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An R Example to create a boxplot of returns of a financial series depending on weekdays
# 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")
myBoxPlotOutliers <- function(x) {
subset(x, x < tmp[1] | tmp[2] < x)
# Download some Data, e.g. the CBOE VIX
# Make a factor depending on the day of week. We will use this to segement data according to days of the week.
# 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=1:7,labels=c("Mon","Tue","Wed","Thu","Fri","Sat","Sun"),ordered=TRUE)
# a dataframe with the factor and the daily returns from close to close
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(, geom="boxplot") +
stat_summary(fun.y = myBoxPlotOutliers, geom="point")
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