R Programming Help and Training Resources

RStudio has compiled a great collection of educational resources, and we recommend following their suggestions based on your current knowledge of R:

• For Beginners: https://education.rstudio.com/learn/beginner/
  • If you are just getting started with R, we recommend that you check out RStudio.cloud Primers: https://rstudio.cloud/learn/primers. This is a cloud-based learning environment that teaches the basics of R through interactive tutorials that allow you to code in your web browser, without requiring you to install R and related software beforehand.
• For Intermediates: https://education.rstudio.com/learn/intermediate/
  • For those of you who have experience with R, but need a handy reference for refreshing your knowledge of an essential R library or R Markdown, download a “cheat sheet” from here: https://rstudio.com/resources/cheatsheets/  
• For Experts: https://education.rstudio.com/learn/expert/

More traditional lecture-based coursework

• The John’s Hopkins Data Science Specialization on Coursera: https://www.coursera.org/specializations/jhu-data-science
  • If you only have time for one course, begin with “R Programming” (https://www.coursera.org/learn/r-programming). This is Course 2 of a sequence of several basic data science short courses that are all pretty useful
  • Time permitting, continue next with “Getting and Cleaning Data” (https://www.coursera.org/learn/data-cleaning) and “Reproducible Research" (https://www.coursera.org/learn/reproducible-research).
• Statistical Learning from Stanford: https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning
  • An excellent introduction to advanced but extremely useful and increasingly essential techniques like cross-validation, bootstrap resampling, regularized regression, and random forests—and how to implement these in R. Taught by some of the foremost experts in the field.