1. Seven good reasons why everyone should be using R

2. Why use R? There are a huge number of statistics packages available, including SPSS, Stata, Minitab, SAS, GenStat, etc., so why use something scary like R (see below)? Here are 7 excellent reasons...

3. Why use R? Reason 1 of 7: It’s FREE, and always will be! For comparison, a single user licence for SPSS costs around £3,000. There’s no guarantee that a particular commercial piece of stats software will be available at your next job – if you’re going to put in the effort to learn a stats programme, you might as well choose one that you will always have access to!

4. Why use R? Reason 2 of 7: R is used by the majority of academic statisticians. Statisticians develop new statistics in R, therefore: R code will generally be available for published statistical techniques; It contains advanced statistical routines not yet available in other packages; Collaborations with statisticians will be most fruitful if you share a common language. R packages are constantly updated to fix bugs and include new routines – tests in R therefore contain less errors and are more feature-rich than in other programs.

5. Why use R? Reason 3 of 7: R is platform independent. If you use Windows, this may not be such a big deal but it is a tremendous advantage if you collaborate with Mac or Linux users.

6. Why use R? Reason 4 of 7: R has unrivalled help resources. There are a large number of superb books and online resources dedicated R, aimed at both new and advanced users (more of this at the end) – far, far more than for any other statistics package. Because R is community constructed, free software, advanced users and the developers themselves are more willing to provide help. The quality and quantity of help for R is particularly relevant when trying to teach yourself a new technique or statistical method.

7. Why use R? Reason 5 of 7: R has state-of-the-art graphics capabilities. Put simply, R produces beautiful, publication-quality figures. R gives you a high level of control over all aspects of a figure’s appearance. You can produce figure types typically only available in a small number of specialist commercial packages (e.g. Matlab, Mathematica).

8. Why use R? Reason 6 of 7: The command line interface! The command line interface – perhaps counter intuitively – is much, much better for learning about stats. It is easy to share code with colleagues or download it from discussion forums, statistics websites, etc. You can tinker with the code to gain a really good understanding of what it actually does and whether it will work for you. It gives you complete control over all aspects of the stats you are running – you are not constrained by the options available in a graphical user interface. If you must, graphical user interfaces are available (e.g. http://sciviews.org/_rgui)

9. Why use R? Reason 7 of 7: R is far more than a statistics application. It is a full programming language, and so provides an unparalleled platform for programming new statistical methods, modifying existing ones and working with data (e.g. to automatically process large datasets, access data stored in databases) in an easy and straightforward manner. People have written packages for just about everything, from optimization to bioinformatics. It can link directly to other programming languages, such as Matlab, C, C++ and Java.

10. To find out more: Staff at Lincoln, e.g. Tom Pike, Biological Sciences (tpike@lincoln.ac.uk) Dedicated discussion forums, e.g. www.r-project.org Dedicated websites, e.g. www.statmethods.net Many great books, including: