To foster international participation, this course will be held online

Modern research in ecology and biology increasingly relies on computational workflows that must be transparent, reproducible, and reliable. This four-day workshop supports scientists in developing robust R programming practices, covering the full lifecycle of an analysis—from project organisation and version control to data manipulation, reporting, and code review.

Participants will learn how to structure analyses for collaboration and long-term maintenance, apply established principles of reproducible research, and critically evaluate both their own code and code generated with the help of AI tools. The course combines practical exercises with best-practice guidelines tailored to real-world research workflows.

• Working knowledge of R, including data frames, vectors, basic functions, and plotting

• Familiarity with RStudio or a similar development environment

• Experience working within an R project structure

By the end of the workshop, you will be able to:

- Write clean, readable, and reproducible R code
-  Build efficient, well-structured projects and functions
- Leverage AI tools like ChatGPT and Copilot safely
- Create advanced static and interactive visualizations
- Produce reproducible reports with Quarto
- Handle large datasets efficiently and connect to SQL
- Collaborate and share code seamlessly with GitHub

Day 1 – Programming Foundations and Project-Based Workflows - 1:00 PM – 7:00 PM Berlin time

• Data structures, types, failure modes

• Control flow and conditional logic

• Writing reusable functions

• Defensive programming and error handling

• Project organisation and reproducible data import

• Data cleaning and naming conventions

Day 2 – Managing Complexity: Data Manipulation, Iteration, and Debugging -  1:00 PM – 7:00 PM Berlin time

• Advanced tidyverse data manipulation

• Functional programming with purrr

• Principles of reproducibility in data analysis

Day 3 – Reproducible Reporting, Visualisation, and Collaboration -  1:00 PM – 7:00 PM Berlin time

• GitHub workflows

• Dependency management with renv

• Reproducible reporting with Quarto

• Data visualization with ggplot2

Day 4 – Code Quality, Performance, and Professional Practice -  1:00 PM – 7:00 PM Berlin time

• Code review practices

• Efficient programming with data.table and memory management

• Responsible use of AI tools in R

"Yes, writing functions/scripts and improving my ggplot skills were the most useful things."

"This was a fantastic course! I think my R skills were less advanced than some of the other participants, but since the course layout was very flexible I was able to work on what was useful for me and take extra time when I needed it."

1 - Missing Data in R  - ONLINE, 22-24 April

2 - Reproducibility with R - ONLINE, 8-11 June

3 - Introduction to R Shiny - ONLINE, 9-10 June

4 - Developing R/Bioconductor packages - ONLINE, 6-10 July

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.