# Resources

In researching and developing statsTeachR, we have stumbled upon many useful and interesting sites with good resources for teaching R. Please email suggestions of other good resources that we could add to our list to nick [at] umass [dot] edu.

##### Notes, etc... from courses using R

- Biostatistics Methods 2, by Nicholas Reich, University of Massachusetts
- Linear Regression Models, by Jeff Goldsmith, Columbia University
- Introduction to Data Analysis, by Hadley Wickham, Rice University
- Data Analysis (on Coursera) [lecture notes] [video lecture channel], by Jeff Leek, Johns Hopkins University
- Data and Computing Fundamentals, Danny Kaplan, Macalester University
- Computing for Data Analysis, by Roger Peng, Johns Hopkins Bloomberg School of Public Health
- Undergraduate Advanced Data Analysis, by Cosma Shalizi, Carnegie Mellon

##### Labs, exercises, lesson planning

- Berkeley Case Studies Workshop: templates for lesson plans
- CATALYST
- Mathematical models of infectious disease teaching exercise
- MOSAIC
- OpenIntro Labs
- Swirl (Statistics with interactive R learning)

##### Free R Textbooks and related resources

- OpenIntro by David Diez, Christopher Barr, Mine Çetinkaya-Rundel
- Advanced R Programming by Hadley Wickham
- Introduction to statistical thought by Michael Lavine
- The Analysis of Data by Guy Lebanon
- Good examples: The R cookbook by Winston Chang
- Worked
*Statistical Sleuth*examples, by Nicholas Horton, Linda Loi, Kate Aloisio, and Ruobing Zhang - R inferno, by Patrick Burns: A great free book, but advanced
- Advanced Statistical Computing Notes by Robery Gray
- Wiki book on R
- Hmisc and Design intro by Carlos Alzola and Frank Harrell
- Graphical Methods for Presenting Data by Willard Cope Brinton (1914)