Basic statistics in R
Basic statistics in R
15 January 2026, 9h00-12h00, 13h00- 16h00
22 January 2026, 9h00- 12h00
29 January 2026, 9h00- 12h00
05 February 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
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.