Mojca Miklavec wrote:

...

Thus I'm curious as to what others use... is R an efficient method to produce elegant charts? Is straight MetaPost preferable?

With metapost you can surely achive most beautiful results and it is not as difficult to learn as TeX-programming. Of course you might need more time to draw what you need or to write your own set of macros, but if you have high demands about quality this might be the way to go.

However, if you prefer doing it quicly using the existing tools (be aware that you have to learn how to use those tools as well), R or gnuplot might be an interesting choice. You'll be limited by the power of those two tools, but in most cases they should suffice for the normal usage.

The gnuplot module is still in development (I've been just begging Hans for help a few hours ago ;). Take a look at the demo section of gnuplot (http://gnuplot.sourceforge.net/demo_4.1/) to see if it can offer you what you want to do. In that case ask on the list again, I'll give you further pointers how to use it with ConTeXt (http://pub.mojca.org/gnuplot).

But basically you can take any program to draw graphs and include the resulting PDFs. I'm afraid that the macros from the paper which you pointed to, use some PostScript code that cannot be handled as-is (you need some conversion to PDF first) and I'm affraid that the effort put into making it work woudn't pay off now that you have a great varienty of other plotting programs, including metapost itself (esp. if the package has never been released - you'll probably hardly get any support for it).

Mojca

Thanks very much for your reply. Your advice seems strong, and in truth I have been intrigued by MetaPost for many years. This certainly seems a valid excuse to delve into it ;) David

Hi Sanjoy, On Sun, 23 Jul 2006 11:48:07 -0600, Sanjoy Mahajan wrote:

It's _Beautiful Evidence_ (2006) and it is beautiful. I think it was typeset in Quark. A few pages from the book, as well some of Tufte's updates on the book's production, are here:

http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=000262&topic_id=1

Can TeX/LaTeX/ConTexT-based typesetting can look as good? Perhaps!

I'm curious: What is preventing ConTeXt in particular from looking this good? What is the basis of your "Perhaps!"? What's missing? Best Idris -- Professor Idris Samawi Hamid Department of Philosophy Colorado State University Fort Collins, CO 80523 -- Using Opera's revolutionary e-mail client: http://www.opera.com/mail/

Sanjoy Mahajan wrote:

But where you put one figure will affect the placement of later figures. And maybe you paint yourself into a corner, and would like to backtrack and sacrifice excellent earlier placements in order to minimize terrible placements now...

http://www.nieuwarchief.nl/serie5/index.php gives some examplex of more complex layouts (done with a pretty old context btw); this is why column sets shows up (they will be used in future renderings) Hans ----------------------------------------------------------------- Hans Hagen | PRAGMA ADE Ridderstraat 27 | 8061 GH Hasselt | The Netherlands tel: 038 477 53 69 | fax: 038 477 53 74 | www.pragma-ade.com | www.pragma-pod.nl -----------------------------------------------------------------

Nicolas Grilly skreiv:

Karl Ove Hufthammer wrote:

...

Yes! R (especially using the new grid and lattice framework) produces excellent charts and graphs, with very sensible default options (much of it based on Cleveland's research).

What is Cleveland's research? Can you provide references on the web?

Cleveland has done much research on graphical perception and the visual decoding of information from data displays. He was one of the first to do actual scientific study on this. Earlier, many people had opinions on various common graphs (e.g., ‘pie charts are bad – I don’t like them’). Cleveland came along and did actual scientific *experiments* to show why some type of graphs were worse than others for presenting data (e.g., ‘humans are very bad at judging angles and very good at judging position along a common scale; that’s why pie charts are terrible and dot plots good at presenting (the same) data’), and he proposed new graphical display *based* on this research. See for example this very interesting and easy to read article: Title: Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods Author(s): William S. Cleveland; Robert McGill Source: Journal of the American Statistical Association, Vol. 79, No. 387. (Sep., 1984), pp. 531-554. Stable URL: http://links.jstor.org/sici?sici=0162-1459%28198409%2979%3A387%3C531%3AGPTEA... Some of Cleveland’s research resulted in novel graphical displays, such as trellis displays, coplots and dot plots, and much of it resulted in improvements to common displays. Unfortunately, many of these smaller improvements and very minor but important details seems to be unknown to people who design graphing software. Let me mention a few (not too exciting) examples: Circles should be used instead of rectangles as plotting symbols, especially with data overlap, because overlapping rectangles still look like rectangles, while overlapping circles look nothing like circles. Cleveland actually recommended a list of plotting symbols (for plotting several groups in one plot) for use in scatterplots; see: Title: A Model for Studying Display Methods of Statistical Graphics Author(s): William S. Cleveland Source: Journal of Computational and Graphical Statistics, Vol. 2, No. 4. (Dec., 1993), pp. 323-343. Stable URL: http://links.jstor.org/sici?sici=1061-8600%28199312%292%3A4%3C323%3AAMFSDM%3... Tick marks should point outwards, not inwards (so they don’t camouflage data). The data rectangle should always be slightly smaller than the scale-line rectangle (the box around the data), again to avoid camouflaging the data. These are just a few (perhaps less interesting) features of graph design that R does correctly, but many other programs (e.g., gnuplot, at least for tick marks and data rectangles) don’t (by default). Much of Cleveland’s research has been summarised in his excellent book W.S. Cleveland. Elements of Graphing Data. Revised edition. 1994. See also his other book W.S. Cleveland. Visualizing data. 1993. Other articles of his that may be of interest: Title: Graphical Perception and Graphical Methods for Analyzing Scientific Data Author(s): William S. Cleveland; Robert McGill Source: Science, New Series, Vol. 229, No. 4716. (Aug. 30, 1985), pp. 828-833. Stable URL: http://links.jstor.org/sici?sici=0036-8075%2819850830%293%3A229%3A4716%3C828... Abstract: Graphical perception is the visual decoding of the quantitative and qualitative information encoded on graphs. Recent investigations have uncovered basic principles of human graphical perception that have important implications for the display of data. The computer graphics revolution has stimulated the invention of many graphical methods for analyzing and presenting scientific data, such as box plots, two-tiered error bars, scatterplot smoothing, dot charts, and graphing on a log base 2 scale. Title: Graphical Perception: The Visual Decoding of Quantitative Information on Graphical Displays of Data Author(s): William S. Cleveland; Robert McGill Source: Journal of the Royal Statistical Society. Series A (General), Vol. 150, No. 3. (1987), pp. 192-229. Stable URL: http://links.jstor.org/sici?sici=0035-9238%281987%29150%3A3%3C192%3AGPTVDO%3... Abstract: Studies in graphical perception, both theoretical and experimental, provide a scientific foundation for the construction area of statistical graphics. From these studies a paradigm that has important applications for practice has begun to emerge. The paradigm is based on elementary codes: Basic geometric and textural aspects of a graph that encode the quantitative information. The methodology that can be invoked to study graphical perception is illustrated by an investigation of the shape parameter of a two-variable graph, a topic that has had much discussion, but little scientific study, for at least 70 years. Title: The Many Faces of a Scatterplot Author(s): William S. Cleveland; Robert McGill Source: Journal of the American Statistical Association, Vol. 79, No. 388. (Dec., 1984), pp. 807-822. Stable URL: http://links.jstor.org/sici?sici=0162-1459%28198412%2979%3A388%3C807%3ATMFOA... Abstract: The scatterplot is one of our most powerful tools for data analysis. Still, we can add graphical information to scatterplots to make them considerably more powerful. These graphical additions, faces of sorts, can enhance capabilities that scatterplots already have or can add whole new capabilities that faceless scatterplots do not have at all. The additions we discuss here-some new and some old-are (a) sunflowers, (b) category codes, (c) point cloud sizings, (d) smoothings for the dependence of $y$ on $x$ (middle smoothings, spread smoothings, and upper and lower smoothings), and (e) smoothings for the bivariate distribution of $x$ and $y$ (pairs of middle smoothings, sum-difference smoothings, scale-ratio smoothings, and polar smoothings). The development of these additions is based in part on a number of graphical principles that can be applied to the development of statistical graphics in general. -- Karl Ove Hufthammer