Every data analyst knows that a good graph is worth a thousand words, and perhaps a hundred tables. But how should one create a good, clean graph? In R, this task is anything but easy. Many users find it almost impossible to resist the siren song of adding grid lines, including grey backgrounds, using elaborate color schemes, and applying default font sizes that makes the text much too small in relation to the graphical elements. As a result, many R graphs are an aesthetic disaster; they are difficult to parse and unfit for publication.
In constrast, a good graph obeys the golden rule: “create graphs unto others as you want them to create graphs unto you”. This means that a good graph is a simple graph, in the Einsteinian sense that a graph should be made as simple as possible, but not simpler. A good graph communicates the main message effectively, without fuss and distraction. In addition, a good graph balances its graphical and textual elements – large symbols demand an increase in line width, and these together require an increase in font size.
The graphing chaos is exacerbated by the default settings in R (and the graphical packages that it provides, such as ggplot2), which are decidedly suboptimal. For instance, the font size is often too small, and the graphical elements are not sufficiently prominent. As a result, creating a good graph in R requires a lot of tinkering, not unlike the process of editing the first draft of a novice writer.
Fortunately, many plots share the same underlying structure, and the tinkering that has led to a clean graph of time series A will generally provide useful starting values for a clean graph of time series B. To exploit the overlap in structure, however, the user needs to remember the settings that were used for the first graph. Usually, this means that the user has to recall the location of the relevant R code. Sometimes the search for this initial code can take longer than the tinkering that was required to produce a clean graph in the first place.
In order to reduce the time needed to find relevant R code, we have constructed a compendium of clean graphs in R. This compendium, available at http://shinyapps.org/apps/RGraphCompendium/index.html, can also be used for teaching or as inspiration for improving one’s own graphs. In addition, the compendium provides a selective overview of the kind of graphs that researchers often use; the graphs cover a range of statistical scenarios and feature contributions of different data analysts. We do not wish to presume the graphs in the compendium are in any way perfect; some are better than others, and overall much remains to be improved. The compendium is undergoing continual refinement. Nevertheless, we hope the graphs are useful in their current state.
As an example of what the compendium has to offer, consider the graph below. This graph shows the proportion of the popular vote as a function of the relative height of the US president against his most successful opponent. Note the large circles for the data, the thick line for the linear relation, and the large font size for the axis labels. Also, note that the line does not touch the y-axis (a subtlety that requires deviating from the default). As in the compendium, the R code that created the graph is displayed after clicking the box “Show R-code”.
A more complicated example takes the same data, but uses it to plot the development of the Bayes factor, assessing the evidence for the hypothesis that taller presidential candidates attract more votes. This plot was created based in part on code from Ruud Wetzels and Benjamin Scheibehenne. Note the annotations on the right side of the plot, and the subtle horizontal lines that indicate Jeffreys’ criteria on the evidence. It took some time to figure out how to display the word “Evidence” in its current direction.
A final example is borrowed from the graphs in JASP (http://jasp-stats.org), a free and open-source statistical software program with a GUI not unlike that of SPSS. In contrast to SPSS, JASP also includes Bayesian hypthesis tests, the results of which are summarized in graphs such as the one below.
The compendium contains many more examples. We hope some R users will find them convenient. Finally, if you create a clean graph in R that you believe is a candidate for inclusion in this compendium, please do not hesitate to write an email to EJ.Wagenmakers@gmail.com. Your contribution will be acknowledged explicitly, alongside the code you provided.
Eric-Jan Wagenmakers and Quentin Gronau
University of Amsterdam, Department of Psychology.
One critique frequently heard about Bayesian statistics is the subjectivity of the assumed prior distribution. If one is cherry-picking a prior, of course the posterior can be tweaked, especially when only few data points are at hand. For example, see the Scholarpedia article on Bayesian statistics:
In the uncommon situation that the data are extensive and of simple structure, the prior assumptions will be unimportant and the assumed sampling model will be uncontroversial. More generally we would like to report that any conclusions are robust to reasonable changes in both prior and assumed model: this has been termed inference robustness
Therefore, it is suggested that …
In particular, audiences should ideally fully understand the contribution of the prior distribution to the conclusions. (ibid)
In the example of Bayes factors for t tests (Rouder, Speckman, Sun, Morey, & Iverson, 2009), the assumption that has to be defined a priori is the effect size δ expected under the H1. In the BayesFactor package for R, this can be adjusted via the r parameter. By default, it is set to 0.5, but it can be made wider (larger r’s, which means one expects larger effects) or narrower (r’s close to zero, which means one expects smaller effects in the population).
In their reanalysis of Bem’s ESP data, Wagenmakers, Wetzels, Borsboom, Kievit, and van der Maas (2011, PDF) proposed a robustness analysis for Bayes factors (BF), which simply shows the BF for a range of priors. If the conclusion is the same for a large range of priors, it could be judged to be robust (this is also called a “sensitivity analysis”).
I wrote an R function that can generate plots like this. Here’s a reproduction of Wagenmakers’ et al (2011) analysis of Bem’s data – it looks pretty identical:
You can throw in as many t values and corresponding sample sizes as you want. Furthermore, the function can compute one-sided Bayes factors as described in Wagenmakers and Morey (2013). If this approach is applied to the Bem data, the plot looks as following – everything is shifted a bit into the H1 direction:
Finally, here’s the function:
Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t-tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin and Review, 16, 225-237. [for a PDF, see bottom of this page]
Wagenmakers, E.-J., & Morey, R. D. (2013). Simple relation between one-sided and two-sided Bayesian point-null hypothesis tests. Manuscript submitted for publication (website)
Wagenmakers, E.-J., Wetzels, R., Borsboom, D., Kievit, R. & van der Maas, H. L. J. (2011). Yes, psychologists must change the way they analyze their data: Clarifications for Bem, Utts, & Johnson (2011) [PDF]
I currently program an automated report generation in R – participants fill out a questionnaire, and they receive a nicely formatted pdf with their personality profile. I use knitr, LaTex, and the sendmailR package.
Some participants did not provide valid email addresses, which caused the sendmail function to crash. Therefore I wanted some validation of email addresses – here’s the function:
Let’s test some valid and invalid adresses:
The regexp is taken from www.regular-expressions.info and adapted to the R style of regexp. Please note the many comments (e.g., here or here) about “Is there a single regexp that matches all valid email adresses?” (the answer is no).
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