Tue Jan #> 5 2002-03-09 2006-01-18 Sat Mar 2002 Wed Jan #> 6 2002-03-10 2006-01-19 Sun Mar 2002 Thu Jan #> 7 2002-03-11 2006-01-20 Mon Mar 2002 Fri Jan #> 8 2002-03-12 2006-01-21 Tue Mar 2002 Sat Jan #> 9 2002-03-13 2006-01-22 Wed Mar 2002 Sun Jan #> 10 2002-03-14 2006-01-23 Thu Mar 2002 Mon Jan #> # … with 1 more variable: Stefan_year <dbl>
tidy(date_rec, number = 1)
#> # A tibble: 6 x 4 #> terms value ordinal id #> <chr> <chr> <lgl> <chr> #> 1 Dan dow FALSE date_4kbnH #> 2 Dan month FALSE date_4kbnH #> 3 Dan year FALSE date_4kbnH #> 4 Stefan dow FALSE date_4kbnH #> 5 Stefan month FALSE date_4kbnH #> 6 Stefan year FALSE date_4kbnH