Many researchers first learn R using tutorials where every dataset is perfectly formatted and every command runs without errors. Then they try to analyze their own data—and suddenly they're dealing with missing values, inconsistent sample names, unexpected file formats, duplicated records, and cryptic error messages.

This gap between tutorials and real-world data is exactly why we developed Dealing with Messy Data in R (13–15 July).

Rather than relying on clean, artificial examples, the course uses realistic datasets that reflect the challenges encountered in biological and biomedical research. Participants will learn how to:

• import and inspect messy datasets
• identify and resolve common data quality issues
• handle missing and inconsistent values
• reshape and clean data efficiently using modern R workflows
• develop practical debugging and troubleshooting strategies
• build reproducible data-cleaning pipelines

Our goal is not simply to teach R syntax, but to help researchers become confident in approaching imperfect datasets—the reality of nearly every data analysis project.

If this sounds useful for your work, you can find the full course description here: https://www.physalia-courses.org/courses-workshops/messy-data/