Reproducible research: an approach using programming languages

Abstract

Talk on reproducible research starting from the scientific method and the data analysis workflow (import, tidy, transform, visualize, model and communicate). It presents R and Python as the foundation for reproducibility and the dynamic-document tools Jupyter, R Markdown and Quarto, explaining how they work with knitr, Jupyter and Pandoc. It shows where to use them, locally (RStudio, VS Code) and in the cloud (Posit Cloud, JupyterLab, Google Colab, Databricks Community), and reviews basic Markdown syntax and recommended references.