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Split calendar exposures by policy year

Source: R/expose_split.R
expose_split.Rd

Split calendar period exposures that cross a policy anniversary into a pre-anniversary record and a post-anniversary record.

After splitting the data, the resulting data frame will contain both calendar exposures and policy year exposures. These columns will be named exposure_cal and exposure_pol, respectively. Calendar exposures will be in the original units passed to expose_split(). Policy exposures will always be expressed in years.

After splitting exposures, downstream functions like exp_stats() and exp_shiny() will require clarification as to which exposure basis should be used to summarize results.

is_split_exposed_df() will return TRUE if x is a split_exposed_df object.

Usage

expose_split(.data)

is_split_exposed_df(x)

Arguments

.data

An exposed_df object with calendar period exposures.

x

Any object

Value

For expose_split(), a tibble with class split_exposed_df, exposed_df, tbl_df, tbl, and data.frame. The results include all columns in .data except that exposure has been renamed to exposure_cal. Additional columns include:

  • exposure_pol - policy year exposures

  • pol_yr - policy year

For is_split_exposed_df(), a length-1 logical vector.

Details

.data must be an exposed_df with calendar year, quarter, month, or week exposure records. Calendar year exposures are created by the functions expose_cy(), expose_cq(), expose_cm(), or expose_cw(), (or expose() when cal_expo = TRUE).

See also

expose() for information on creating exposure records from census data.

Examples

toy_census |> expose_cy("2022-12-31") |> expose_split()
#> 
#> ── Exposure data ──
#> 
#> • Exposure type: split_year
#> • Target status:
#> • Study range: 1900-01-01 to 2022-12-31
#> 
#> # A tibble: 58 × 9
#>    pol_num status issue_date term_date cal_yr     cal_yr_end pol_yr
#>      <int> <fct>  <date>     <date>    <date>     <date>      <int>
#>  1       1 Active 2010-01-01 NA        2010-01-01 2010-12-31      1
#>  2       1 Active 2010-01-01 NA        2011-01-01 2011-12-31      2
#>  3       1 Active 2010-01-01 NA        2012-01-01 2012-12-31      3
#>  4       1 Active 2010-01-01 NA        2013-01-01 2013-12-31      4
#>  5       1 Active 2010-01-01 NA        2014-01-01 2014-12-31      5
#>  6       1 Active 2010-01-01 NA        2015-01-01 2015-12-31      6
#>  7       1 Active 2010-01-01 NA        2016-01-01 2016-12-31      7
#>  8       1 Active 2010-01-01 NA        2017-01-01 2017-12-31      8
#>  9       1 Active 2010-01-01 NA        2018-01-01 2018-12-31      9
#> 10       1 Active 2010-01-01 NA        2019-01-01 2019-12-31     10
#> # ℹ 48 more rows
#> # ℹ 2 more variables: exposure_cal <dbl>, exposure_pol <dbl>