R Introduction
R Introduction
3 September 2026, 9h00-12h00, 13h00- 16h00
7 September 2026, 9h00- 12h30
10 September 2026, 9h00- 12h30
14 September 2026, 9h00-12h00, 13h00- 16h00
General context
Data processing, visualization, and statistical basics are essential for anyone working with data. This beginner-friendly training introduces you to the R software environment, a powerful open-source tool for data analysis and visualization. Participants will learn how to manage and visualize data, apply basic statistical techniques in R such as t-tests, ANOVA, and non-parametric counterparts. By the end of the training, you will be able to generate publication-ready plots and perform simple statistical comparisons on your data. This course lays the groundwork for following more advanced statistical modeling and data analysis training sessions.
- Recognize the purpose and basic functionalities of R and RStudio to use the software effectively
- Import, format, and export datasets in R to prepare data for analysis
- Create a range of visualizations (e.g., bar charts, boxplots, violin plots, scatter plots, PCA plots, heat maps) to explore and communicate data insights
- Apply basic statistical tests in R (e.g., t-tests, Wilcoxon tests, one-way ANOVA, Kruskal-Wallis tests, correlations, survival analysis) to analyze data appropriately
- Write and execute R scripts to document and reproduce data analysis workflows
This course combines e-learning with online sessions
The course will consist of:
- A theoretical e-learning course to explain the statistics behind the analysis in R (not mandatory but recommended for those with no statistical background)
- e-learning, where you will watch parts of the theory upfront
- Demo and exercise sessions on YouTube, where your questions will be answered, and where you can apply the theory to real-life example data
Since part of the course is done via e-learning, you have to consider that you will have to spend some time on this course outside of class.
People with no experience in R who are planning to follow a training that requires some R background, such as single-cell RNA-seq, linear mixed models, computational cytometry, spatial omics analyses, and bulk RNA-seq.
- R
- Rstudio
- various R packages
This course is part of multiple learning paths
Trainers
Janick Mathys
Janick tries to help VIB scientists analyze their data by offering bioinformatics training and support. Next to organizing trainings, creating e-learning courses and teaching statistics, R, Python, Linux, HPC, bulk and single cell RNA-Seq analysis, she consults scientists and develops pipelines for omics analyses. Before joining VIB, she worked as a post-doc at KULeuven, doing research on transcriptomics and transcription regulation. She coordinated the Master of Bioinformatics program of KULeuven and taught the course on Biological Databases.