R + Statistics Workshop Series
For research staff and consortium members in the ABCD study
A four-part workshop series that teaches programming in R and statistics using longitudinal study data. We start from the very beginning — no prior programming experience assumed — and build toward running and interpreting your own analyses on ABCD-style data.
Sessions are live on Zoom, noon–2pm PT. All registered participants receive the Zoom link by email.
Schedule
| Module | Date | Topic | Assumes |
|---|---|---|---|
| M0 | self-paced | Get your computer set up | Nothing |
| M1 | Thu Sep 10, 2026 | Foundations of R programming | Never used R |
| M2 | Thu Sep 24, 2026 | Data exploration, visualization & descriptive statistics | Module 1 |
| M3 | Thu Oct 29, 2026 | Inferential statistics | Module 2 |
| M4 | Thu Nov 12, 2026 | Applied projects | Module 3 required |
Start here
Module 0 · Setup
self-paced
Install R and RStudio, or use Posit Cloud if your laptop is locked down. Set up a project folder, install the packages we use, and run a short check to confirm everything works.
Module 1 · Foundations of R
Sep 10, 2026
Reading data, the shape of R code, the pipe, the core data-wrangling verbs, handling missing values, and your first plots — all on a synthetic ABCD-style dataset.
How the series fits together
You are welcome to attend any module you like — they are designed to stand on their own where possible. That said, they do build on each other, and the one hard rule is that Module 3 is a prerequisite for Module 4, since Module 4 is a hands-on project session that assumes you can already choose and run a statistical test.
If you have never opened R before, do Module 0 first. It is short, self-paced, and it is the difference between spending the first half hour of Module 1 fighting an installer or actually writing code.
Certificates
After the series, we award a certificate backed by the ABCD Professional Development Workgroup and ABCD-ReproNim indicating which modules you completed. These are a useful addition to a CV and strengthen applications for graduate school or roles that expect demonstrated statistical programming skill.
What we teach with
We use tidyverse style R throughout — the |> pipe, and packages like dplyr, ggplot2 and readr — because it is the most readable way in for newcomers and the style most modern R code you will meet is written in. We introduce just enough base R notation along the way for you to read code you find elsewhere.
All examples run on synthetic datasets shaped like an ABCD longitudinal release: one row per participant per visit, with realistic missing data. They contain no real participant data.
There are two, because a first hour in R should be spent learning R rather than decoding variable names:
- Module 1 uses a small CSV with three visits and plain English column names.
- Modules 2, 3 and 4 use a fuller dataset — 500 participants across five annual visits, with real ABCD variable names, coded factor levels and structured missing data. The dataset reference page explains what is in it.
Questions?
Contact Andrew Moore (ajm9311@ou.edu) or Billu Das (biplabendu.das@gmail.com).