0. Introduction to R! for Actuaries Histograms
R! Working Party
Introduction to R! for Actuaries Histograms
Avraham Adler, FCAS, MAAA

1. Introduction to R! for Actuaries Histograms
It is often useful to view histograms of loss severity on both regular and logged scales
There are many ways to do so using R!
Following are two methods, one using the MASS package and the other using the more sophisticated ggplot2 package
R! code will be displayed in Courier New font, and can be copied directly into R! to generate results
R! text outputs will be displayed in blue-colored Courier New

2. Histograms Generating losses
For demonstration purposes, the actuar package will be used to generate a suite of losses
Fixing the initial random seed ensures reproducibility
set.seed(216)
library(actuar)
Claims<-rgenpareto(n=1000, shape1=2.7, shape2=1.4, scale=1e6)
summary(Claims)
Min. 1st Qu. Median Mean 3rd Qu. Max.

3. Histograms Using MASS

4. Using MASS Introduction and R! Code
The MASS package has some simple, but useful formulæ to generate histograms
library(MASS)
par(mfrow=c(2,1))
truehist(Claims)
lines(density(Claims), col="red", lwd=2)
truehist(log10(Claims))
lines(density(log10(Claims)), col="red", lwd=2)

5. Using MASS Results

6. Histograms Using ggplot2

7. Using ggplot2 Introduction
The ggplot2 package is both much more complicated and powerful
ggplot2 requires data frames and not merely vectors
The initial “plot” is created, and then layers are added or adjusted
Viewports are used to show multiple plots on the same device
The plot including histogram and density will be created first, and then a new scale can be overlaid on it for the second version

8. Using ggplot2 R! Code library(ggplot2) ClaimsDF<-data.frame(Claims)
Hist<-ggplot(ClaimsDF, aes(x=Claims))
Hist1<-Hist + geom_histogram(aes(y=..density..), colour="black", fill="dodgerblue") + stat_density(color="red", size=1, geom="line")
Hist2<-Hist1+scale_x_log10()
vplayout <- function(x, y) viewport(layout.pos.row=x, layout.pos.col=y)
grid.newpage()
pushViewport(viewport(layout=grid.layout(2,1)))
print(Hist1, vp=vplayout(1,1))
print(Hist2, vp=vplayout(2,1))

9. Using ggplot2 Results

10. Histograms References
Dutang, C.; Goulet, V. & Pigeon, M. “actuar: An R Package for Actuarial Science.” Journal of Statistical Software (2008)
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing:Vienna, Austria. 2010
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. New York:Springer-Verlag, 2002
Wickham, H., ggplot2: Elegant Graphics for Data Analysis. New York:Springer Science+Business Media, Inc., 2009