Date feature generator

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,
  locale = clock::clock_locale()$labels,
  columns = NULL,
  keep_original_cols = TRUE,
  skip = FALSE,
  id = rand_id("date")
)

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 Date or POSIXct. See selections() 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.

features

A character string that includes at least one of the following values: month, dow (day of week), mday (day of month), 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.

locale

Locale to be used for month and dow, see locales. On Linux systems you can use system("locale -a") to list all the installed locales. Can be a locales string, or a clock::clock_labels() object. Defaults to clock::clock_locale()$labels.

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() 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 step added to the sequence of any existing operations.

Details

Unlike some other steps, step_date() does not remove the original date variables by default. Set keep_original_cols to FALSE to remove them.

See step_time() if you want to calculate features that are smaller than days.

Tidying

When you tidy() this step, a tibble is returned with columns terms, value, ordinal , and id:

terms

character, the selectors or variables selected

value

character, the feature names

ordinal

logical, are factors ordered

id

character, id of this step

Case weights

The underlying operation does not allow for case weights.

See also

Other dummy variable and encoding steps: step_bin2factor(), step_count(), step_dummy(), step_dummy_extract(), step_dummy_multi_choice(), step_factor2string(), step_holiday(), 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)
#> 
#> 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 × 4
#>   terms            value ordinal id        
#>   <chr>            <chr> <lgl>   <chr>     
#> 1 all_predictors() dow   FALSE   date_vUNsj
#> 2 all_predictors() month FALSE   date_vUNsj
#> 3 all_predictors() year  FALSE   date_vUNsj

date_rec <- prep(date_rec, training = examples)

date_values <- bake(date_rec, new_data = examples)
date_values
#> # A tibble: 10 × 8
#>    Dan        Stefan     Dan_dow Dan_month Dan_year Stefan_dow Stefan_month
#>    <date>     <date>     <fct>   <fct>        <int> <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         
#> # ℹ 1 more variable: Stefan_year <int>

tidy(date_rec, number = 1)
#> # A tibble: 6 × 4
#>   terms  value ordinal id        
#>   <chr>  <chr> <lgl>   <chr>     
#> 1 Dan    dow   FALSE   date_vUNsj
#> 2 Dan    month FALSE   date_vUNsj
#> 3 Dan    year  FALSE   date_vUNsj
#> 4 Stefan dow   FALSE   date_vUNsj
#> 5 Stefan month FALSE   date_vUNsj
#> 6 Stefan year  FALSE   date_vUNsj