**Statistical Modeling, Causal Inference, and Social Science » R**, and kindly contributed to R-bloggers)

Here. Indeed, I’d much rather be a legend than a myth.

I just want to clarify one thing. Walter Hickey writes:

[Antony Unwin and Andrew Gelman] collaborated on this presentation where they take a hard look at what’s wrong with the recent trends of data visualization and infographics.

The takeaway is that while there have been great leaps in visualization technology, some of the visualizations that have garnered the highest praises have actually been lacking in a number of key areas.

Specifically, the pair does a takedown of the top visualizations of 2008 as decided by the popular statistics blog Flowing Data.

This is a fair summary, but I want to emphasize that, although our dislike of some award-winning visualizations is central to our argument, it is only the first part of our story. As Antony and I worked more on our paper, and especially after seeing the discussions by Robert Kosara, Stephen Few, Hadley Wickham, and Paul Murrell (all to appear in Journal of Computational and Graphical Statistics, along with our original article and our rejoinder), we realized that a better framing is that there are tradeoffs in information graphics.

Some of the images that Antony and I don’t like *as statistical graphics* are fine as *visualizations*—they are unique, intriguing, and invite the viewer to learn more. Statistical graphs are different: they provide a more direct mapping of data but are better suited to viewers who are already interested in the topic.

In the internet age, we should not have to choose between attractive graphs and informational graphs: it should be possible to display both, via interactive displays. But to follow this suggestion, one must first accept that not every beautiful graph is informative, and not every informative graph is beautiful. Yes, it can sometimes be possible for a graph to be both beautiful and informative, as in Minard’s famous Napoleon-in-Russia map, or more recently the Baby Name Wizard, which we featured in our article. But such synergy is not always possible, and we believe that an approach to data graphics that focuses on celebrating such wonderful examples can mislead people by obscuring the tradeoffs between the goals of visual appeal to outsiders and statistical communication to experts.

Thus, in pointing out problems with the visualizations that were celebrated on the Flowing Data blog, we are not meaning to do a “takedown” of those images but rather to use these examples to explore the different goals of information graphics.

The post An epithet I can live with appeared first on Statistical Modeling, Causal Inference, and Social Science.

**leave a comment**for the author, please follow the link and comment on his blog:

**Statistical Modeling, Causal Inference, and Social Science » R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...