Welcome to "Introduction to Data Analysis with R"! This mini-course is aimed at people who are new to programming.

- Learn some basics of R
- Know how to supplement your knowledge with online resources
- Have some fun programming

- A computer
- R downloaded
- RStudio downloaded

**Everyone who's anyone is doin it.** R is the statistical software used by most academic statisticians. This means that if you go to a statistician for help with your data,
you'll be able to understand their help! It *also* means that the support network is unparalleled. For almost any question you have,
someone else has already asked it and had it answered in a clear and reproducible way. Check it out here.

**Reproducible research.** Using R (especially keeping R scripts) allows us to do reproducible research. You can go back at any time and reproduce exactly
what you did 5 months (or years, or whatever!) ago to get that one graph. It also allows other researchers to follow the steps
that you've taken to analyze the data.

**R is open-source.** This means it's free to use. More importantly, it means that there is a large community of R users who are regularly
improving R. It's only ever going to get more useful!

**It's platform independent.** You can use R in Windows and Linux/Mac.

**R is a programming language.** As a programming language, R gives you flexibility to create new functions for data analysis and visualization that fit your
own needs. As a part of the R community, you can then share your functions with everyone else and help make R even better!.

**Beautiful evidence.** The graphics are beautiful. R gives you a lot of control over your data visualization.

If you're interested in more tutorials (which I highly recommend!), there's a nice interactive online one at Try R Codeschool. RStudio has developed a package called swirl, which is a tutorial that runs in the command line of RStudio (great for learning R in the context of RStudio!). Joe Fruehwald's Study group for R is a great resource for R for linguistic data.