Loss Data Analytics (Data Works)

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Visit https://openacttexts.github.io/Loss-Data-Analytics/index.html#for-our-readers for the topic discussion.

Visit https://github.com/OpenActTexts/Loss-Data-Analytics/tree/master/Chapters for the data sets.

Visit https://openacttexts.github.io/LDARcode/#prerequisites for the R codes.

1 Introduction to Loss Data Analytics

This is blah blaa

summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Read Property Fund (.csv)

in_sample <- read.csv("PropertyFundInsample.csv", header = T, 
                      na.strings = c("."), stringsAsFactors = FALSE)
in_sample_2010 <- subset(in_sample, Year == 2010)

head(in_sample_2010)

##    PolicyNum Year LnCoverage     BCcov Premium Freq Deduct        y      lny
## 5     120002 2010   3.157489  23511493    7994    1   1000  6838.87 8.830378
## 10    120003 2010   4.741850 114646079   36687    1   5000  9711.28 9.181043
## 15    120004 2010   3.461150  31853574   17839    1   1000 10323.50 9.242178
## 20    120005 2010   3.487794  32713698   12431    0   5000     0.00 0.000000
## 25    120008 2010   3.856969  47321724   16090    1    500  3469.79 8.151849
## 30    120009 2010   4.280644  72286976   15180    0  25000     0.00 0.000000
##        yAvg  lnDeduct Fire5 NoClaimCredit TypeCity TypeCounty TypeMisc
## 5   6838.87  6.907755     1             1        0          1        0
## 10  9711.28  8.517193     1             0        0          1        0
## 15 10323.50  6.907755     1             0        0          1        0
## 20     0.00  8.517193     1             0        0          1        0
## 25  3469.79  6.214608     1             0        0          1        0
## 30     0.00 10.126631     1             0        0          1        0
##    TypeSchool TypeTown TypeVillage AC00 AC05 AC10 AC15
## 5           0        0           0    1    0    0    0
## 10          0        0           0    0    0    0    1
## 15          0        0           0    0    0    0    1
## 20          0        0           0    0    0    0    1
## 25          0        0           0    0    0    0    1
## 30          0        0           0    0    0    0    1

#summary(in_sample_2010)

Table 1.1 - 1.____

library(pander)
table <- as.data.frame(table(in_sample_2010$Freq))
names(table) <- c("Number of Claims", "Frequency")
pander(t(table))

Table continues below

Number of Claims	0	1	2	3	4	5	6	7	8	9	10	11
Frequency	707	209	86	40	18	12	9	4	6	1	3	2

Number of Claims	13	14	15	16	17	18	19	30	39	103	239
Frequency	1	2	1	2	1	1	1	1	1	1	1

pander(summary(in_sample_2010$Freq))

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
0	0	0	1.241	1	239

table_sev <- as.data.frame(in_sample_2010$yAvg)
names(table_sev) <- c("claims severity (2010)")
head(table_sev)

##   claims severity (2010)
## 1                6838.87
## 2                9711.28
## 3               10323.50
## 4                   0.00
## 5                3469.79
## 6                   0.00

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