Appendix B. Resources

B.1. Gnuplot

The official website for the gnuplot project is www.gnuplot.info. There you’ll find the official documentation, including the extensive and helpful FAQ, as well as links (not always current) to other sites and related projects.

Community support is available through the newsgroup comp.graphics.apps .gnuplot and the mailing list [email protected]. There’s also a mailing list for gnuplot developers: [email protected].

B.1.1. Websites

Several websites are dedicated to gnuplot and often contain very good information, as well as tips and tricks. The following stand out both for the quality of their material and for the range of topics covered:

• Gnuplotting at www.gnuplotting.org—Hagen Wierstorf maintains a page with some stunning examples and uncommon gnuplot applications.
• Impossible Gnuplot Graphs at www.phyast.pitt.edu/~zov1/gnuplot/html/intro.html—The first of two websites with gnuplot tricks by Zoltán Vörös.
• Gnuplot Tricks at http://gnuplot-tricks.blogspot.com—The other site prepared by Zoltán Vörös.
• Gnuplot—not so Frequently Asked Questions at http://lowrank.net/gnuplot/index-e.html and at http://mng.bz/5i80—Originally collected by Toshihiko Kawano at Los Alamos National Lab, this is a large collection of techniques. Some of the material is a little dated, but most of it is still relevant and interesting. (The original site has gone away, but the material is now fortunately hosted by volunteers.)

For specific topics, a quick Google search will typically result in additional posts and articles.

B.1.2. Books

To my knowledge, there is only one other book dedicated exclusively to gnuplot:

• Gnuplot Cookbook by Lee Phillips (Packt Publishing, 2012)—This book contains a collection of “recipes” for common, and not-so-common, tasks.

B.2. Data repositories

I assume that you’ll usually have your own data that you want to plot! Still, the need for example data sets arises from time to time. Here are some good collections of freely available data sets on the web:

• UCI Machine Learning Repository at http://archive.ics.uci.edu/ml/index.html—A very large and frequently updated collection of data sets.
• Journal of Statistics Education Data Archive at www.amstat.org/publications/jse/jse_data_archive.htm—A large collection of often very interesting data sets used in papers published by the JSE.
• Time Series Data Library at http://data.is/TSDLdemo—A very large collection of time series data, collected by Rob Hyndman. Unfortunately only sparsely documented.
• The Data and Story Library at http://lib.stat.cmu.edu/DASL—An older, mediumsized collection of data sets and “stories,” intended to teach basic statistics.
• StatLib Datasets Archive at ftp://rcom.univie.ac.at/mirrors/lib.stat.cmu.edu/datasets/.index.html—Many data sets that have been mentioned in textbooks can be downloaded from this site. You usually need the respective textbook to make sense of the downloads.
• 100+ Interesting Data Sets for Statistics at http://rs.io/100-interesting-data-sets-forstatistics—A large and heterogeneous collection of links and pointers to data archives on the web.

B.3. Books

Here are a few books on scientific graphics and graphical discovery that I’ve found helpful and interesting:

• The Elements of Graphing Data by William S. Cleveland (Hobart Press, 1994)—Probably the definitive reference on graphical analysis (as opposed to presentation graphics). Much of the material in chapter 13 and chapter 14 has been influenced by Cleveland’s careful approach. There’s also a companion volume, Visualizing Data (1993), by the same author.
• Visual Revelations by Howard Wainer (Copernicus/Springer, 1997), Graphic Discovery by Howard Wainer (Princeton University Press, 2005), and Picturing the Uncertain World: How to Understand, Communicate, and Control Uncertainty through Graphical Display by Howard Wainer (Princeton University Press, 2011)—Three collections of short essays on statistical graphics, written in an entertaining, semi-popular style. Many good ideas and interesting case studies can be found here, but also a lot of editorializing about various issues.
• Data Analysis with Open Source Tools by Philipp K. Janert (O’Reilly, 2009)—The first several chapters of this book cover graphical methods in data analysis (similar in spirit to chapters 13 and 14, but in more depth). The remainder of the book discusses a variety of other (nongraphical) techniques for extracting insights from data.