1. Introduction to R

2. Statistical Software Statistical software – Wide variety of software tools that researchers use to analyze data – Common examples are Stata, SPSS, SAS, and R – Some simple statistics can also be run in Excel or other spreadsheet systems, but we need something more robust.

3. Why R? – Emphasizes text-based programming: we primarily work from a command line so we know what we are doing. – Powerful - Extends easily to cover advanced data processing – Allows a lot of options for advanced scenarios. Simpler software packages make simple tasks very easy, but do not offer the flexibility of R. – Open source; available to use on all major platforms (Unix/Linux, PC, Mac) – Anyone can download, and anyone can extend the language – Popular According to some measures, the most popular stats package in use today.

4. Downsides to R? Not especially easy to use for beginners. Not as easy to get ‘inline’ help from within the R system – You will often have to do a bit of searching on the Internet or in a R textbook to figure out a particular error or problem.