Coming of Age: R and Spatial Data Visualisation

I have been using R (a free statistics and graphics software package) now for the past four years or so and I have seen it become an increasingly powerful method of both analysing and visualising spatial data. Crucially, more and more people are writing accessible tutorials (see here) for beginners and intermediate users and the development of packages such as ggplot2 have made it simpler than ever to produce fantastic graphics. You don’t get the interactivity you would with conventional GIS software such as ArcGIS when you produce the visualisation but you are much more flexible in terms of the combinations of plot types and the ease with which they can be combined. It is, for example, time consuming to produce multivariate symbols (such as those varying in size and colour) in ArcGIS but with R it is as simple* as one line of code. I have, for example, been able to add subtle transitions in the lines of the migration map above.  Unless you have massive files, plotting happens quickly and can be easily saved to vector formats for tweaking in a graphics package.

R’s utilisation has been tempered by its relatively sparse documentation and challenging usability. The R community is increasingly aware of this with packages such as DeducerSpatial providing a graphical user interface to some of R’s spatial data functionality. More and more tutorials are appearing and people have been inspired by some high profile maps made with R (see here) so I am confident that it will be increasingly seen as the engine for slightly glossier analysis and visualisation packages.

R can’t do everything- I find handling map projections a bit tricky and its not possible to pan and zoom the maps as they are being created. In some circumstances I can’t do without these functions so I opt for a traditional GIS. Also, for the programmers out there used to the likes of Python and Java, R can have quite a few quirks in its syntax so be patient. Despite it’s flaws, if you have a large data processing and visualisation task R is a great option. It offers a high degree of flexibility in terms of input data formats and with packages such as twitteR, RCurl, and XML it is easier than ever to import online data sources from social media sites and data feeds.  Aside from traditional export formats for the visualisations it has become incredibly simple to export interactive and animated graphics using the googleVIS package or igraph for network visualisations. Such flexibility is invaluable if you are seeking to create a variety of different graphics from a single datasource without having to format it for multiple software packages. The great thing with R is the sense that it still has masses of unrealised potential for  future spatial data visualizations. If you know of any good visualisations or tutorials please leave a comment!
I should also say that if you would like to learn how to do these sorts of visualisations (and more!) come and do our masters course!
*simple might be a slightly optimistic way of thinking about it if you haven’t used R before, but with a bit of practice you will ge there!