Advanced R
Overview
This advanced course is designed for experienced R users who want to master professional-grade tools and practices for data science. You’ll learn how to build robust, maintainable, and efficient R code that can handle complex workflows and scale to production environments. The course focuses on best practices for reproducible research, modular code architecture, and advanced programming techniques.
This course will run from 24 - 26 June 2026 in person at Belval campus, combining lectures with hands-on practical exercises.
Learning Objectives
By the end of this course, participants will be able to:
- Apply metaprogramming concepts to write flexible and dynamic R functions
- Use the {targets} package to create efficient, reproducible computational pipelines
- Create professional research documents with Quarto that integrate code, results, and narrative
- Build and package R functions for reuse and distribution
- Optimize R code for performance and maintainability
- Integrate version control with GitHub and GitLab and collaborative workflows in data science projects
- Learn and discuss option for support by Large Language Models in data science projects.
Course outline
The course is structured around four core modules:
Module 1: Computational Pipelines with targets
- Introduction to workflow automation and reproducibility
- Using functional programming for workflows
- Setting up and configuring targets projects
- Managing dependencies and handling computationally intensive tasks
- Visualizing and debugging workflows
Module 2: Document Generation with Quarto
- Creating dynamic documents combining code and narrative
- Reproducible workflows from data to publication
- Parameterized reports and batch processing
- Publication formats: HTML, PDF, presentations, and websites
- Best practices for literate programming in R
Module 3: Package Development
- R package structure and conventions
- Documentation with roxygen2
- Testing strategies for R packages (unit tests, integration tests)
- Dependency management and package versioning
- Publishing and maintaining packages for collaboration
Module 4: AI, Version Control, and CI/CD Workflows
- Introduction to Large Language Models (LLMs) in data science projects
- Using AI tools for code generation, documentation, and debugging
- Best practices and ethical considerations for AI-assisted development
- Git workflows: branching, merging, and collaborative development
- Automated testing, linting, and code quality checks
- Continuous deployment strategies
Registration
Registration is currently open.
Address
University of Luxembourg Belval campus Esch-sur-Alzette
Contact
Roland Krause (LCSB, Elixir-Luxembourg)
