📊 Introduction to R for Life Sciences

📝 Course Overview

R is a widely used programming language for statistical computing and data visualisation. In this course, you will develop a strong foundation in R programming, with a focus on practical, hands-on experience in data analysis.

The course is structured as a two-week intensive programme, alternating between live sessions and self-study. Participants will learn data manipulation, visualisation, and statistical analysis techniques, and apply them to real-life datasets.

🚀 This course is the first step in the GS-LS R learning line. Completing it will prepare you for more advanced R courses such as ggplot2 for publication and differential expression analysis.

🎯 Learning Objectives

Understand the basics of R syntax and data structures.
Learn how to import, clean, and manipulate datasets.
Perform basic statistical analyses.
Perform basic exploratory plots.
Gain confidence in reading and writing R scripts.

🗓️ Course Schedule

Session	Topic
1	Introduction, RStudio, Data Types & Structures
2	Data Import, Cleaning & Manipulation
3	Exploratory Data Analysis & Basic Plots
4	Programming concepts and scripting
5	Bonus topics and Q&A
6	Final Assignment & Project Presentation (Online)

The course takes place over 5 sessions, spread over two weeks. Sessions contain lectures, interactive coding challenges and Q&A time. Expect to spend most of your time on R coding during these two weeks. This long format helps in gaining solid R programming skills. Learning a language takes time; programming languages are no different.

🎓 Final Assessment

To finalise this course, participants must complete a final assignment. You can choose one of two options:

Exam-Style Assignment: A structured set of exercises covering key topics. An answer model will be provided for self-assessment.
Personal Data Project: Use your newly acquired R skills on your own research data and present your findings in an RMarkdown report. This option requires more time but is highly beneficial for applying skills directly to your research.

The final assignment must be completed to receive a course certificate.

🧰 Prerequisites

No prior programming experience is required.
Basic familiarity with data analysis.

📚 Instructional Method

Pre-Recorded Lectures: Theory will be available in video format for flexible learning.
Interactive In-Class Sessions: Live sessions focus on Q&A, coding challenges, and exercises.
Self-Study & Practice: Participants are expected to spend additional time practising exercises.
Personalised Support: Teachers will be available for guidance and feedback throughout the course.

💻 Tools and Resources

Software: R & RStudio (installation instructions provided before the course).
Course materials: R scripts and exercise datasets will be shared.
Additional resources: Cheat sheets, recommended readings, and video tutorials.

⭐ What Participants Say

✅ Hands-on & Practical – This course is structured to help you learn by doing, with plenty of exercises and interactive sessions. Many participants say they feel confident applying R to their own research by the end!
✅ Supportive Learning Environment – Whether you’re a complete beginner or need a refresher, you’ll have access to an instructor for guidance, feedback, and troubleshooting.

💡 Heads-up: This course is intensive! Some participants noted that it moves fast, especially for those new to programming. But don’t worry—we provide step-by-step guidance, extra resources, and encourage you to practise at your own pace!

🚀 How to Enrol

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! 📩