Transcript Slide 1
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 ([email protected]) • Dedicated discussion forums, e.g. www.r-project.org • Dedicated websites, e.g. www.statmethods.net • Many great books, including: