In honor of World Statistics Day and the read paper that my co-authors Chris Wild, Maxine Pfannkuch, Matt Regan, and I are presenting at the Royal Statistical Society today, we present the R code to generate a combination dotplot/boxplot that is useful for students first learning statistics. One of the over-riding themes of the paper is that introductory students (be they in upper elementary or early university) should keep their eyes on the data.

When describing distributions, students often are drawn to the most visually obvious aspects: median, quartiles and extremes. These are the ingredients of the basic boxplot, which is often introduced as a graphical display in introductory courses. Students are taught to calculate the quartiles, and this becomes one of the components of a boxplot.

One limitation of the boxplot is that it loses the individual data points. Given a dotplot (see here for an example of one in Fathom), it is very easy to guesstimate and draw a boxplot by hand. Wild et al. argue that doing so is probably the best way of gaining an appreciation of just what a boxplot actually is. The boxplot provides a natural bridge between operating entirely in terms of what is seen in graphics to reasoning using summary statistics. Retaining the dots in the combination dotplot/boxplot provides a reminder that the box plot is just summarizing the raw data, thus preserving a connection to more concrete foundations.

Certainly, such a plot has a number of limitations. It breaks down when there are a large number of observations, and has redundancy not suitable for publication. But as a way to motivate informal inference, it has potential value. It's particularly useful when combined in an animation using multiple samples from a known population, as demonstrated here (scroll through the file quickly).

In addition, the code, drafted by Chris and his colleagues Steve Taylor and Dineika Chandrananda at the University of Auckland, demonstrates a number of useful and interesting techniques.

**R**

Because of the length of the code, we'll focus just on the

`boxpoints3()`function shown below. The remaining support code must be run first (and can be found at http://www.math.smith.edu/sasr/examples/wild-helper.R). The original

`boxpoints()`function has a large number of options and configuration settings, which the function below sets at the default values.

# Create a plot from two variables, one continuous and one categorical.

# For each level of the categorical variable (grps) a stacked dot plot

# and a boxplot summary are created. Derived from code written by

# Christopher Wild, Dineika Chandrananda and Steve Taylor

#

boxpoints3 = function(x,grps,varnames1,varnames2,labeltext)

{

observed = (1:length(x))[!is.na(x) & !is.na(grps)]

x = x[observed]

grps = as.factor(grps[observed])

ngrps = length(levels(grps))

# begin section to align titles and labels

xlims = range(x) + c(-0.2,0.1)*(max(x)-min(x))

top = 1.1

bottom = -0.2

plot(xlims, c(bottom,top), type="n", xlab="", ylab="", axes=F)

yvals = ((1:ngrps)-0.7)/ngrps

text(mean(xlims), top, paste(varnames1, "by", varnames2, sep=" "),

cex=1, font=2)

text(xlims[1],top-0.05,varnames2, cex=1, adj=0)

addaxis(xlims, ylev=0, tickheight=0.03, textdispl=0.07, nticks=5,

axiscex=1)

text(mean(xlims), -0.2, varnames1, adj=0.5, cex=1)

# end section for titles and labels

for (i in 1:ngrps) {

xi = x[grps==levels(grps)[i]]

text(xlims[1], yvals[i]+0.2/ngrps,

substr(as.character(levels(grps)[i]), 1, 12), adj=0, cex=1)

prettyrange = range(pretty(xi))

if (min(diff(sort(unique(xi)))) >= diff(prettyrange)/75)

xbins = xi # They are sufficiently well spaced.

else {

xbins = round(75 * (xi-prettyrange[1])/diff(prettyrange))

}

addpoints(xi, yval=yvals[i], vmax=0.62/ngrps, vadd=0.075/ngrps,

xbin=xbins, ptcex=1, ptcol='grey50', ptlwd=2)

bxplt = fivenum(xi)

if(length(xi) != 0)

addbox(x5=bxplt, yval=yvals[i], hbxwdth=0.2/ngrps,

boxcol="black", medcol="black", whiskercol="black",

boxfill=NA, boxlwidth=2)

}

}

Most of the function tends to issues of housekeeping, in particular aligning titles and labels. Once this is done, the support functions

`addpoints()`and

`addbox()`functions are called with the appropriate arguments. We can call the function using data from female subjects at baseline of the HELP study, comparing PCS (physical component scores) for homeless and non-homeless subjects.

source("http://www.math.smith.edu/sasr/examples/wild-helper.R")

ds = read.csv("http://www.math.smith.edu/r/data/help.csv")

female = subset(ds, female==1)

with(female,boxpoints3(pcs, homeless, "PCS", "Homeless"))

We see that the homeless subjects have lower PCS scores than the non-homeless (though the homeless group also has the highest score of either group in this sample).

## 10 comments:

Gosh, do we really have to make the life of R so miserable? The "wheel" has already been there for years and it might not be a good idea to reinvent it:

ds = read.csv("http://www.math.smith.edu/r/data/help.csv")

smallds = subset(ds, female==1)

boxplot(pcs~homeless, data=smallds, horizontal=TRUE)

stripchart(round(pcs)~homeless, method='stack', data=smallds, add=TRUE)

Even better, with ggplot2:

source("http://www.math.smith.edu/sasr/examples/wild-helper.R")

ds = read.csv("http://www.math.smith.edu/r/data/help.csv")

female = subset(ds, female==1)

theme_update(panel.background = theme_rect(fill = "white", colour=NA))

theme_update(panel.grid.minor=theme_line(colour = "white", size = NA))

p <- ggplot(female, aes(factor(homeless),pcs))

p + geom_boxplot(outlier.colour="transparent") +

geom_point(position=position_dodge(width=0.2), pch="O") +

opts(title = "PCS by Homeless") +

labs(x = "Homeless", y="PCS") +

coord_flip() +

opts(axis.colour="white")

I like both of these as quick graphics. When I run the latter one, though, I get "

ymax not defined: adjusting position using y instead". Is that what's expected?

Ken, not sure, but the ymax error may be because of a mismatch of either ggplot2 or R versions. It works on a fresh R start on my machine without an error.

Just letting you know that this plot can be done in SAS too! Would you like the code?

That would be great, Kriss! Thanks for offering. You can send to me or post here. (I'll make a separate post out of it either way.)

Thanks for sharing the code? Just wondering if it is possible to limit the whisker length such that it does not extend beyond 1.5times the IQR. Thanks!

Is it possible to make these graphs in SAS v 9.1? If so, could you please share the code?

I think it would be a hassle to do this in SAS 9.1. You'd have to plot either the data, the box, or both using an annotate data set. Here's a link to a similar figure using SAS 9.2, though.

Is there SAS 9.2 code to do a combination dotplot/boxplot? If so, where can I find it?

Post a Comment