Data-Overview

devtools::load_all()
# library(lossrx)

data("claims_transactional")
data("losses")
data("exposures")

latest_eval <- losses |> dplyr::filter(eval_date == max(.data$eval_date))
wc_dat <- latest_eval |> dplyr::filter(coverage == "WC")
al_dat <- latest_eval |> dplyr::filter(coverage == "AL")

lossrx Datasets

lossrx comes with some built in data for example usage, including:

• a simulated transactional claims data.frame
• a suite of example WC and AL lossruns combined into a single data.frame
• sample exposure data for WC ($ payroll) and AL (vehicles or miles driven)

Loss Data

plot_distr(
  ~ total_incurred | coverage,
  latest_eval,
  mod.method = "split"
)

Top 10 Rows:

head(losses) |>
  kable(format = "html", digits = 2) |>
  kable_styling()

eval_date	devt_age	occurrence_number	coverage	member	program_year	loss_date	rept_date	hire_date	report_lag	report_lag_group	day_of_week	claim_type	claimant_state	loss_state	cause	department	tenure	tenure_group	claimant_age	claimant_age_group	driver_age	driver_age_group	status	total_paid	total_incurred	count	open_count	close_count	incurred_group
2019-12-31	108	1	WC	Member 2	2011	2011-01-03	2011-01-04	2000-09-11	1	1 to 3 Days	Monday	WCIN	CA	CA	Strain / Overexertion	Drivers	19.30	10+ Years	51	30+ Years Old	NA	(Missing)	C	4149.66	4149.66	1	0	1	$0-$50K
2019-08-31	104	1	WC	Member 2	2011	2011-01-03	2011-01-04	2000-09-11	1	1 to 3 Days	Monday	WCIN	CA	CA	Strain / Overexertion	Drivers	18.97	10+ Years	51	30+ Years Old	NA	(Missing)	C	4149.66	4149.66	1	0	1	$0-$50K
2019-04-30	100	1	WC	Member 2	2011	2011-01-03	2011-01-04	2000-09-11	1	1 to 3 Days	Monday	WCIN	CA	CA	Strain / Overexertion	Drivers	18.63	10+ Years	51	30+ Years Old	NA	(Missing)	C	4149.66	4149.66	1	0	1	$0-$50K
2018-12-31	96	1	WC	Member 2	2011	2011-01-03	2011-01-04	2000-09-11	1	1 to 3 Days	Monday	WCIN	CA	CA	Strain / Overexertion	Drivers	18.30	10+ Years	51	30+ Years Old	NA	(Missing)	C	4149.66	4149.66	1	0	1	$0-$50K
2018-08-31	92	1	WC	Member 2	2011	2011-01-03	2011-01-04	2000-09-11	1	1 to 3 Days	Monday	WCIN	CA	CA	Strain / Overexertion	Drivers	17.97	10+ Years	51	30+ Years Old	NA	(Missing)	C	4149.66	4149.66	1	0	1	$0-$50K
2018-04-30	88	1	WC	Member 2	2011	2011-01-03	2011-01-04	2000-09-11	1	1 to 3 Days	Monday	WCIN	CA	CA	Strain / Overexertion	Drivers	17.63	10+ Years	51	30+ Years Old	NA	(Missing)	C	4149.66	4149.66	1	0	1	$0-$50K

Summary:

print(dfSummary(losses, 
                varnumbers   = FALSE, 
                valid.col    = FALSE, 
                graph.magnif = 0.76),
      method = 'render')

Data Frame Summary

losses

Dimensions: 79748 x 30
Duplicates: 0

Variable	Stats / Values	Freqs (% of Valid)	Graph	Missing
eval_date [Date]	min : 2011-04-30 med : 2017-08-31 max : 2019-12-31 range : 8y 8m 1d	27 distinct values		0 (0.0%)
devt_age [numeric]	Mean (sd) : 40.9 (25.1) min ≤ med ≤ max: 4 ≤ 36 ≤ 108 IQR (CV) : 40 (0.6)	27 distinct values		0 (0.0%)
occurrence_number [character]	1. 1 2. 10 3. 100 4. 101 5. 102 6. 103 7. 104 8. 105 9. 106 10. 107 [ 6095 others ]	27 ( 0.0% ) 27 ( 0.0% ) 27 ( 0.0% ) 27 ( 0.0% ) 27 ( 0.0% ) 27 ( 0.0% ) 27 ( 0.0% ) 27 ( 0.0% ) 27 ( 0.0% ) 27 ( 0.0% ) 79478 ( 99.7% )		0 (0.0%)
coverage [character]	1. AL 2. WC	31449 ( 39.4% ) 48299 ( 60.6% )		0 (0.0%)
member [character]	1. Member 9 2. Member 10 3. Member 4 4. Member 17 5. Member 6 6. Member 1 7. Member 2 8. Member 12 9. Member 18 10. Member 13 [ 8 others ]	10878 ( 13.6% ) 10547 ( 13.2% ) 9347 ( 11.7% ) 8001 ( 10.0% ) 7165 ( 9.0% ) 5592 ( 7.0% ) 4508 ( 5.7% ) 3978 ( 5.0% ) 3707 ( 4.6% ) 3521 ( 4.4% ) 12504 ( 15.7% )		0 (0.0%)
program_year [character]	1. 2011 2. 2012 3. 2013 4. 2014 5. 2015 6. 2016 7. 2017 8. 2018 9. 2019	13978 ( 17.5% ) 12249 ( 15.4% ) 11750 ( 14.7% ) 11281 ( 14.1% ) 11018 ( 13.8% ) 9068 ( 11.4% ) 5541 ( 6.9% ) 3380 ( 4.2% ) 1483 ( 1.9% )		0 (0.0%)
loss_date [Date]	min : 2011-01-02 med : 2014-02-28 max : 2019-12-31 range : 8y 11m 29d	2430 distinct values		0 (0.0%)
rept_date [Date]	min : 2011-01-04 med : 2014-03-11 max : 2019-12-31 range : 8y 11m 27d	2240 distinct values		0 (0.0%)
hire_date [Date]	min : 1973-09-01 med : 2011-01-31 max : 2019-11-15 range : 46y 2m 14d	1727 distinct values		33290 (41.7%)
report_lag [numeric]	Mean (sd) : 8.6 (40.2) min ≤ med ≤ max: 0 ≤ 1 ≤ 1620 IQR (CV) : 3 (4.7)	166 distinct values		0 (0.0%)
report_lag_group [factor]	1. 0 Days 2. 1 to 3 Days 3. 3+ Days	18386 ( 23.1% ) 37273 ( 46.7% ) 24089 ( 30.2% )		0 (0.0%)
day_of_week [character]	1. Friday 2. Monday 3. Saturday 4. Sunday 5. Thursday 6. Tuesday 7. Wednesday	13428 ( 16.8% ) 16559 ( 20.8% ) 2604 ( 3.3% ) 2745 ( 3.4% ) 14644 ( 18.4% ) 16062 ( 20.1% ) 13706 ( 17.2% )		0 (0.0%)
claim_type [character]	1. WCMO 2. ALPD 3. WCIN 4. AUPD 5. ALBI 6. AUBI 7. WCIO 8. AUIO 9. AUNA 10. WCNA [ 9 others ]	27910 ( 35.0% ) 24365 ( 30.6% ) 20122 ( 25.2% ) 5172 ( 6.5% ) 1354 ( 1.7% ) 300 ( 0.4% ) 235 ( 0.3% ) 112 ( 0.1% ) 55 ( 0.1% ) 32 ( 0.0% ) 91 ( 0.1% )		0 (0.0%)
claimant_state [character]	1. NY 2. IA 3. TX 4. CT 5. AL 6. CA 7. MD 8. HI 9. MO 10. NM [ 34 others ]	13361 ( 16.8% ) 10318 ( 13.0% ) 9429 ( 11.8% ) 9374 ( 11.8% ) 7874 ( 9.9% ) 6513 ( 8.2% ) 6053 ( 7.6% ) 4695 ( 5.9% ) 3753 ( 4.7% ) 2163 ( 2.7% ) 6040 ( 7.6% )		175 (0.2%)
loss_state [character]	1. NY 2. TX 3. CT 4. IA 5. AL 6. CA 7. MD 8. HI 9. MO 10. NM [ 32 others ]	12310 ( 15.4% ) 9359 ( 11.7% ) 9059 ( 11.4% ) 8557 ( 10.7% ) 7067 ( 8.9% ) 6473 ( 8.1% ) 5663 ( 7.1% ) 4703 ( 5.9% ) 4118 ( 5.2% ) 2213 ( 2.8% ) 10201 ( 12.8% )		25 (0.0%)
cause [character]	1. Burns 2. Caught In / Under / Betwe 3. Collisions - Multi-Vehicl 4. Cuts / Punctures 5. Miscellaneous 6. Motor Vehicle Accident 7. Repetitive Motion 8. Single Vehicle Accident 9. Slip / Trip / Fall 10. Strain / Overexertion 11. Struck By / Against	262 ( 0.3% ) 2201 ( 2.8% ) 14613 ( 18.3% ) 1955 ( 2.5% ) 3002 ( 3.8% ) 1066 ( 1.3% ) 731 ( 0.9% ) 16792 ( 21.1% ) 10980 ( 13.8% ) 19498 ( 24.4% ) 8648 ( 10.8% )		0 (0.0%)
department [character]	1. Drivers 2. Food Prep/Mfg 3. Inside Sales / Administra 4. Outside Sales 5. Warehouse	50634 ( 63.5% ) 2630 ( 3.3% ) 4744 ( 6.0% ) 2048 ( 2.6% ) 19647 ( 24.7% )		45 (0.1%)
tenure [numeric]	Mean (sd) : 7.4 (6.2) min ≤ med ≤ max: 0 ≤ 5.8 ≤ 46.3 IQR (CV) : 6.6 (0.8)	11551 distinct values		33290 (41.7%)
tenure_group [factor]	1. Less than 1 Year 2. 1 to 3 Years 3. 3 to 5 Years 4. 5 to 10 Years 5. 10+ Years 6. (Missing)	2092 ( 2.6% ) 8620 ( 10.8% ) 9143 ( 11.5% ) 15406 ( 19.3% ) 11197 ( 14.0% ) 33290 ( 41.7% )		0 (0.0%)
claimant_age [numeric]	Mean (sd) : 24.8 (20.2) min ≤ med ≤ max: 0 ≤ 27 ≤ 117 IQR (CV) : 40 (0.8)	73 distinct values		1565 (2.0%)
claimant_age_group [factor]	1. Less than 18 Years Old 2. 18 to 21 Years Old 3. 22 to 30 Years Old 4. 30+ Years Old 5. (Missing)	26584 ( 33.3% ) 2561 ( 3.2% ) 14784 ( 18.5% ) 34254 ( 43.0% ) 1565 ( 2.0% )		0 (0.0%)
driver_age [numeric]	Mean (sd) : 38.6 (10.9) min ≤ med ≤ max: 0 ≤ 38 ≤ 95 IQR (CV) : 16 (0.3)	64 distinct values		63489 (79.6%)
driver_age_group [factor]	1. Less than 18 Years Old 2. 18 to 21 Years Old 3. 22 to 30 Years Old 4. 30+ Years Old 5. (Missing)	125 ( 0.2% ) 118 ( 0.1% ) 4060 ( 5.1% ) 11956 ( 15.0% ) 63489 ( 79.6% )		0 (0.0%)
status [character]	1. C 2. I 3. O 4. R	71606 ( 89.8% ) 335 ( 0.4% ) 7288 ( 9.1% ) 519 ( 0.7% )		0 (0.0%)
total_paid [numeric]	Mean (sd) : 6955.5 (34099) min ≤ med ≤ max: 0 ≤ 897 ≤ 1161358 IQR (CV) : 3154 (4.9)	10119 distinct values		0 (0.0%)
total_incurred [numeric]	Mean (sd) : 8873.6 (42103.2) min ≤ med ≤ max: 0 ≤ 1077.5 ≤ 1166036 IQR (CV) : 3526 (4.7)	9606 distinct values		0 (0.0%)
count [numeric]	Min : 0 Mean : 1 Max : 1	0 : 335 ( 0.4% ) 1 : 79413 ( 99.6% )		0 (0.0%)
open_count [numeric]	Min : 0 Mean : 0.1 Max : 1	0 : 71941 ( 90.2% ) 1 : 7807 ( 9.8% )		0 (0.0%)
close_count [numeric]	Min : 0 Mean : 0.9 Max : 1	0 : 8142 ( 10.2% ) 1 : 71606 ( 89.8% )		0 (0.0%)
incurred_group [factor]	1. $0-$50K 2. $50K-$100K 3. $100K-$250K 4. $250K-$500K 5. $500K+	76789 ( 96.3% ) 1557 ( 2.0% ) 1021 ( 1.3% ) 281 ( 0.4% ) 100 ( 0.1% )		0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.1)
2024-10-17

Worker’s Compensation

Distribution of Claims

library(fplot)
fplot::plot_distr(wc_dat$total_incurred)

plot_distr(~ total_incurred | cause, wc_dat, cumul = TRUE)

plot_lines(
  total_incurred ~ program_year,
  losses
)