Background
Data analysis is a vital skill in today’s digital age, where large amounts of information are generated across all sectors. Using R for data analysis enables you to turn raw data into clear, actionable insights that support effective decision-making. While the foundational concepts suit academic research and scientific discovery, the benefits extend well beyond science.
Learning contents
This course is designed to introduce data analytics using R through a series of practical modules. It starts with the basics, covering variables, data types (including factors), essential operations, and script writing to automate your workflow. You'll work with key R data structures such as vectors, matrices, and data frames, which form the foundation for managing data effectively.
As the course progresses, you'll learn how to clean, transform, and wrangle datasets using hands-on examples. You will also explore methods for reading from and writing to common file formats like CSV and Excel, along with developing custom functions for routine analytic tasks.
In the later stages, the course covers basic statistical techniques—including regression analysis and principal component analysis (PCA)—to help extract insights from your data. Additionally, you'll gain experience in using ggplot2 to build clear visualizations.
Learning objectives
- R Fundamentals: Understand basic R concepts including variables, data types and essential operations
- Scripting and Automation: Learn to write and execute scripts in R to automate repetitive tasks and streamline workflows.
- Data Structures: Gain proficiency in working with R’s key data structures such as vectors, matrices, and data frames for effective data management.
- Data I/O: Develop skills to read data from and write data to common file formats like CSV and Excel.
- Data Wrangling: Acquire practical experience in cleaning, transforming, and wrangling datasets through hands-on examples.
- Custom Functions: Learn to build custom functions for recurring analytic tasks to improve efficiency.
- Statistical Techniques: Understand and apply basic statistical methods—such as regression analysis and principal component analysis (PCA)—to extract insights from data.
- Data Visualization: Gain experience in using ggplot2 to create clear and impactful visualizations.
Prior knowledge
- Basic Computer Skills: Participants should be comfortable using a computer for file management and simple tasks
- No Programming Experience Required: This beginner course is designed with the assumption that learners have little or no prior experience in programming or R
- Basic Data Handling Familiarity (Optional): While not essential, familiarity with basic data organization—such as using spreadsheets (e.g., CSV or Excel files)—can be beneficial
- Eagerness to Learn: A willingness to engage with hands-on exercises
Technical requirements
- Own laptop
- Connection to the Wifi
Recommended literature / materials
- R cheat sheet: https://iqss.github.io/dss-workshops/R/Rintro/base-r-cheat-sheet.pdf
- Advanced book: https://r4ds.hadley.nz
- R Coder: https://r-coder.com
- R Graph Gallery: https://r-graph-gallery.com
- Cheat sheet collection of specific packages: https://posit.co/resources/cheatsheets/?type=posit-cheatsheets/
- W3Schools.com R Tutorials: https://www.w3schools.com/r/default.asp
Assistance
Lars Harms, Stefan Neuhaus, Noemi Ruegg
