Statistics With R

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. Time permitting, we can also discuss other topics of interest to course participants. 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 August 29, 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. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time

*We understand that scheduling is difficult during this unpredictable time. 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.

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 participants understand the fundamentals of how R “thinks” so that they 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, participants will be well-equipped to tailor R to the sort of work they do.

This course is thoroughly hands-on. Participants are encouraged to write code along with the instructor, and to participate in the carefully-designed exercises that will be interspersed throughout the seminar. By the end of the course, participants 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. Participants 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
  • Working with Data (merging, sorting)

Understanding R’s Functions and Help Files
Writing Good R Code (nesting vs. tidyverse pipes)

Day 2: Exploring Data in R
Exploring Data

  • Descriptive Statistics
  • Exploratory Data Plots

Recoding Data
A Few Bivariate Techniques

  • Classic Statistical Tests (e.g., t-tests, chi-square tests)
  • Bivariate Plots

Day 3: Analyzing Data in R
Linear Models

  • Detecting and Correcting Problems
  • Predictions

Generalized Linear Models
Visualizing Model Results

Day 4: Making R Work for You

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

Sharing Your Work

  • Getting Results Out of R
  • From Start to Finish with R Code

Reviews of Statistics with R

“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

“I found the instructor to be knowledgeable, approachable, and engaging. He struck a good balance between overview and detail. He has an excellent command of the content and conveys it in a conversational presentation style. I also appreciated the flexibility to participate asynchronously to accommodate my clinical schedule. I am still working through the videos and content even though the live class ended a couple weeks ago.”
     Jeremy Davis, UT Health San Antonio

“I enjoyed many things about this seminar: the flexibility to join course live or watch afterward via recordings (I needed to be able to do both), the comprehensive course notes, and the ability to program in parallel with Andrew as he worked through course modules.”
     Tara McAuley, University of Waterloo

“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 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

“The pace was perfect for a 3-day intro. It’s  a good foundation for continued learning.”
     Greg Petroski, University of Missouri School of Medicine


Seminar information

Monday, August 29, 2022 –
Thursday, September 1, 2022

Each day will follow this schedule:

10:30am-12:30pm ET (New York time): Live session via Zoom

1:30pm-3:00pm ET: Live session via Zoom

Payment Information

The fee of $895 includes all course materials.

PayPal and all major credit cards are accepted.

Our Tax ID number is 26-4576270.