Various teaching material

View the Project on GitHub lgatto/TeachingMaterial


This repository is an aggregator for various R, make and git/github teaching material. Most of the courses are taught at the University of Cambridge, UK, and some have been adapted and exported outside. We would also like to acknowledge contributions from Aleksandra Pawlik, Software Sustainability Institute, Raphael Gottardo, Fred Hutchinson Cancer Research Center and Karl Broman, University of Wisconsin-Madison.

Each material subdirectory has its own repository; TeachingMaterial aggregates a snapshot as a central entry point. Aggregation is done using git-subtree (see the administration page for details). The local copies linking to external repositories are prefixed with an underscore.

Unless otherwise stated, all material is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This means you are free to copy, distribute and transmit the work, adapt it to your needs as long as you cite its origin and, if you do redistribute it, do so under the same license.

See also the TeachingMaterial wiki for meta-information about the repository and general R installation material and links.

If you like this material and/or this initiative, do not hesitate to let us know by starring the repo, tweeting about it and sharing it with your colleagues.


Mass spectrometry and proteomics using R/Bioconductor

Visualising biomolecular data

A gentle introduction to git and Github

Introduction to bioinformatics and data science


Advanced R programming

R debugging and robust programming







R package development

Benchmarking, profiling and optimisation




R functional programming

R vectorisation

R debugging

R parallel

R object oriented programming

One day course on R OO programming and package development

Short S4 tutorial

R programming tutorial

R and C/C++




Best practices in bioinformatics research: open source software and reproducibility

Beginner’s statistics in R

Statistics primer

Inspection, visualisation and analysis of quantitative proteomics data

R and Bioconductor for Mass Spectrometry and Proteomics data analysis

An Introduction to Machine Learning with R


We try to only aggregate material that is openly available, generally under Creative Commons Attribution license, which gives you the right to share and adapt the material as long as you credit to original author(s). Please refer to the orignal repository for details.