Statistics With R: A Short Course

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 and assigned as “take-home” exercises after each class session. 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.

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 
• Essential R skills
• Understanding R’s functions and help files
  • Writing understandable R code

Day 2: Exploring and analyzing data in R

• Exploring data  
  • Descriptive statistics  
  • Exploratory data plots  
• A few bivariate techniques  
  • Classic statistical tests 
  • Bivariate plots  
• Linear models  
  • Detecting and correcting problems  
  • Prediction

Day 3: Practical R skills

• Generalized linear models
• Visualizing model results 
• Programming  
  • Control structures (if/else statements, loops)  
  • Writing functions and functional coding 
• Getting results out of R

“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 instructor, Dr. Andrew Miles, was excellent. He provided great hands-on exercises and answered questions clearly.”
  Alexander Leung, University of Calgary

“The pacing was perfect! I liked how Dr. Miles introduced the basics of coding in R first and gradually built on previous days’ content to do more complex operations.”
  Aubrey Dueweke, East Tennessee State University

“This course was a good intro to R. The pacing and content coverage was just right.”
  Jim Killarney, Colby-Sawyer College

“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 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