Resources

R

Introductory Tutorials and Course Materials

• Introduction to R (Rice & Thornton, UW Biostat)
• Swirl (interactive tutorial from within the R console)
• UW DEOHS Computing Bootcamp - R (click on the *.md files)
• UW Biost-578/Envh-590 Introduction to R
• Getting Started with R (York University
• Getting Started with R (Cross Validated / Stack Exchange)

Free Online R Courses

• Introduction to Data Science with R
  • By Roger Peng - YouTube - free
  • See: his books
• Up and Running with R (2-1/2 hours)
  • By Barton Poulson - Lynda.com - free with SPL library card
  • Login through SPL
  • using your SPL library card to gain free access.
  • See: R Statistics Essential Training (6 hours)
• R for the Intimidated (2 hours)
  • By Annika Salzberg - DataCamp - free with registration
• Statistics and R for the Life Sciences
  • By Rafael Irizarry and Michael Love - edX - free with registration
  • Part of an entire 8-course series
• Introduction to R (DataCamp) - free with registration
• Coursera Data Science Specialization (JHU) - free to audit with registration
• Software Carpentry Lessons
  • Programming with R (Inflammation)
  • Reproducibility with R (Gapminder)
• Data Carpentry Lessons
  • R Data Analysis and Visualization (Ecology)

University of Washington (UW) Courses & Resources

• CS&SS 508 Introduction to R for Social Scientists (1 credit)
• E-Science Institute Drop-in hours
• UW Center for Social Science Computation & Research Consulting

R eBooks at UW Library

These are available at no cost to UW students, staff, or faculty. The links we have provided (for the title text) are to the UW-licenced copy. There are many more R books available through the UW, but these are some of our favorites.

• Beginning Data Science with R
  • by Manas A. Pathak
• R for Stata Users
  • by Robert A. Muenchen and Joseph Hilbe
• R in a Nutshell First Edition and R in a Nutshell Second Edition
  • by Joseph Adler and M. Eng
• R by example
  • by Jim Albert and Maria L. Rizzo
• The Art of R Programming: A Tour of Statistical Software Design
  • by Norman Matloff
• Data Science in R
  • by Deborah Nolan
• R for Everyone: Advanced Analytics and Graphics
  • by Jared P. Lander
• R in Action: Data Analysis and Graphics with R
  • by Robert Kabacoff