Visualization is a useful tool for data exploration and statistical analysis, and it’s an important method for communicating your discoveries to others. While those two uses of visualization are related, they aren’t identical.
One of the reasons that I like ggplot so much is that it excels at layering together multiple views and summaries of data in ways that improve both data exploration and communication. Of course, getting at the right graph can be a bit of work, and often I will stop when I get to a visualization that tells me what I need to know — even if no one can read that graph but me. In this post I’ll look at a couple of ggplot graphs that take the extra step: communicating effectively to others.
For my examples I’ll use a pre-treated sample from the 2011 U.S. Census American Community Survey. The dataset is available as an R object in the file
phsample.RData; the data dictionary and additional information can be found here. Information about getting the original source data from the U.S. Census site is at the bottom of this post.
phsample.RData contains two data frames:
dhus (household information), and
dpus (information about individuals; they are joined to households using the column
SERIALNO). We will only use the
dhus data frame.
# Restrict to non-institutional households
# (No jails, schools, convalescent homes, vacant residences)
hhonly = subset(dhus, (dhus$TYPE==1) &(dhus$NP > 0))
I was flipping through my copy of William Cleveland’s The Elements of Graphing Data the other day; it’s a book worth revisiting. I’ve always liked Cleveland’s approach to visualization as statistical analysis. His quest to ground visualization principles in the context of human visual cognition (he called it “graphical perception”) generated useful advice for designing effective graphics .
I confess I don’t always follow his advice. Sometimes it’s because I don’t agree with him, but also it’s because I use ggplot for visualization, and I’m lazy. I like ggplot because it excels at layering multiple graphics into a single plot and because it looks good; but deviating from the default presentation is often a bit of work. How much am I losing out on by this? I decided to do the work and find out.
Details of specific plots aside, the key points of Cleveland’s philosophy are:
- A graphic should display as much information as it can, with the lowest possible cognitive strain to the viewer.
- Visualization is an iterative process. Graph the data, learn what you can, and then regraph the data to answer the questions that arise from your previous graphic.
Of course, when you are your own viewer, part of the cognitive strain in visualization comes from difficulty generating the desired graphic. So we’ll start by making the easiest possible ggplot graph, and working our way from there — Cleveland style.
I suspect I am not unique in not being able to remember how to control the point shapes in R. Part of this is a documentation problem: no package ever seems to write the shapes down. All packages just use the “usual set” that derives from S-Plus and was carried through base-graphics, to grid, lattice and ggplot2. The quickest way out of this is to know how to generate an example plot of the shapes quickly. We show how to do this in ggplot2. This is trivial- but you get tired of not having it immediately available. Continue reading
The important criterion for a graph is not simply how fast we can see a result; rather it is whether through the use of the graph we can see something that would have been harder to see otherwise or that could not have been seen at all.
– William Cleveland, The Elements of Graphing Data, Chapter 2
In this article, I will discuss some graphs that I find extremely useful in my day-to-day work as a data scientist. While all of them are helpful (to me) for statistical visualization during the analysis process, not all of them will necessarily be useful for presentation of final results, especially to non-technical audiences.
I tend to follow Cleveland’s philosophy, quoted above; these graphs show me — and hopefully you — aspects of data and models that I might not otherwise see. Some of them, however, are non-standard, and tend to require explanation. My purpose here is to share with our readers some ideas for graphical analysis that are either useful to you directly, or will give you some ideas of your own.
This is a tutorial on how to try out a new package in R. The summary is: expect errors, search out errors and don’t start with the built in examples or real data.
Suppose you want to try out a novel statistical technique? A good fraction of the time R is your best bet for a first trial. Take as an example general additive models (“Generalized Additive Models,” Trevor J Hastie, Robert Tibshirani, Statistical Science (1986) vol. 1 (3) pp. 297-318); R has a package named “gam” written by Trevor Hastie himself. But, like most R packages, trying the package from the supplied documentation brings in unfamiliar data and concerns. It is best to start small and quickly test if the package itself is suitable to your needs. We give a quick outline of how to learn such a package and quickly find out if the package is for you.