Statistics With R
A 4-Day Remote Seminar Taught by Andrew Miles, Ph.D.
To see a sample of the course materials, click here.
This seminar is currently sold out. Email email@example.com to be added to the waitlist.
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 July 21, we are offering this seminar as a 4-day synchronous*, remote workshop. Each day will consist of a 3-hour, live morning lecture held via the free video-conferencing software Zoom. 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. Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on one’s own that afternoon. A final session will be held each evening as an “office hour”, where participants can review the exercise results with the instructor and ask any questions.
*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, meaning that you will get all of the class discussion and exercise solutions even if you cannot participate synchronously.
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 two days. By the end of the course, participants can expect to log more than a dozen hours of guided practice coding in R.
This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Prior to each session, participants will receive an email with the meeting code you must use to join.
To participate in the hands-on exercises, you are strongly encouraged to bring a laptop 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 Participate?
This course is for anyone who wants to learn R. No prior knowledge of R is assumed. However, participants should 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
• 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
• Descriptive Statistics
• Exploratory Data Plots
A Few Bivariate Techniques
• Classic Statistical Tests (e.g., t-tests, chi-square tests)
• Bivariate Plots
Day 3: Analyzing Data in R
• Detecting and Correcting Problems
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
“This is one of the best introductory programming courses I’ve taken. The instruction was clear and well-organized. The instructor was great at teaching and providing super clear directions/explanations on what can be a complicated program to understand. I’ve found R difficult to grasp before this course and now feel I have a foundational understanding of R programming. The instructor is great at assisting students with questions and incorporating in-class exercises to break up the lecture and apply the skills you’ve learned or see what’s still tripping you up.”
Larisa Burke, University of Illinois at Chicago
“Extremely helpful course for people who have no experience in using R. The course is well-paced and covers a wide range of functions and topics. I gained a lot of confidence through exercises in the course and feel more ready to take on projects.”
Silvia Li, McMaster University
“I am competent with statistics but have had only a tangential experience with R. This course was a great introduction to the program and gave me a foundation on which to build and begin increasing my productivity.”
Stanley Gehrt, Ohio State University
“Andrew was very helpful and knowledgeable. He was patient and very good at answering questions. I had no knowledge of R prior to taking this course, and now I am confident that I can use it for my course and research (of course, with more practice at home). The textbook was also excellent.”
Aki Roberts, University of Wisconsin, Milwaukee
“This is a great course for learning the basics of R. I took this course after completing a few doctorate-level epi & stats courses. This helped to put many things into perspective. It will also help with my research and dissertation.”
Daryle M. Blackstock, City University of New York
“This course has given me the confidence to start using R. I have taken several R courses online but because of competing priorities in the office, I couldn’t dedicate enough time to them or couldn’t even understand them. It was great to have a classroom setting with individual attention. The exercises really help to reinforce the knowledge.”
Brad Wohler, Florida Cancer Data
“I had been using R but found the syntax very frustrating and the existing documentation very unhelpful. This was a two-day “safari” through a slew of useful tasks, guided by an expert user. I feel infinitely more comfortable with R now!”
Rita McGill, University of Chicago
“A good basic course in R. The instructor was super helpful and really good at explaining complex ideas so they seem simple.”
Hlin Kristbergsdottir, Reykjavík University
“Andrew was an incredible teacher, very detail-oriented and knowledgeable. He provided exercises throughout the workshop to allow us to practice and truly understand what we were learning. He is incredibly approachable as well. 10/10 would recommend and do again.”
David Bodenstein, University of Toronto