ggplot2 is a powerful and flexible tool for creating high-quality, publication-ready data visualisations in R. This course provides a structured, hands-on introduction to ggplot2, focusing on designing clear, reproducible, and insightful plots for life sciences research.
The course is structured as a two-week intensive programme, combining live sessions, exercises, and independent work. Participants will learn to construct various types of visualisations, apply aesthetic mappings effectively, and enhance their plots with facets, themes, and annotations.
🚀 This course is part of the GS-LS R learning line. Completing it will strengthen your data visualisation skills and prepare you for advanced R-based analysis courses such as Differential Expression Analysis.
| Session | Topic |
|---|---|
| 1 | Welcome, Grammar of Graphics, and Data Reformatting |
| 2 | Geometries, Statistical Transformations, and Faceting |
| 3 | Themes, Colour Scales, Guides, and Annotation |
| 4 | Advanced Customisation: Patchwork, Patterns, and Plugins |
| 5 | Final Project Presentations and Peer Feedback |
The course is interactive and hands-on, with structured exercises, check-ins, and opportunities to apply ggplot2 techniques to real datasets. The course starts with lots of exercises, and throughout the course days, slowly transitions in a workshop where there is more time to work on your own figures. A final project allows participants to create a meaningful visualisation, incorporating the skills learned throughout the course.
The final assessment consists of a practical project, where participants will apply their ggplot2 skills to a dataset of their choice.
Assessment Options:
Final Presentation:
Attendence of 80% or higher and completing this project are required to receive a certificate of completion.
✅ Hands-on learning with real-world applications – Participants appreciate the interactive exercises and focus on real datasets, making the course directly relevant to their research.
✅ Step-by-step guidance – The combination of live coding, structured exercises, and self-practice helps participants develop confidence.
✅ Well-paced and engaging – The course provides a good balance of structured teaching and independent problem-solving.
✅ Supportive learning environment – Many found the instructor’s feedback and discussions valuable in improving their plots.
⚡ Challenges with complexity – Some participants noted that advanced customisation options (themes, facets, and scales) were challenging. To help, we provide extra practice exercises and templates to ease the learning process.
To sign up, check the registration page or contact me directly at l.w.dijkhuizen@uu.nl.
Need more details? Feel free to reach out! 📩