Best Practices for Reproducible Data Processing and Analysis using R and Quarto

Semester

Semester 2, 2024-2025

Type of course

Methodological and Practical Courses

Date

March 11 and March 12, 2025

Location

Online - Zoom

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Duration

2 days

Maximum number of participants

15

ECTS

0.5 EC will be appointed for participation in the complete course

Staff

Sofia Pelica (VU Amsterdam) and Sarah Ashcroft-Jones, PhD (Columbia University)

Join us for a beginner-friendly workshop on reproducible data processing and analysis, where you will learn best practices for creating, documenting, and sharing code 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 does not guarantee the correctness of your results. Therefore, this workshop will also cover techniques to validate your code using practices and methods to prevent and detect errors. 

 

Learning goals

• Understanding the principles of computational reproducibility and its importance in research.

• Developing skills to improve coding readability.

• Practicing defensive coding techniques. 

• Learning to write and publish portable scripts for reproducible data processing and analysis across different systems.

 

Schedule

First Day

14.00-14.30: Importance of Reproducibility and Open Science in the Context of Data Processing and Analysis

14:30-14:40: Break

14.40-15.30: Getting Started with R and Quarto

15.30-16.00: Break

16.00-17.00: Writing Clear and Comprehensible Code

 

Second Day

14.00-14.50: Approaches to Error-Resistant Coding

14.50-15.00: Break

15.00-15.30: Publishing Reproducible and Portable Scripts

15.30-16.40: Hands-on Practice (with Break included)

16:40-17.00: Closing Remarks

 

Requirements for participation

• A computer operating system with R and RStudio installed. 

• Basic proficiency in R programming is not required.

 

If there are more PhDs interested in participating than available places, distribution will be based on seniority for this course. This means that we look at how long someone has been a KLI member.