Convert aggregate transaction experience studies to the trx_df class.
Usage
as_trx_df(
x,
col_trx_amt = "trx_amt",
col_trx_n = "trx_n",
col_trx_flag = "trx_flag",
col_exposure = "exposure",
col_percent_of = NULL,
col_percent_of_w_trx = NULL,
col_trx_amt_sq = "trx_amt_sq",
start_date = as.Date("1900-01-01"),
end_date = NULL,
conf_int = FALSE,
conf_level = 0.95
)
is_trx_df(x)Arguments
- x
An object. For
as_trx_df(),xmust be a data frame.- col_trx_amt
Optional. Name of the column in
xcontaining transaction amounts.- col_trx_n
Optional. Name of the column in
xcontaining transaction counts.- col_trx_flag
Optional. Name of the column in
xcontaining the number of exposure records with transactions.- col_exposure
Optional. Name of the column in
xcontaining exposures.- col_percent_of
Optional. Name of the column in
xcontaining a numeric variable to use in "percent of" calculations.- col_percent_of_w_trx
Optional. Name of the column in
xcontaining a numeric variable to use in "percent of" calculations with transactions.- col_trx_amt_sq
Optional and only required when
col_percent_ofis passed andconf_intisTRUE. Name of the column inxcontaining squared transaction amounts.- start_date
Experience study start date. Default value = 1900-01-01.
- end_date
Experience study end date
- conf_int
If
TRUE, future calls tosummary()will include confidence intervals around the observed utilization rates and anypercent_ofoutput columns.- conf_level
Confidence level for confidence intervals
Details
is_trx_df() will return TRUE if x is a trx_df object.
as_trx_df() will coerce a data frame to a trx_df object if that
data frame has the required columns for transaction studies listed below.
as_trx_df() is most useful for working with aggregate summaries of
experience that were not created by actxps where individual policy
information is not available. After converting the data to the trx_df
class, summary() can be used to summarize data by any grouping variables,
and autoplot() and autotable() are available for reporting.
At a minimum, the following columns are required:
Transaction amounts (
trx_amt)Transaction counts (
trx_n)The number of exposure records with transactions (
trx_flag). This number is not necessarily equal to transaction counts. If multiple transactions are allowed per exposure period,trx_flagwill be less thantrx_n.Exposures (
exposure)
If transaction amounts should be expressed as a percentage of another variable (i.e. to calculate utilization rates or actual-to-expected ratios), additional columns are required:
A denominator "percent of" column. For example, the sum of account values.
A denominator "percent of" column for exposure records with transactions. For example, the sum of account values across all records with non-zero transaction amounts.
If confidence intervals are desired and "percent of" columns are passed, an
additional column for the sum of squared transaction amounts (trx_amt_sq)
is also required.
The names in parentheses above are expected column names. If the data
frame passed to as_trx_df() uses different column names, these can be
specified using the col_* arguments.
start_date, and end_date are optional arguments that are
only used for printing the resulting trx_df object.
Unlike trx_stats(), as_trx_df() only permits a single transaction type and
a single percent_of column.
See also
trx_stats() for information on how trx_df objects are typically
created from individual exposure records.
Examples
# convert pre-aggregated experience into a trx_df object
dat <- as_trx_df(agg_sim_dat,
col_exposure = "n",
col_trx_amt = "wd",
col_trx_n = "wd_n",
col_trx_flag = "wd_flag",
col_percent_of = "av",
col_percent_of_w_trx = "av_w_wd",
col_trx_amt_sq = "wd_sq",
start_date = 2005, end_date = 2019,
conf_int = TRUE)
dat
#> Transaction study results
#>
#> Study range: 2005 to 2019
#> Transaction types: wd
#> Transactions as % of: av
#> # A tibble: 180 × 17
#> pol_yr inc_guar qual product exposure_n claims_n av exposure_amt
#> <int> <lgl> <lgl> <fct> <dbl> <int> <dbl> <dbl>
#> 1 1 FALSE FALSE a 880. 3 1204699 1168118.
#> 2 1 FALSE FALSE b 885. 9 1209902 1154044.
#> 3 1 FALSE FALSE c 1685. 12 2265030 2170944.
#> 4 1 FALSE TRUE a 1073. 11 1433862 1375173.
#> 5 1 FALSE TRUE b 1086. 8 1449080 1396136.
#> 6 1 FALSE TRUE c 2111. 13 2907067 2794199.
#> 7 1 TRUE FALSE a 1299. 9 1796934 1744069.
#> 8 1 TRUE FALSE b 1230. 2 1732269 1662742.
#> 9 1 TRUE FALSE c 2702. 11 3700302 3590893.
#> 10 1 TRUE TRUE a 1601. 5 2234728 2147418.
#> # ℹ 170 more rows
#> # ℹ 9 more variables: claims_amt <dbl>, av_sq <dbl>, exposure <int>,
#> # trx_amt <dbl>, trx_n <dbl>, trx_flag <int>, trx_amt_sq <dbl>,
#> # av_w_trx <dbl>, trx_type <chr>
is_trx_df(dat)
#> [1] TRUE
# summary by policy year
summary(dat, pol_yr)
#> Transaction study results
#>
#> Groups: pol_yr
#> Study range: 2005 to 2019
#> Transaction types: wd
#> Transactions as % of: av
#> # A tibble: 15 × 21
#> pol_yr trx_type trx_n trx_flag trx_amt exposure avg_trx avg_all trx_freq
#> <int> <chr> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 1 wd 16942 7921 419763 19995 53.0 21.0 2.14
#> 2 2 wd 16900 7923 440919 18434 55.7 23.9 2.13
#> 3 3 wd 16679 7796 449349 16806 57.6 26.7 2.14
#> 4 4 wd 16193 7575 440510 15266 58.2 28.9 2.14
#> 5 5 wd 15353 7153 420462 13618 58.8 30.9 2.15
#> 6 6 wd 14382 6708 411073 12067 61.3 34.1 2.14
#> 7 7 wd 13248 6183 396732 10541 64.2 37.6 2.14
#> 8 8 wd 12034 5603 369098 9130 65.9 40.4 2.15
#> 9 9 wd 10461 4868 330507 7591 67.9 43.5 2.15
#> 10 10 wd 8978 4155 290363 6185 69.9 46.9 2.16
#> 11 11 wd 7470 3448 243606 4897 70.7 49.7 2.17
#> 12 12 wd 5079 2314 176383 3093 76.2 57.0 2.19
#> 13 13 wd 3310 1495 119153 1937 79.7 61.5 2.21
#> 14 14 wd 2119 946 85073 1182 89.9 72.0 2.24
#> 15 15 wd 982 430 40328 510 93.8 79.1 2.28
#> # ℹ 12 more variables: trx_util <dbl>, av_w_trx <dbl>, av <dbl>,
#> # pct_of_av_w_trx <dbl>, pct_of_av_all <dbl>, trx_util_lower <dbl>,
#> # trx_util_upper <dbl>, pct_of_av_w_trx_lower <dbl>,
#> # pct_of_av_w_trx_upper <dbl>, pct_of_av_all_lower <dbl>,
#> # pct_of_av_all_upper <dbl>, trx_amt_sq <dbl>