Download presentation

Presentation is loading. Please wait.

Published byJeremiah Strickland Modified over 3 years ago

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

Similar presentations

OK

My Website Project: Bloomingdale Library’s Curiosity Club: A Web portal for science-minded kids Jill Rothstein SILS 654 Spring 2007 Presentation.

My Website Project: Bloomingdale Library’s Curiosity Club: A Web portal for science-minded kids Jill Rothstein SILS 654 Spring 2007 Presentation.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google