I’ve just been through the process of contributing to the source code of a package in R (in a very small way) so here’s a short piece on how easy it was, and why anyone can do it! I originally wrote this post in August last year, but waited to post it until the new version of maptools was released. I missed this (we are now at 0.8-39!) and have only just rediscovered this post. It’s all still relevant though!

I have been using the Maptools library extensively in my use of R as a GIS, as well as in my teaching material (hosted at https://github.com/nickbearman/intro-r-spatial-analysis). The default plot order in the legend is to have the darkest colour at the bottom of the legend, and the lightest colour at the top. This was just something I accepted, and to be honest, never really thought about before.

I recently delivered a training course on R to some staff at the ONS (Office for National Statistics, England & Wales) and they said that their best practice guidelines are to have the darkest colour at the top of the legend. They asked me how to do this, which I didn’t know!

After some fiddling about with an R script, I created a version which worked for them. I then thought it might be useful to integrate this into the Maptools library, and emailed the package author, Roger Bivand. He was very helpful, and I added the additional code to the sourcefiles for Maptools. These are now avaliable in version 0.8-37 (or later), which has recently be released. Running update.packages(“maptools”) should get you the new version.

To reverse the colours is a simple matter of changing the legend code in two places. Using the example from the helpfile, the original line:

legend(x=c(5.8, 7.1), y=c(13, 14.5), legend=leglabs(brks), fill=colours, bty="n")

The revised line:

legend(x=c(5.8, 7.1), y=c(13, 14.5), legend=leglabs(brks, reverse = TRUE), fill=rev(colours), bty="n")

To give you some nice visual examples:

Or for those of you who have attended my R course:

The file I updated is at https://r-forge.r-project.org/scm/viewvc.php/pkg/R/colslegs.R?view=markup&root=maptools (this link shows the changes), and I also updated the helpfile. If you’ve done some R scripting, then it is not too difficult to do. Any questions, please post them here. Good luck!

This week I have run two courses on ‘Introduction to Using R for Spatial Analysis’ which have been very successful. Both courses sold out, with 15 people attending in Liverpool and 20 in London. We had people with a wide range of GIS and R experience, ranging from no experience in either GIS or R, to significant experience in one but little in the other.

We covered the basics of using R through the RStudio interface, which I find makes R easier to understand for newbies! I certainly found it much easier to learn R using RStudio, and still use it everyday for my R work (I’ve opened the native R interface maybe twice since I started using it!). We also looked projections and coordinate systems (which were at the bottom of a GIS problem a colleague had today) and at spatial data representation, particularly how to create a representative, truthful choropleth map, and I made use of a blog post about this very issue, which I recently tweeted.

We also had a number of very interesting discussions about the pros and cons of R vs other GIS software, such as ArcGIS or QGIS, as well as other languages, such as Python. Each has their own pros and cons, and in my work I regularly use a mix of these, depending on what I am trying to achieve.

 I am also in the process of developing an intermediate course that will focus more on spatial analysis. If you are interested in finding out more about when either the basic or the intermediate courses will be run again, please send me a message (using the contact form on this site) and I will add you to a list to hear about future courses.

All of the material from this course is freely available, and hosted on GitHub. Head over to http://github.com/nickbearman/intro-r-spatial-analysis and you can view the material yourself and work through it at your own pace. You can even use it to contribute to new teaching material, and if you do, please also make your material available through Creative Commons so others can benefit from it as well.

Cross-posted at http://geographicdatascience.com/blog/training/R-for-Spatial-Analysis-Courses-in-Liverpool-and-London/.