Transcript Introduction to R! for Actuaries Histograms

R! Working Party
Introduction to R! for Actuaries
Histograms
Avraham Adler, FCAS, MAAA
www.guycarp.com
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
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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
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set.seed(216)
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library(actuar)
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Claims<-rgenpareto(n=1000, shape1=2.7, shape2=1.4, scale=1e6)
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summary(Claims)
Min.
1905
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1st Qu.
175900
Median
417100
Mean
843900
3rd Qu.
Max.
932800 35240000
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Histograms
Using MASS
Using MASS
Introduction and R! Code
 The MASS package has some simple, but useful formulæ to generate
histograms
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library(MASS)
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par(mfrow=c(2,1))
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truehist(Claims)
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lines(density(Claims), col="red", lwd=2)
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truehist(log10(Claims))
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lines(density(log10(Claims)), col="red", lwd=2)
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Using MASS
Results
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Histograms
Using ggplot2
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
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Using ggplot2
R! Code
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library(ggplot2)
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ClaimsDF<-data.frame(Claims)
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Hist<-ggplot(ClaimsDF, aes(x=Claims))
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Hist1<-Hist + geom_histogram(aes(y=..density..), colour="black",
fill="dodgerblue") + stat_density(color="red", size=1, geom="line")
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Hist2<-Hist1+scale_x_log10()
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vplayout <- function(x, y) viewport(layout.pos.row=x, layout.pos.col=y)
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grid.newpage()
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pushViewport(viewport(layout=grid.layout(2,1)))
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print(Hist1, vp=vplayout(1,1))
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print(Hist2, vp=vplayout(2,1))
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Using ggplot2
Results
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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
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www.guycarp.com