recipes (version 0.1.0)

step_date: Date Feature Generator

Description

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

Usage

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

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.

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.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

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

# NOT RUN {
library(lubridate)

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())

date_rec <- prep(date_rec, training = examples)
date_values <- bake(date_rec, newdata = examples)
date_values
# }