Introduction to R for Data Analysis: A Short Course

A 4-Day Livestream Seminar Taught by Andrew Miles, Ph.D.

Download Sample Course Slides

R is a free and open-source package for statistical analysis that is widely used in the social, health, physical, and computational sciences. Researchers gravitate to R because it is powerful, flexible, and has excellent graphics capabilities. It also has a large and rapidly growing community of users.

This course is designed as an introduction to R for those who are looking to use R for applied statistical tasks. Topics include data coding and management as well how to perform basic descriptive, bivariate, and multivariate analyses. We will also address the fundamentals of programming in R, using plots to explore data, and how R can simplify the process of exporting the results from statistical analyses. To be clear, this course does not teach the principles of data management or statistical analysis. Instead, it assumes prior knowledge of these topics and focuses on explaining how they can be implemented in R.

Starting June 4, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.

*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously. 

Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.

More Details About the Course Content

There is no way to cover all the possible uses of R in a single course, so an important theme will be helping you understand the fundamentals of how R “thinks” so that you can begin to use R independently. For this reason, the course focuses on basic R functions and practical issues like interpreting output and getting help. After this course, you will be well-equipped to tailor R to the sort of work they do.

This course is thoroughly hands-on. You are encouraged to write code along with the instructor, and to participate in the carefully-designed exercises that will be interspersed throughout the seminar and assigned as “take-home” exercises after each class session. By the end of the course, you can expect to log more than a dozen hours of guided practice coding in R.


To participate in the hands-on exercises, you are strongly encouraged to use a computer with the most recent version of R installed. You are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.

Who Should Register?

This course is for anyone who wants to learn R. No prior knowledge of R is assumed, though those lacking experience with any type of statistical coding language might find the course more intensive (but doable!). You should also have prior experience with data management, and a basic understanding of fundamental bivariate and multivariate statistics including linear regression and the generalized linear model.


Day 1:Working with R, Working with Data

Introduction: R Basics

Data Basics

  • Importing and Exporting Data
  • Basic Data Structures in R
  • Viewing and Modifying Objects
  • Missing Data

Recoding Data

  • Logical Operators
  • Functions for Recoding Data

Day 2:Exploring Data in R

Essential R Skills

Understanding R’s Functions and Help Files

Writing Understandable R Code

Exploring Data

  • Descriptive Statistics
  • Exploratory Data Plots

A Few Bivariate Techniques

  • Classic Statistical Tests
  • Bivariate Plots

Day 3:Analyzing Data in R

Linear Models

  • Detecting and Correcting Problems
  • Predictions

Generalized Linear Models

Visualizing Model Results

Day 4:Practical R Skills


  • Control Structures (if/else statements, loops)
  • Writing Functions and Functional Coding

Getting Results Out of R

Reviews of Introduction to R for Data Analysis

“Andrew’s knowledge and understanding of R is unbelievable. No matter what the questions were, Andrew confidently answered in a welcoming manner that made the participant feel like their question added value to the teaching and was a great way to demonstrate a feature or aspect of R that benefited the class. Andrew did an excellent job with course materials and providing resources and tools for class participants to use and find help within the R package and on additional websites. He worked to empower the student to help themselves as they develop their skills with this new tool.”
  Nick Baer, Colby-Sawyer College

“The course was very comprehensive and the instructor was incredibly clear, organized, knowledgeable, and approachable with questions. The course materials are thorough and the content and pace were challenging but very doable. This course was an excellent introduction to a variety of tasks in R and recommendations to continue building R skills in the future.”
  Isabel DoCampo, Guttmacher Institute

“The instructor was engaging and easy to follow. I liked that we were able to code along in real time and apply what we learned in the homework. The Zoom format worked really well!”
  Karen Dugosh, Public Health Management Corporation

“Dr. Miles is an outstanding teacher. He is extremely knowledgeable, well-organized, and responsive to questions. I learned more in 4 days than I have in many semester-long stats courses.” 
  Jim Cranford, University of Michigan 

“This is a very exciting and inspiring course. I now feel that I can use R in a more conscious way. All topics were thoroughly discussed, and exercises were very helpful to adapt basic R algorithms to personal tasks. The course covered common statistical approaches, so right after the course I was able to apply statistical analysis on my own data. As a non-native English speaker, I can also add that all materials were explained very clearly.” 
  Natalia Shartova, Higher School of Economics University 

“I appreciate that Andrew took the time to answer all of our questions. I think we were all on different learning curves with R, and all of the topics covered were well-placed to help everyone sprint up that curve.” 
  Valerie Schweizer-Robinson, LAPOP Lab 

“As a beginner to R, I needed a course that would help me get started and give me a conceptual overview of how R works, how to apply it, and how to continue to become efficient with the software. This course was fantastic on all counts.”
  Mary Beth Oliver, Penn State

“The course did a terrific job covering the various basics I needed to bridge the gap between my statistical knowledge and how to run those analyses (and view the results) in R. It covered the basics of R language and packages in a very applied way, providing the necessary background and R package suggestions to manipulate data in R for the purpose of running analyses and writing up results. The instructor was also personable, knowledgeable, and very clear and the course was wonderfully organized.”
  Wendy Rote, University of South Florida

“I really liked the heaps of data provided for practicing. The practice exercises during the session were also very helpful!”
  Annu Mehta, Lincoln University

“I took some time to sit down with R last spring. However, I felt like I didn’t know enough about how R thinks to be confident using it. After taking this course, I not only feel much more comfortable using R and being able to apply these basic functions for my needed purposes, but I also feel confident in my ability to expand past the scope of the course in learning new functions. I think this course accomplishes exactly what it advertises – providing a strong foundation in the R language.”
  Andrew Thompson, SUNY Albany

Seminar Information

Tuesday, June 4 –
Friday, June 7, 2024

Daily Schedule: All sessions are held live via Zoom. All times are ET (New York time).

10:30am-12:30pm (convert to your local time)

Payment Information

The fee of $995 includes all course materials.

PayPal and all major credit cards are accepted.

Our Tax ID number is 26-4576270.