Next week I’m teaching a three‑session, hands‑on introduction to data analysis in R using the tidyverse, hosted by Instats in partnership with the American Statistical Association. We’ll meet February 24–26, from 8–11am PT each day — three focused mornings designed to give participants a clear, modern workflow from raw data to publication‑ready results.
I’ve taught variations of this material for years, and the tidyverse remains my favorite tool for exploratory data analysis. Even in a world where Python is everywhere, the tidyverse offers something distinctive: you can dive straight into data analysis without first learning a long list of prerequisites. In Python, beginners often have to learn the language itself, then Pandas, then plotting libraries before they can explore a dataset. With R — and especially with the tidyverse — people start analyzing data almost immediately, using a syntax that feels intuitive.
What we’ll cover
Across the three sessions, participants will learn how to:
- Import structured data from common research formats
- Apply tidy data principles to organize datasets for analysis and reproducibility
- Transform and summarize data with
dplyr - Create effective visualizations with
ggplot2 - Work with common data types (strings, logicals, dates, categorical variables)
- Document analyses using R Notebooks
- Structure projects for collaboration, peer review, and replication
- Build comfort with the R console and the RStudio IDE
The seminar blends short explanations with hands‑on exercises you can adapt to your own work. Time permitting, we’ll also touch on foundational base R concepts — vectors, environments, data frames — to help you understand how tidyverse tools build on core R behavior.
The kind of work we’ll do
One of my favorite exercises in the workshop is having students create a visualization of how their name has changed in popularity over time. We use the popular babynames dataset, which lets participants practice the full workflow — importing data, transforming it with dplyr, and visualizing trends with ggplot2.
The graph below is my own solution to that exercise:
Who this is for
Although Instats primarily serves researchers, this workshop is intentionally broader. If you work with data in any capacity — policy, nonprofits, journalism, tech, public health, or simply your own projects — you’ll walk away with tools you can use immediately. The tidyverse lowers the barrier to entry in a way that makes R accessible to people from many backgrounds, not just academia.
No prior experience with R is required. If you’ve heard that R is powerful but found it confusing or hard to approach, this workshop offers a clear, intuitive path in.
Registration
Full details and registration are available through Instats: Data Analysis in R Using the Tidyverse.
