Introduction to R for Data Analysis: 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 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.

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
    • Predictions

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

“The instructor did an excellent job of providing enough introductory material to get folks curious about the topics, while still being able to leave the course feeling capable of running data analyses. Great pacing and breadth of topics.”
  Daniel Lakin, Pacific Clinics

“Andrew is an exceptional instructor. His communication abilities are excellent and his explanations are perfect. He was the absolute best part of this course.”
  Matt Hayat, Georgia State University

“The teaching was excellent, and just right for my background – someone who has experience in another package (Stata) and benefits from having an instructor who can explain the logic differences.”
  Michelle Ko, University of California, Davis

“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! He was able to provide 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

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