This course material is part of the "First Steps with R in Life Science" two-day course of SIB Training and is addressed to beginners wanting to become familiar with the R syntax, environment, and the most common commands to start using R to explore and interpret their data.
This course material provides an introduction to R and Rstudio (an integrated development environment for R) in an interactive manner. It contains example data-sets to practise data manipulation, graphical exploration and statistical hypothesis testing.
To follow this course, make sure you have R and Rstudio installed beforehand.
The course material is organized in parts corresponding to the 2 days of the course.
The slides provided contain both theory and hands-on practice session instructions.
The data used in the practicals can be found in the data course_datasets/ folder, and
solutions codes can be found in the solutions/ folder (NB: practise session 1 and 2 do not have associated code).
- Day 1 slides covers the most basic aspects
- first contact with the language and software
- most common data types in R
- reading and writing data files with R
- Day 2 - morning slides discusses visualization in R and how to create and customize simple, yet efficient, graphics from your data
- Day 2 - afternoon slides introduces how to perform statistical analysis in R (be warned: this is not a statistics course and only aims to show how the language deals with it)
In order to help you throughout the practicals, we encourage you to consult the following cheatsheetL
- keyboards cheatsheet to remember where the special characters are (
[]{}&^~\|/...) - base R cheatsheet a nice 2-pages cheatsheet with most of R basics
The extra datasets in this repository are taken from published work and are reused under the Creative Commons Attribution 4.0 International license (CC BY 4.0). They were modified for teaching purposes (see below).
Metabolomics dataset (Bosnjakovic2025_metabolites.xlsx)
Bosnjakovic A, Eichmann T, Stern A, Rasmussen MA, Lovric M, Zegura B (2025).
Metabolomic fingerprints of PAH exposure – identifying toxicological
biomarkers in dynamically cultured 3D cell spheroids. bioRxiv (preprint).
https://doi.org/10.1101/2025.07.10.663939
Changes: <Bosnjakovic2025_metabolites.xlsx sheets exported to tab-delimited .txt files>
Cell line dataset (Ujiie2025_Supplementary Table S3.xlsx)
Ujiie H, Sakyo T, Oya K, Sugawara Y, Ota M, Yonezawa H, Nishiya N (2025).
Machine Learning–Driven Integration of Cancer Cell Phenotypes Predicts
Cisplatin Sensitivity. Cancer Medicine.
https://doi.org/10.1002/cam4.71373 (Supplementary Table S3)
Changes: none
If you enjoyed this course or found it beneficial and want to refer to it, please cite it as :
Duchemin, W., & Burdet, F. (2025, November 12). Course material First steps with R in Life Sciences. Zenodo. https://doi.org/10.5281/zenodo.17590793