Essential R Skills for Intermediate Users - Online Course

Course content can be roughly grouped into three broad skillsets. The first are “R skills”—skills that improve your ability to write effective R code. We will discuss how R finds files and how we can make this process less error prone, as well as methods for troubleshooting and debugging code. We will also learn how to write our own R functions and why doing so is usually a good idea.

The second type of skills are “research skills”—ways to use R to perform common research-related tasks. These include learning how to build flexible, publication-ready tables directly in R (goodbye spreadsheets!), learning the basics of data generation and simulation, and exploring how to bootstrap results from a variety of common analyses.

The third skillset consists of strategies for using large language models (LLMs). These AI tools are powerful additions to the modern R programmer’s toolkit because they can enhance coding workflows across a variety of tasks. Accordingly, LLM content will be spread throughout the course. We will discuss both when to use them and how to use them effectively for tasks such as debugging code, enhancing tables, and supporting simulation work.

This course is unapologetically hands-on. You are encouraged to write code along with the instructor, and to participate in the exercises interspersed through the seminar. By the end of the course, you can expect to log over a dozen hours of guided practice coding in R.

A primary aim of this course is to “upskill” your ability to work independently and flexibly in R. Consequently, particular emphasis will be placed on processes for tackling problems rather than ready-made solutions. We also will focus on the “nuts and bolts” of the various methods we discuss so that you will leave the course ready to tailor out-of-the-box functions to the specifics of your work.

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.

Participants are encouraged to have access to a prompt-based LLM service, such as Gemini, ChatGPT, or Claude. The course LLM content will be presented using the paid version of Gemini.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.

This course is for anyone who is already comfortable performing basic data manipulation and analysis (e.g., regression) in R. You should also be familiar with basic data handling in both base R and the tidyverse. If $, [], [[]], and %>% are all familiar to you, then you are probably good to go.

Those needing a more basic treatment are encouraged to take R for Stata Users, R for SAS Users, R for SPSS Users, or Introduction to R for Data Analysis, any of which would be good preparation for this course.

Finding files in R

• • Absolute and relative file paths
  • RStudio projects
  • The here package

Basic principles for using LLMs in R coding

Making tables

• • Standard data and results tables
  • Making custom tables
  • LLMs in the table-making workflow

Programming

• • Control structures
  • Writing functions and functional coding
  • Coding more complex functions
  • LLM supports for programming and function-writing

Debugging code

• • Two simple debugging strategies
  • Debugging code in closed environments (e.g., functions)
  • Using LLMs to debug code

Improving efficiency

• • Testing code efficiency (speed, memory use)
  • Parallel computing

Data generation

• • Generating univariate and multivariate data
  • Generating data to produce specific regression coefficients
  • Using LLMs in data generation

Simulation

• • Basic strategy
  • Example 1 (core ideas): simulating sampling distributions
  • Example 2 (adding complexity): power analysis
  • Using LLMs to improve simulations

Bootstrapping

• • Bootstrap inference for common regression models
  • Flexible bootstrapping

The fee of $995 USD includes all course materials.

PayPal and all major credit cards are accepted.

Our Tax ID number is 26-4576270.