R Introduction
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.
Learning outcomes
- 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
Approach
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.
Event intended for
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.
Required skills
If you are intending to follow the Spatial metabolomics data analysis course and you have no experience with R, this course is a prerequisite.
Course materials
Software demonstrated
- R
- Rstudio
- various R packages
Extra information
This course is part of multiple learning paths