Introduction to Modern R

Learn R the easy way, by focusing on modern “tidyverse” functions. This 2-day workshop starts at ground zero and shows you how to import data, then transform, visualize, and analyze it. You’ll have hands-on experience every step of the way. The slides, examples, and output are all integrated into a single document. You can add your own notes as you go, and when finished, you “knit” it all together into a single 180-page book.


This workshop is available at your organization’s site, or via webinars.

The on-site version is the most engaging, generating much discussion and occasionally veering off briefly to cover topics specific to a particular organization. The instructor presents a topic for around twenty minutes. Then we switch to exercises, which are already open in another tabbed window. The exercises contain hints that show the general structure of the solution; you adapt those hints to get the final solution. The complete solutions are in a third tabbed window, so if you get stuck the answers are a click away. The typical schedule for training on site is located here.

A webinar version is also available. The approach saves travel expenses and is especially useful for organizations with branch offices. It’s offered as two half-day sessions, often with a day or two skipped in between to give participants a chance to do the exercises and catch up on other work. There is time for questions on the lecture topics (live) and the exercises (via email). However, webinar participants are typically much less engaged, and far less discussion takes place.

For further details or to arrange a webinar or site visit, contact the instructor, Bob Muenchen, at


This workshop assumes no prior knowledge of R. Some knowledge of statistics is helpful, but not required. The instructor is well aware that knowledge of statistics fades rapidly when not used.

Learning Outcomes

When finished, participants will be able to use R to import data, transform it, create publication quality graphics, perform commonly used statistical analyses and know how to generalize that knowledge to more advanced methods.


Robert A. Muenchen is the author of R for SAS and SPSS Users and, with Joseph M. Hilbe, R for Stata Users. He is also the creator of, a popular website devoted to analyzing trends in analytics software and helping people learn the R language. Bob is an ASA Accredited Professional Statistician™ with 35 years of experience and is currently the manager of OIT Research Computing Support (formerly the Statistical Consulting Center) at the University of Tennessee. He has taught workshops on research computing topics for more than 500 organizations and has offered training in partnership with the American Statistical Association,, New Horizons Computer Learning Centers, Revolution Analytics, RStudio and Xerox Learning Services. Bob has written or coauthored over 70 articles published in scientific journals and conference proceedings and has provided guidance on more than 1,000 graduate theses and dissertations.

Bob has served on the advisory boards of SAS Institute, SPSS Inc., StatAce OOD, Intuitics, the Statistical Graphics Corporation and PC Week Magazine (now eWeek). His suggested improvements have been incorporated into SAS, SPSS, JMP, STATGRAPHICS, and many R packages. His research interests include statistical computing, data graphics and visualization, text analytics, and data mining.

Computer Requirements

We will use the free and open-source version of R, which you can download R for free here: We will also use RStudio, which you can download for free here: If you already know a different R editor, that’s fine too.

On-site training is best done in a computer lab with a projector and, for large rooms, a PA system. The webinar version is delivered to your computer using Zoom (or similar webinar systems if your organization has a preference.)

Course Materials

Course notes, programs, data sets, practice exercises, and solutions will be sent to you in electronic form a week before the workshop. For ease of searching, the course notes are indexed by keywords from Excel, SQL, SAS, SPSS, and Stata. Searching on any fundamental topic from those languages is likely to take you directly to the R equivalent.

Other searchable keywords include alerts on topics that people often err on, as well as common R warning and error messages along with their meanings and solutions. The notes, code, and output are summarized in the 156-page book, Introduction to Modern R, which has an interactive table of contents, allowing you to jump quickly to any topic.

Course Outline
(In-depth data management topics are covered in an optional separate workshop that usually follows immediately after this one.)

  1. Introduction
  2. Overview of R
  3. Installing and Maintaining R
  4. RStudio Basics & Workshop Files
  5. R Markdown
  6. R Language Basics
  7. Help & Documentation
  8. Data Structures
  9. Managing Files & Workspace
  10. Controlling Functions
  11. Data Acquisition
  12. Choosing Variables
  13. Choosing Observations
  14. Choosing Both Vars & Obs
  15. Transformations
  16. Missing Values
  17. Graphics: Base
  18. Graphics: ggplot2
  19. Writing & Applying Functions
  20. Statistics Review
  21. Descriptive Statistics
  22. Correlation & Regression
  23. Crosstabulation
  24. Comparing Two Groups
  25. Comparing >2 Groups: ANOVA
  26. High-Quality Output
  27. Debugging R Programs
  28. Graphical User Interaces to R

Here is a slide show of previous workshops.

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