Using R for Introductory Statistics : John Verzani

Using R for introductory statistics 170The standard deviation isn’t always a better measure of spread. We will repeat thesimulation with exponential data and investigate. Before doing so, we look at script files,which save a sequence of commands to be executed.6.5.1 Script files and source()R can “read” the contents of a file and execute the commands as though they were typedin at the command line. The command to do this is source(), as insource(file=“filename”). (Most of the GUIs have this ability.)For example, if a file named “sim.R” contains thesecommands## file sim.Rf = function(n) sd(rexp(n))g = function(n) IQR(rexp(n))Figure 6.5 Boxplot of standarddeviation and IQR for normal datares.sd = c(); res.iqr = c()for(i in 1:200){res.sd[i] = f(100)res.iqr[i] = g(100)}boxplot(list(sd=res.sd, iqr=res.iqr))then the command> source("sim.R")will read and evaluate these commands producing a graph similar to Figure 6.6. Withexponential data, the spread of each statistic is similar. The more skewed or long-tailedthe data is, the wider the spread of the standard deviation compared to the IQR.By using a separate file to type our commands, we can more easily make changes thanwith the command line, and we can save our work for later use.