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# actxps <a href="https://mattheaphy.github.io/actxps/"><img src="man/figures/logo.png" align="right" height="138" /></a>

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The actxps package provides a set of tools to assist with the creation
of actuarial experience studies. Experience studies are used by
actuaries to explore historical experience across blocks of business and
to inform assumption setting for projection models.

- The `expose()` family of functions convert census-level records into
  policy or calendar year exposure records.
- The `exp_stats()` function creates experience summary data frames
  containing observed termination rates and claims. Optionally, expected
  termination rates, actual-to-expected ratios, and limited fluctuation
  credibility estimates can also be returned.
- The `add_transactions()` function attaches summarized transactions to
  a data frame with exposure-level records.
- The `trx_stats()` function creates transaction summary data frames
  containing transaction counts, amounts, frequencies, and utilization.
  Optionally, transaction amounts can be expressed as a percentage of
  one or more variables to calculate rates or actual-to-expected ratios.
- The `autoplot()` and `autotable()` functions creates plots and tables
  for reporting.
- The `exp_shiny()` function launches a Shiny app that allows for
  interactive exploration of experience drivers.

## Installation

The actxps package can be installed from CRAN with:

``` r
install.packages("actxps")
```

To install the development version from [GitHub](https://github.com/)
use:

``` r
devtools::install_github("mattheaphy/actxps")
```

## Basic usage

An expanded version of this demo is available in `vignette("actxps")`.

The actxps package includes simulated census data for a theoretical
deferred annuity product with an optional guaranteed income rider. The
grain of this data is one row *per policy*.

``` r
library(actxps)
library(dplyr)

census_dat
#> # A tibble: 20,000 × 11
#>    pol_num status  issue_date inc_guar qual    age product gender wd_age premium
#>      <int> <fct>   <date>     <lgl>    <lgl> <int> <fct>   <fct>   <int>   <dbl>
#>  1       1 Active  2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  2       2 Surren… 2007-09-24 FALSE    FALSE    71 a       F          71     708
#>  3       3 Active  2012-10-06 FALSE    TRUE     62 b       F          63     466
#>  4       4 Surren… 2005-06-27 TRUE     TRUE     62 c       M          62     485
#>  5       5 Active  2019-11-22 FALSE    FALSE    62 c       F          67     978
#>  6       6 Active  2018-09-01 FALSE    TRUE     77 a       F          77    1288
#>  7       7 Active  2011-07-23 TRUE     TRUE     63 a       M          65    1046
#>  8       8 Active  2005-11-08 TRUE     TRUE     58 a       M          58    1956
#>  9       9 Active  2010-09-19 FALSE    FALSE    53 c       M          64    2165
#> 10      10 Active  2012-05-25 TRUE     FALSE    61 b       M          73     609
#> # ℹ 19,990 more rows
#> # ℹ 1 more variable: term_date <date>
```

Convert census records to exposure records with one row *per policy per
year*.

``` r
exposed_data <- expose(census_dat, end_date = "2019-12-31", 
                        target_status = "Surrender")

exposed_data
#> 
#> ── Exposure data ──
#> 
#> • Exposure type: policy_year
#> • Target status: Surrender
#> • Study range: 1900-01-01 to 2019-12-31
#> 
#> # A tibble: 141,252 × 15
#>    pol_num status issue_date inc_guar qual    age product gender wd_age premium
#>      <int> <fct>  <date>     <lgl>    <lgl> <int> <fct>   <fct>   <int>   <dbl>
#>  1       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  2       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  3       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  4       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  5       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  6       1 Active 2014-12-17 TRUE     FALSE    56 b       F          77     370
#>  7       2 Active 2007-09-24 FALSE    FALSE    71 a       F          71     708
#>  8       2 Active 2007-09-24 FALSE    FALSE    71 a       F          71     708
#>  9       2 Active 2007-09-24 FALSE    FALSE    71 a       F          71     708
#> 10       2 Active 2007-09-24 FALSE    FALSE    71 a       F          71     708
#> # ℹ 141,242 more rows
#> # ℹ 5 more variables: term_date <date>, pol_yr <int>, pol_date_yr <date>,
#> #   pol_date_yr_end <date>, exposure <dbl>
```

Create a summary grouped by policy year and the presence of a guaranteed
income rider.

``` r
exp_res <- exposed_data |> 
  group_by(pol_yr, inc_guar) |> 
  exp_stats()

exp_res
#> 
#> ── Experience study results ──
#> 
#> • Groups: pol_yr and inc_guar
#> • Target status: Surrender
#> • Study range: 1900-01-01 to 2019-12-31
#> 
#> # A tibble: 30 × 6
#>    pol_yr inc_guar n_claims claims exposure   q_obs
#>     <int> <lgl>       <int>  <int>    <dbl>   <dbl>
#>  1      1 FALSE          56     56    7720. 0.00725
#>  2      1 TRUE           46     46   11532. 0.00399
#>  3      2 FALSE          92     92    7103. 0.0130 
#>  4      2 TRUE           68     68   10612. 0.00641
#>  5      3 FALSE          67     67    6447. 0.0104 
#>  6      3 TRUE           57     57    9650. 0.00591
#>  7      4 FALSE         123    123    5799. 0.0212 
#>  8      4 TRUE           45     45    8737. 0.00515
#>  9      5 FALSE          97     97    5106. 0.0190 
#> 10      5 TRUE           67     67    7810. 0.00858
#> # ℹ 20 more rows
```

Calculate actual-to-expected ratios.

First, attach one or more columns of expected termination rates to the
exposure data. Then, pass these column names to the `expected` argument
of `exp_stats()`.

``` r
expected_table <- c(seq(0.005, 0.03, length.out = 10), 0.2, 0.15, rep(0.05, 3))

# using 2 different expected termination rates
exposed_data <- exposed_data |> 
  mutate(expected_1 = expected_table[pol_yr],
         expected_2 = ifelse(exposed_data$inc_guar, 0.015, 0.03))

exp_res <- exposed_data |> 
  group_by(pol_yr, inc_guar) |> 
  exp_stats(expected = c("expected_1", "expected_2"))

exp_res
#> 
#> ── Experience study results ──
#> 
#> • Groups: pol_yr and inc_guar
#> • Target status: Surrender
#> • Study range: 1900-01-01 to 2019-12-31
#> • Expected values: expected_1 and expected_2
#> 
#> # A tibble: 30 × 10
#>    pol_yr inc_guar n_claims claims exposure   q_obs expected_1 expected_2
#>     <int> <lgl>       <int>  <int>    <dbl>   <dbl>      <dbl>      <dbl>
#>  1      1 FALSE          56     56    7720. 0.00725    0.005        0.03 
#>  2      1 TRUE           46     46   11532. 0.00399    0.005        0.015
#>  3      2 FALSE          92     92    7103. 0.0130     0.00778      0.03 
#>  4      2 TRUE           68     68   10612. 0.00641    0.00778      0.015
#>  5      3 FALSE          67     67    6447. 0.0104     0.0106       0.03 
#>  6      3 TRUE           57     57    9650. 0.00591    0.0106       0.015
#>  7      4 FALSE         123    123    5799. 0.0212     0.0133       0.03 
#>  8      4 TRUE           45     45    8737. 0.00515    0.0133       0.015
#>  9      5 FALSE          97     97    5106. 0.0190     0.0161       0.03 
#> 10      5 TRUE           67     67    7810. 0.00858    0.0161       0.015
#> # ℹ 20 more rows
#> # ℹ 2 more variables: ae_expected_1 <dbl>, ae_expected_2 <dbl>
```

Create visualizations using the `autoplot()` and `autotable()`
functions.

``` r
autoplot(exp_res)
```

<img src="man/figures/README-plots-1.png" width="100%" />

``` r
# first 10 rows showed for brevity
exp_res |> head(10) |> autotable()
```

<center>
<img src="man/figures/exp_gt.png" width="55%" height="55%" />
</center>

Launch a Shiny app to interactively explore experience data.

``` r
exp_shiny(exposed_data)
```

<img src="man/figures/exp_shiny.png" width="100%" />

<br> **Logo**

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