Creating Loop for Triangles

Question

I have multiple objects that I created which include data I need for each triangle. I now want to create triangles and export to csv, but I have so many datasets that I'd like to do this in a loop. Bonus points if I can also name each csv the same as the triangle name (for example below, LiabilityTriangleMonthly). My current code that works on a one-by-one basis is as follows:

install.packages("ChainLadder")
install.packages("writexl")
install.packages("openxlsx")
install.packages("data.table")
install.packages("xlsx")
install.packages("sqldf")
library(ChainLadder)
library(writexl)
library(openxlsx)
library(data.table)
library(dplyr)
library(readxl)
library(sqldf)
LiabilityData_Monthly<-read.csv.sql("//filepath/FullTriangle.csv" , sql = "select * from file where LineofBusiness=194")

    Liability_Triangle_Monthly = as.triangle(LiabilityData_Monthly, 
                              dev = "MaturityMonth", 
                              origin = "AY", 
                              value = "IncurredTotal")

    LiabilityTriangleMonthly=as.matrix(Liability_Triangle_Monthly)
    LiabilityTriangleMonthly[is.na(LiabilityTriangleMonthly)]<-0
    write.csv(LiabilityTriangleMonthly,"//filepath/LiabilityTriangleMonthly.csv")

For reference, the names of 2 of my objects: LiabilityData_Monthly PhysDamData_Monthly

Below is my sample dataset (FullTriangle.csv) that I'm pulling my data from:

AY	MaturityMonth	IncurredTotal	LineOfBusiness
2023	3	1000	194
2023	6	5000	194
2023	9	10000	194
2023	12	15000	194
2023	15	20000	194
2024	3	100	194
2023	3	100	212
2023	6	500	212
2023	9	1000	212
2023	12	1500	212
2023	15	2000	212
2024	3	150	212
2023	3	1500	193
2023	6	4000	193
2023	9	9000	193
2023	12	12000	193
2023	15	16000	193
2024	3	200	193

Tried to create a list to loop through... but very new to R and need a simpler version than what I've seen on this site already.

Also kindly define the needed packages. Where is as.triangle from? Try to make your example minimal and self-contained. — Axeman
– Axeman, Commented May 17, 2024 at 21:10

Parfait · Accepted Answer · 2024-05-27 05:18:08Z

Assuming you need to run subsets across the LineOfBusiness column, consider defining a method to run one split, then iteratively call the method passing subset splits using by (object-oriented wrapper to tapply). Also, to properly name CSVs, below builds a small lookup data frame to merge to main data frame:

# HANDLE DATA SETS
FullTriangle <- read.csv("//filepath/FullTriangle.csv")

LineCodes <- data.frame(
  LineOfBusiness = c(193, 194, 212),
  LineOfBusinessTitle = c("Other_Monthly", "LiabilityData_Monthly", "PhysDamData_Monthly")
)

FullTriangle <- merge(FullTriangle. LineCodes, by="LineOfBusiness")

# USER-DEFINED METHOD
proc_triangle <- function(sub) {
    tri = as.triangle(
        sub, 
        dev = "MaturityMonth", 
        origin = "AY", 
        value = "IncurredTotal"
    )

    tri <- as.matrix(tri)
    tri[is.na(tri)] <- 0
    write.csv(tri, paste0("//filepath/", tri$LineOfBusinessTitle[1], ".csv"))

    return(tri)
}

# BUILD NAMED LIST OF TRIANGLE WITH CSV OUTPUTS
df_list <- by(FullTriangle, FullTriangle$LineOfBusiness, proc_triangle)

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