Seven good reasons why everyone should be using R

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...

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!

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.

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.

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.

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).

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)

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.

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: