Create exposure records in a recipes step

step_expose() creates a specification of a recipe step that will convert a data frame of census-level records to exposure-level records.

Usage

step_expose(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  end_date,
  start_date = as.Date("1900-01-01"),
  target_status = NULL,
  options = list(cal_expo = FALSE, expo_length = "year"),
  drop_pol_num = TRUE,
  skip = TRUE,
  id = recipes::rand_id("expose")
)

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. See selections() for more details.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

end_date

Experience study end date

start_date

Experience study start date. Default value = 1900-01-01.

target_status

Character vector of target status values. Default value = NULL.

options

A named list of additional arguments passed to expose().

drop_pol_num

Whether the pol_num column produced by expose() should be dropped. 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 expose step added to the sequence of any existing operations. For the tidy method, a tibble with the columns exposure_type, target_status, start_date, and end_date.

Details

Policy year exposures are calculated as a default. To switch to calendar exposures or another exposure length, use pass the appropriate arguments to the options parameter.

Policy numbers are dropped as a default whenever the recipe is baked. This is done to prevent unintentional errors when the model formula includes all variables (y ~ .). If policy numbers are required for any reason (mixed effect models, identification, etc.), set drop_pol_num to FALSE.

See also

expose()

Examples

expo_rec <- recipes::recipe(status ~ ., toy_census) |>
  step_expose(end_date = "2022-12-31", target_status = "Surrender",
              options = list(expo_length = "month")) |>
  prep()

recipes::juice(expo_rec)
#> # A tibble: 416 × 7
#>    issue_date term_date status pol_mth pol_date_mth pol_date_mth_end exposure
#>    <date>     <date>    <fct>    <int> <date>       <date>              <dbl>
#>  1 2010-01-01 NA        Active       1 2010-01-01   2010-01-31              1
#>  2 2010-01-01 NA        Active       2 2010-02-01   2010-02-28              1
#>  3 2010-01-01 NA        Active       3 2010-03-01   2010-03-31              1
#>  4 2010-01-01 NA        Active       4 2010-04-01   2010-04-30              1
#>  5 2010-01-01 NA        Active       5 2010-05-01   2010-05-31              1
#>  6 2010-01-01 NA        Active       6 2010-06-01   2010-06-30              1
#>  7 2010-01-01 NA        Active       7 2010-07-01   2010-07-31              1
#>  8 2010-01-01 NA        Active       8 2010-08-01   2010-08-31              1
#>  9 2010-01-01 NA        Active       9 2010-09-01   2010-09-30              1
#> 10 2010-01-01 NA        Active      10 2010-10-01   2010-10-31              1
#> # ℹ 406 more rows