I started to become interested in visualisation last year, particularly at Culture Hack Scotland. They provided data, such as footfall in central Edinburgh, which some people visualised. I wanted to try something like that but was not sure how to start.
As a programmer, my first thought before doing something is to find out how it has been done before and to use libraries. I struggled to find a clear good answer. I also didn’t know much about it. Since then I have been asking people, reading and trying things out, so I think I can now make some recommendations to a beginner to the field.
As for libraries, I was expecting there to be some clear winners for this but there are not. I think the reason for this may be that good visualisations are often original. For example, the rose diagram, a favourite of Tufte which, by clearly showing deaths in the army caused by poor sanitation, persuaded the British government to improve conditions and saved lives. O’Reilly’s Beautiful Data also argues that beautiful visualisations must be original. Higher level libraries can restrict what you can make.
There are libraries for standard charts but even in these cases they do not really offer much. For example, drawing a bar chart is a matter of drawing rectangles. The scales can cause a bit more work, but it’s not a big deal really.
I did try some charting libraries: Google Charts for one. I found it inconvenient and limiting. I was trying to make something visually very similar to github’s punchcard, which is made with it. I started by copying how they had done it. Generating the GET request string was fairly unpleasant. Once I’d done it, it worked to start with but as soon as I added more data it complained the URL was too long. I found I could then use POST instead so I changed, with more fiddling. Then, having done all that, I found that the size in pixels of the output image was limited below what I required. I gave up on this and decided to do it manually.
Everyone I spoke to who got stuff done at the Culture Hack used low level drawing commands. It seems that this is the way to go. I used this method with the Perl SVG module to visualise source repositories. It works perfectly reasonably.
Some libraries worth looking at for SVG:
- Perl SVG module. I used this to visualise git commit times.
Other useful visualisation / data tools:
- R is a powerful statistics package which has various visualisation packages. I’ve used it for contour plots, heatmaps and 3D surface plots.
- Google Spreadsheets now has pivot tables. These are handy for data exploration.
- Google Refine. Helps clean up data, e.g. normalise capitalisation and spelling.
- TikZ. This is a very nice LaTeX graphics library.
The first book I read on this subject was O’Reilly’s Beautiful Visualisations. I was initially rather disappointed but I think this is in part because of the nature of the subject. When I learn a new subject, I want a theoretical overview and introduction the main concepts and principles. I am used to this in more purely technical subjects. This book, and to a lesser extent some others I have read, did not do this. This book is mostly a collection of reports on various visualisation projects. It may serve as a source of inspiration but it does not provide any overview or principles.
I then read Tufte’s second book . I was expecting this to be much better because Tufte seems to be considered the master of visualisation and his books are highly regarded. Unfortunately I found again a book of examples with little theory or practical guidance for designing my own visualisations. I also did not like how he seemed to jump from one subject to the next - it often felt like I had missed a page. This style I think was because he was really just making notes on the image on each page, rather than having some bigger theory or idea to explain.
There were some more general points though, for example the use of colour. A principle here is not to use large areas of strong colour but to have neutral background and highlight some parts with colour.
I then read a paper  which was much more what I was expecting. This outlined some basic methods of visually communicating quantitative values and evaluated some of them.
I am currently reading Tufte: Visual Display of Quantitative Information. This seems to try and introduce some principles but they are a poor attempt. For example, “Graphical Excellence” is defined pretty much tautologically, near enough, “Excellent Graphic is Excellent”. The book smells very nice though.
Some principles do seem useful though, particularly to maximise the information to ink ratio. This is similar to writing natural languages and code.
Vigorous writing is concise. A sentence should contain no unnecessary words [and] a paragraph no unnecessary sentences, for the same reason that a drawing should have no unnecessary lines and a machine no unnecessary parts. This requires not that the writer make all his sentences short, or that he avoid all detail and treat his subjects only in outline, but that every word tell.
— Elements of Style, William Strunk, Jr. - 1918
I would like to learn more about the perception of data graphics. For example, if you present some circles sized to represent some data, do people perceive the value as the diameter of the circle, the area, or something else? Should your graphics be adjusted to allow for human perceptual anomalies? (I write about these questions in a further post on area and circles.)
There is of course the artistic aspect to the design as well. If art is theft, books of good visualisations like I have seen should help in this regard. I would rather be able to produce something correct and functional first though.
In general, it seems best to use an API at the shape drawing level. Visualisation libraries are often more trouble than they are worth, and they will limit your ability to create an original visualisation.
Use SVG in general and for the web. For a paper, TikZ, a LaTeX library, can produce very nice graphics.
Read Tufte, but don’t expect much theory, it’s more a catalogue of ideas and critique. If you are interested in things like the perception of graphics you need to search the literature for research papers.
1. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods
William S. Cleveland and Robert McGill
Journal of the American Statistical Association, Vol. 79, No. 387 (Sep., 1984), pp. 531-554
2. Envisioning Information