Package 'lossrx'

claims_transactional

Description

Transactional claims dataset. Convert to a static lossrun using the loss_run() function.

Usage

claims_transactional

Format

A data.frame with 80278 rows and 12 variables:

claim_num

integer. DESCRIPTION.

claim_id

character. DESCRIPTION.

accident_date

double. DESCRIPTION.

state

character. DESCRIPTION.

claimant

character. DESCRIPTION.

report_date

double. DESCRIPTION.

status

character. DESCRIPTION.

payment

double. DESCRIPTION.

case

double. DESCRIPTION.

transaction_date

double. DESCRIPTION.

trans_num

integer. DESCRIPTION.

paid

double. DESCRIPTION.

---

Coalesce Join

Description

Coalesce Join

Usage

coalesce_join(
  x,
  y,
  by = NULL,
  suffix = c(".x", ".y"),
  join = dplyr::full_join,
  ...
)

Arguments

x	x
y	y
by	by
suffix	suffix
join	join type
...	passed to dplyr join function

Value

a tibble

---

Connect DB

Description

Connect to the Actuarial Database Instance.

Usage

connect_db(pool = TRUE)

Arguments

pool	Logical - should the connection be pooled?

Value

a database connection

---

Create Table

Description

Creates a database table given a connection, table name, and:

• SQL file specifying the table's schema

• CSV file to seed the table's values

Usage

create_tbl(
  conn,
  tbl_name,
  csv_path = "data-raw/database/CSV",
  sql_path = "data-raw/database/SQL",
  drop_if_exists = TRUE
)

Arguments

conn	database connection
tbl_name	character string representing table name
csv_path	base path (excluding file) to the CSV file
sql_path	base path (excluding file) to the SQL file
drop_if_exists	Should the table be dropped (with CASCADE) if it already exists?

Value

the created database table returned as an R data.frame().

Note

It is assumed that the tbl_name mirrors both the basename of the CSV file and the SQL file (excluding extensions).

---

Data Manipulation Utilities

Description

Data Manipulation Utilities

Pull Unique Values from a dataframe

Usage

pull_unique(df, var)

Arguments

df	a provided data.frame()
var	character/numeric - quoted named of a variable from df or its numerical index.

Value

• pull_unique returns a character vector of unique, sorted values from specified column

Examples

df <- data.frame(let = rep(letters, 2), num = rep(c(1:26), 2))
pull_unique(df, 1)
pull_unique(df, "num")

---

Date Utility Functions

Description

A set of helper functions for dealing with dates in a typical actuarial analysis context.

Derive the number of months elapsed between two dates.

Usage

end_of_month(date)

start_of_month(date)

extract_date(string)

elapsed_months(end_date, start_date)

Arguments

date	character or date representation of a date.
string	string to extract a date from
end_date	end date
start_date	start date

Value

• end_of_month returns last day of the month given.

• start_of_month returns the first day of the month given.

• extract_date returns a date object extracted from the provided string.

numeric

See Also

as.Date(), lubridate::ceiling_date(), lubridate::floor_date(), flipTime::AsDate()

Examples

# character input
start_of_month("2020-08-13")
end_of_month("2017-10-20")

# can handle human-readable dates also
start_of_month("July 7, 1999")
end_of_month("February 5, 2019")

# date input
start_of_month(as.Date("2020-08-13"))
end_of_month(as.Date("2020-10-20"))

---

Document Datasets

Description

Creates skeleton to document datasets via roxygen2.

Usage

doc_data(
  obj,
  title = deparse(substitute(obj)),
  description = "DATASET_DESCRIPTION",
  write_to_file = TRUE,
  ...
)

Arguments

obj	object to document
title	Title
description	Description
write_to_file	Logical
...	N/A

Value

silently returns the doc_string

Examples

library(lossrx)
data(losses)
string <- doc_data(losses, "Loss Data", "Claims Data", FALSE)
cat(string)

---

exposures

Description

Exposure data.

Usage

exposures

Format

A data.frame with 855 rows and 5 variables:

member

character. DESCRIPTION.

program_year

double. DESCRIPTION.

department

character. DESCRIPTION.

payroll

double. DESCRIPTION.

miles

double. DESCRIPTION.

---

Extract numbers from a string

Description

Extract numbers from a string

Usage

extract_num(string)

Arguments

string

String to pull numbers from

Value

String of numbers

---

Feedback Message Utility Functions

Description

A set of helper functions for providing verbose feedback to the developer using this packages functions.

Usage

msg_field(x)

msg_value(x)

msg_done(x)

msg_bullet(x, bullet = cli::symbol$bullet)

msg_err(x)

msg_path(x)

msg_info(x)

msg_code(x)

msg_feedback(x)

Arguments

x	The string passed to various msg_ functions.
bullet	What to use for the message's bullet. Defaults to cli::symbol$bullet

See Also

• usethis::ui-questions()

• cli::list_symbols()

Other Feedback Utilities: indent(), inform()

---

Indent

Description

Indentation around various msg_ feedback functions.

Usage

indent(x, first = "  ", indent = first)

Arguments

x	The string passed to various msg_ functions.
first	what to indent with - defaults to " ".
indent	indentation of next line - defaults to first

Value

string

See Also

Other Feedback Utilities: feedback, inform()

---

Inform

Description

A wrapper around rlang::inform() for providing feedback to developers using this packages functions.

Usage

inform(...)

Arguments

...	Passed to rlang::inform()

Value

feedback in console

See Also

rlang::inform()

Other Feedback Utilities: feedback, indent()

---

interp - Actuarial Interpolation

Description

Interpolate Cumulative Loss Development Factors (CDFs).

Usage

interp(new_age, cdf_array, age_array, cutoff = 450, method = 3)

interp.dblexp(new_age, age_high, age_low, cdf_high, cdf_low)

interp.exp(new_age, age_high, age_low, cdf_high, cdf_low)

interp.linear(new_age, age_high, age_low, cdf_high, cdf_low)

Arguments

new_age	integer - value of the new age whose CDF is to be interpolated
cdf_array	numeric vector of CDFs (usually representative of the selected factors)
age_array	numeric vector of ages corresponding to the supplied cdf_array
cutoff	the largest possible age, after which, no interpolation is performed
method	integer - must be 1, 2, or 3 where 1 represents linear, 2 represents exponential, and 3 represents double exponential. Defaults to 3, but falls back onto 1 if necessary.
age_low, age_high	Low and High ages
cdf_low, cdf_high	Low and High CDFs

Details

This generic function comes with three possible methods:

1. Linear Interpolation

2. Exponential Interpolation

3. Double Exponential Interpolation

Actuaries often have to interpolate values in-between the selected Loss Development Factors (LDFs) / Cumulative Loss Development Factors (CDFs) in order to derive development factors at a variety of possible ages of maturity, outside the scope of the selected factors by the actuary.

For example, an actuary will select factors by maturity or development age in months using actuarial triangles. Due to the fact the actuarial selections are limited to the maturities present in the triangle (i.e. ages 12, 24, etc.), the factors for ages before, after, and in-between the selection ages must be interpolated.

A comprehensive approach to deriving the interpolated values would follow a pattern similar to the following:

• For ages of maturity ⁠<= First Selected Age of Maturity⁠ (i.e. ⁠<= 12⁠), factors are derived using persistencies. A persistency is simply a percentage value representing the percent paid/reported at a given age compared to the age's ceiling and floor. For example, a persistency as of age 3 would represent the percent paid/reported at 3 months of development out of the total percent paid/reported at 12 months of development. The persistency as of age 15 would represent the percent paid/reported at age 15 compared to the total percent paid/reported between ages 12 and 24.

• For ages of maturity ⁠Selected Age of Maturity Floor <= x <= Selected Age of Maturity Ceiling⁠, i.e. in-between ages, the factors are derived using double-exponential interpolation using the selections at the floor and ceiling ages.

• For ages of maturity ⁠>= Last Selected Age of Maturity⁠ (i.e. ⁠>= 106⁠), a decay factor approach is used to decay the final selected factor across the ages beyond that final age.

Value

derived numeric value for the supplied new_age's CDF

Functions

• interp.dblexp(): Double Exponential Interpolation

• interp.exp(): Exponential Interpolation

• interp.linear(): Linear Interpolation

Examples

cdfs <- c(3.579, 2.866, 2.489, 2.121, 1.876, 1.543, 1.222, 1.150, 1.109, 1.005, 1.0025)
ages <- seq(from = 12, to = (length(cdfs) * 12), by = 12)

interp(14, cdfs, ages)
interp(12, cdfs, ages) == cdfs[[1]]
interp(27, cdfs, ages, method = 2)

---

loss_run

Description

view losses as of a specific date

Usage

loss_run(val_date, trans_dat)

Arguments

val_date	date the valuation date of the loss run. Claim values from trans will be values as of the val_date
trans_dat	data frame of claims transactions

Value

data frame of claims (1 claim per row) valued as of the val_date

---

losses

Description

losses

Usage

losses

Format

A data.frame with 79748 rows and 30 variables:

eval_date

double. DESCRIPTION.

devt_age

double. DESCRIPTION.

occurrence_number

character. DESCRIPTION.

coverage

character. DESCRIPTION.

member

character. DESCRIPTION.

program_year

character. DESCRIPTION.

loss_date

double. DESCRIPTION.

rept_date

double. DESCRIPTION.

hire_date

double. DESCRIPTION.

report_lag

double. DESCRIPTION.

report_lag_group

integer. DESCRIPTION.

day_of_week

character. DESCRIPTION.

claim_type

character. DESCRIPTION.

claimant_state

character. DESCRIPTION.

loss_state

character. DESCRIPTION.

cause

character. DESCRIPTION.

department

character. DESCRIPTION.

tenure

double. DESCRIPTION.

tenure_group

integer. DESCRIPTION.

claimant_age

double. DESCRIPTION.

claimant_age_group

integer. DESCRIPTION.

driver_age

double. DESCRIPTION.

driver_age_group

integer. DESCRIPTION.

status

character. DESCRIPTION.

total_paid

double. DESCRIPTION.

total_incurred

double. DESCRIPTION.

count

double. DESCRIPTION.

open_count

double. DESCRIPTION.

close_count

double. DESCRIPTION.

incurred_group

integer. DESCRIPTION.

---

Open pkgdown site of the package

Description

Open pkgdown site of the package

Usage

open_pkgdown()

Examples

# open_pkgdown()

---

simulate_claims

Description

A function to simulate transactional actuarial claims/loss data for Property Casualty Insurance.

Usage

simulate_claims(
  n_claims = 1000,
  start_date = "2015-01-01",
  end_date = Sys.Date(),
  seed = 12345,
  loss_distribution = "lnorm",
  params = list(mean_log = 7.5, sd_log = 1.5),
  status_prob_open = 0.96,
  cache = FALSE,
  ...
)

Arguments

n_claims	Numeric - Number of claims to be simulated.
start_date, end_date	Character/Date - Start and End dates for simulation to create claims within (experience_period).
seed	Numeric - the seed is used to isolate randomness during statistical simulations.
loss_distribution	Character - must be one of the distributions mentioned in the details below. Defaults to lognormal.
params	Parameters associated with the specified loss_distribution in a list (i.e. list(mean_log = 7.5, sd_log = 1.5) for lognormal distribution).
status_prob_open	Numeric - must be within ⁠0 < x < 1⁠ and represents probability a claim is open when running binomial simulations for claims' status.
cache	Boolean/Logical - enable caching?
...	If needed

Details

Severity/Loss Distributions:

• Normal: norm

• Lognormal: lnorm

• Gamma: gamma

• LogGamma: lgamma

• Pareto: pareto

• Weibull: weibull

• Generalized Beta: genbeta

Value

The return value, if any, from executing the function.

---

To Proper

Description

To Proper

Usage

toproper(
  string,
  replace_underscores = TRUE,
  underscore_replacement = " ",
  return_as = c("titlecase", "uppercase", "lowercase", "asis"),
  uppers = c("Tpa")
)

Arguments

string	string to manipulate on
replace_underscores	Logical: if TRUE replaces all underscores with specified underscore_replacement argument's value.
underscore_replacement	Character: if argument replace_underscores equals TRUE, will replace all "_"'s with specified string.
return_as	How should the string be returned? Options are: "titlecase": Applies stringr::str_to_title. "uppercase": Applies toupper. "lowercase": Applied tolower. "asis": No manipulation. Returns as is.
uppers	Abbreviations to keep upper-case.

Value

"Proper" string

See Also

str_replace.

Examples

s <- "variable_a is awesome"
toproper(s)

---

View an Individual Claim's History

Description

This is a comprehensive function that allows the user to gain valuable insights about an individual claim's historical transactions.

Usage

view_claim_history(claim_id, claims_data = NULL)

Arguments

claim_id	Claim ID
claims_data	Dataset

Value

a list containing a) claim details b) transactional history c) interactive timeline

---