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,
  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

step_holiday() step_rm() recipe() prep.recipe() bake.recipe()

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 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