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# theHausdorffMetric/margintale_blog_1

Created Mar 4, 2012
An R Example to create a boxplot of returns of a financial series depending on weekdays
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 require(quantmod) require(ggplot2) require(reshape2) # The standard definitions of boxplots are non-obvious to interpret for non-statisticians. # A "the box is fifty percent, the line 95% and there you have 5% outlier points" is # typically more easily swallowed by practitioners. # I therefore define two functions which will change the boxplot appearance below. myBoxPlotSummary <- function(x) { r <- quantile(x, probs = c(0.025, 0.25, 0.5, 0.75, 0.975),na.rm=TRUE) names(r) <- c("ymin", "lower", "middle", "upper", "ymax") r } myBoxPlotOutliers <- function(x) { tmp<-quantile(x,probs=c(.025,.975),na.rm=TRUE) subset(x, x < tmp[1] | tmp[2] < x) } # Download some Data, e.g. the CBOE VIX getSymbols("^VIX",src="yahoo") # Make a factor depending on the day of week. We will use this to segement data according to days of the week. wd<-factor(.indexwday(VIX),levels=1:5,labels=c("Mon","Tue","Wed","Thu","Fri"),ordered=TRUE) # Note here that I do not use the weekdays function, because this will be locale dependent and lead # to an unwanted sorting of the days in the boxplot wd<-factor(.indexwday(VIX),levels=1:5,labels=c("Mon","Tue","Wed","Thu","Fri"),ordered=TRUE) # wd<-factor(.indexwday(VIX),levels=1:7,labels=c("Mon","Tue","Wed","Thu","Fri","Sat","Sun"),ordered=TRUE) # a dataframe with the factor and the daily returns from close to close tail(mydf<-data.frame(wd=wd,ROC(Cl(VIX)))) mdat<- melt(mydf) # plot the boxplots with own summary functions and outliers ggplot(mdat,aes(wd,value)) + opts(title = "Daily returns of the VIX") + xlab("") + ylab("% per day") + stat_summary(fun.data=myBoxPlotSummary, geom="boxplot") + stat_summary(fun.y = myBoxPlotOutliers, geom="point") #kruskal.test(x=mydf[,2],g=mydf[,1])
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