## Graphics

#first get some data datafilename="http://personality-project.org/r/datasets/maps.mixx.epi.bfi.data" data =read.table(datafilename,header=TRUE) #read the data file #see the graphic window for the output boxplot(epi) #boxplot of the five epi scales hist(epiE) #simple histogram plot(epiE,epiImp) #simple scatter plot model=lm(epiImp~epiE) #find the regression of Imp on E abline(model) #show the best fit regression line pairs(epi) #splom plot

Boxplot

Histogram

Simple scatter plot

Basic ScatterPlot Matrix (SPLOM)

More advanced graphics take advantage of the power of R to operate on the figure. The following code is taken from the help menu for the pairs command. It shows a SPLOM in the lower diagonal and reports the correlation coeffients in the upper diagnonal, scaling the font size to reflect the absolute value of the correlation. Note that once the function (panel.cor) is declared, it can but used later for other plots.

## the following code and figure is adapted from the help file for pairs ## put (absolute) correlations on the upper panels, ## with size proportional to the correlations. #first create a function (panel.cor) panel.cor <- function(x, y, digits=2, prefix="", cex.cor) { usr <- par("usr"); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) r = (cor(x, y)) txt <- format(c(r, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep="") if(missing(cex.cor)) cex <- 0.8/strwidth(txt) text(0.5, 0.5, txt, cex = cex * abs(r)) } # now use the function for the epi data. (see figure) pairs(epi, lower.panel=panel.smooth, upper.panel=panel.cor) #use the same function for yet another graph. pairs(bfi, lower.panel=panel.smooth, upper.panel=panel.cor)

Scatter plot matrix of EPI scales -- font size adjusted for absolute value of correlation

#this next command defines a new function which can then be used #for making multiple histograms multi.hist <- function(x) {nvar <- dim(x)[2] #number of variables nsize=trunc(sqrt(nvar))+1 #size of graphic old.par <- par(no.readonly = TRUE) # all par settings which can be changed par(mfrow=c(nsize,nsize)) #set new graphic parameters for (i in 1:nvar) { name=names(x)[i] #get the names for the variables hist(x[,i],main=name,xlab=name) } #draw the histograms for each variable on.exit(par(old.par)) #set the graphic parameters back to the original } #now use the function on the data multi.hist(person.data) #draw the histograms for all variables (see above)

part of a short guide to R

Version of November 26, 2004

William Revelle

>Department of Psychology

Northwestern University