Comparing Medians and Inter-Quartile Ranges Using the Box Plot

[This article was first published on R on datascienceblog.net: R for Data Science, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The box plot is useful for comparing the quartiles of quantitative variables. More specifically, lower and upper ends of a box (the hinges) are defined by the first (Q1) and third quartile (Q3). The median (Q2) is shown as a horizontal line within the box. Additionally, outliers are indicated by the whiskers of the boxes whose definition is implementation-dependent. For example, in geom_boxplot of ggplot2, whiskers are defined by the inter-quartile range (IQR = Q3 – Q1), extending no further than 1.5 * IQR.

Creating a box plot in native R

We will use the warpbreaks data set to exemplify the use of box plots. In native R, a box plot can be obtained via boxplot.

data(warpbreaks)
# create positions for tick marks, one more than number of bars
x <- warpbreaks$breaks
# create labels
x.labels <- paste0(warpbreaks$wool, "-", warpbreaks$tension)
# specify colors for groups
group.cols <- c("darkred", "red", "darksalmon", 
                "darkblue", "blue", "lightblue")
cols <- c(rep(group.cols[1], 9), rep(group.cols[2], 9), 
        rep(group.cols[3], 9), rep(group.cols[4], 9), 
        rep(group.cols[5], 9), rep(group.cols[6], 9))
boxplot(x ~ warpbreaks$wool + warpbreaks$tension, col = group.cols)
legend("topright", legend = c(unique(x.labels)), 
        col = group.cols, pch = 20)

Creating a box plot with ggplot

We could compare the tensions for each type of wool using facet_wrap in the following way:

library(ggplot2)
ggplot(warpbreaks, aes(x = tension, y = breaks)) +
    geom_boxplot() + facet_wrap(.~wool) +
    ggtitle("Breaks for wool A and B")

ggplot(warpbreaks, aes(x = tension, y = breaks, fill = wool)) +
    geom_boxplot() + 
    ggtitle("Breaks for wool A and B")

Showing all points

To view the individual measurements associated with the box plot, we set outlier.shape = NA to prevent duplicates and call geom_point.

ggplot(warpbreaks, aes(x = tension, y = breaks, fill = wool)) +
    geom_boxplot(outlier.shape = NA) + 
    ggtitle("Breaks for wool A and B") +
    # dodge points horizontally (there are two bars per tick)
    # and jitter points horizontally so that they don't overlap
    geom_point(position = position_jitterdodge(jitter.width = 0.1))

Showing all the points helps us to identify whether the sample size is sufficient. In this case, most pairs of wool and tension exhibit high variabilities (especially wool A with tension L). Thus, the question would be whether this level of variability is inherent to the data or a result of the small number of samples (n = 9). Note that you can combine a box plot with a beeswarm plot to optimize the locations of the points.

To leave a comment for the author, please follow the link and comment on their blog: R on datascienceblog.net: R for Data Science.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)