Reproducible Data Processing and Analysis using R
Workshop in Reproducible Coding Practices in R
A beginner-friendly workshop on reproducible data processing and analysis, where you’ll learn best practices for creating, documenting, and sharing your scripts that ensure computational reproducibility and adherence to open science principles. The primary goal of reproducible data processing and analysis is to enable others to independently replicate your results by using your code and data. However, reproducibility alone doesn’t guarantee the correctness of your results.
Therefore, this workshop will also cover techniques to validate your code using practices and testing methods to prevent and detect errors. Also, you’ll learn how to track the provenance of any result, such as figures or tables, to document the process by which they were obtained.
Learning Goals
- Understand the principles of computational reproducibility and their importance in research.
- Improve coding readability and organization.
- Practice defensive coding techniques to prevent and detect errors.
- Learn to write and publish portable scripts for reproducible analysis across different systems.
Materials
All materials including slides and practice scripts can be found here, please adapt and credit to Sarah Ashcroft-Jones and Sofia Pelica accordingly.