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Surviving the analysis jungle (R Markdown, osf, and GitHub)


Semester 2, 2021-2022

Type of course

Methodological and Practical Courses


May 24 and May 25, 2022




2 days

Maximum number of participants



1 EC will be appointed for participation in the complete course


Anne-Kathrin Kleine, MSc. (RUG)

Surviving the analysis jungle: Tips and tricks to organise, present, and store data and analysis scripts using R Markdown, osf, and GitHub


Practicing open science relies heavily on the ability to use open science tools. In this workshop, participants will learn how to organize their R code in R Markdown chunks. R Markdown is a file format for making dynamic documents with R. An R Markdown document is written in markdown (an easy-to-write plain text format) and contains chunks of embedded R code. On the first day, we will cover the technical details of using R Markdown to create beautiful html outputs. Additionally, participants will learn tricks and tips on how to plan their analyses ahead and organize their code to enable themselves and other to work with it (in the future). Participants will be able to implement their analyses in R Markdown and potentially enhance their code quality through file (re)organization and code refactoring. On the second day, we will briefly delve into the possibility of letting the user see outputs based on user selections (reactivity) by using shiny apps. Finally, participants will learn about the options of storing their code and analysis outputs on osf and/or GitHub. We will briefly cover ways of working on code collaboratively using GitHub.  

Learning goals

  1. Getting to know tools like R Markdown, osf, GitHub, and shiny apps that make practicing open science a bit easier
  2. Learning about proper code/file organization and refactoring
  3. Learning how to create data analysis output files (as html/ pdf) for presentation purposes
  4. Learning how and where to store code and output (for yourself, other scientists, and the interested R user)
  5. Learning where to find help (for coding and code organization) and how to ask for it.


It is expected that participants have worked with R before attending the workshop. That is, they should be able to at least run some basic descriptive analyses in R before attending the workshop. They may benefit most from the workshop if they bring their data and the analysis script they would like to work on. However, I will provide files (data and code) that may be used throughout the workshop.

The latest R ( and RStudio ( versions should be installed. If you run into issues with the installation, please look here: No prior knowledge of R Markdown or the other tools used in the workshop is required.