step_date_hcai: Date and Time Feature Generator

Description Usage Arguments Details Value Examples

View source: R/step_date_hcai.R

Description

'step_date_hcai' creates a *specification* of a recipe step that will convert date data into factor or numeric variable(s). This step will guess the date format of columns with the "_DTS" suffix, and then create either 'categories' or 'continuous' columns. Various portions of this step are copied from 'recipes::step_date'.

Usage

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step_date_hcai(recipe, ..., role = "predictor", trained = FALSE,
  feature_type = "continuous", columns = NULL, skip = FALSE)

## S3 method for class 'step_date_hcai'
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' or their name must end with 'DTS'. 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 number of NA values have been counted in preprocessing.

feature_type

character, either 'continuous' (default) or 'categories'.

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?

x

A 'step_date_hcai' object.

Details

Unlike other steps, 'step_date_hcai' does *not* remove the original date variables. [step_rm()] can be used for this purpose.

Value

For 'step_date_hcai', 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).

Examples

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library(lubridate)
library(recipes)

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_hcai(all_predictors())

date_rec <- prep(date_rec, training = examples)

date_values <- bake(date_rec, newdata = examples)
date_values

# changing `feature_type` to `categories`
date_rec <-
  recipe(~ Dan + Stefan, examples) %>%
  step_date_hcai(all_predictors(), feature_type = "categories")

date_rec <- prep(date_rec, training = examples)

date_values <- bake(date_rec, newdata = examples)
date_values

healthcareai documentation built on Sept. 2, 2018, 1:03 a.m.