R-Workshop for Beginners
Alan R. Lemmon Postdoctoral Research Fellow Center for Population Biology
The R statistical programming language is a powerful platform for data analysis and visualization used by many statisticians, engineers, and scientists. R, which is based on the statistical platform S, has gained much popularity due to its unique combination of power and flexibility. Since R is a free, open-source platform, many packages useful for the analysis of biological and non-biological data are available. Due to these features, the use of R can substantially increase the efficiency by which large or complex data sets are analyzed or visualized. Nevertheless, most biologists do not know how to use R because they think that the learning curve may be insurmountable without prior programming experience. This assumption is simply not true, provided that students are introduced to the components of R in an appropriate way and spend enough hands-on time practicing basic techniques so that a firm foundation is established.
During a 9-hour workshop spread over three days, I will train interested students, faculty, and postdocs in the R statistical programming language (prior knowledge of programming and advanced statistics is NOT required). Each session will contain several sort lectures and demonstrations, as well as ample time for the participants to practice using R. The primary aim of the R workshop will be to introduce the participants to the most useful, time-saving features of R, and to give the participants enough hands-on experience so that they feel comfortable learning additional components of R on their own. The workshop will focus on the components that make R unique, instead of emphasizing statistical tests that can be performed in many other statistical packages. After this workshop, participants should feel comfortable learning any one of the numerous statistical tests available in R. Topics covered will include: R basics, using help menus, handling vectors and matrices, loading and saving data, summarizing data, statistical distributions, plotting data, writing functions and loops, sorting, and writing custom randomization tests. All of the demonstrations will be compiled into a comprehensive manual that will be made available to the workshop participants for further reference, as well as to other members of the Center for Population Biology as a stand-alone tutorial.