Streamlines the creation of reproducible analytical pipelines using 'default.nix' expressions generated via the 'rix' package for reproducibility. Define derivations in 'R', 'Python' or 'Julia', chain them into a composition of pure functions and build the resulting pipeline using 'Nix' as the underlying end-to-end build tool. Functions to plot the pipeline as a directed acyclic graph are included, as well as functions to load and inspect intermediary results for interactive analysis. User experience heavily inspired by the 'targets' package.

How can we ensure long-term reproducibility across operating systems, platforms, and time? Bruno Rodrigues believes the answer lies in Nix—and a package he developed for the R ecosystem.

🇬🇧 Best practices for doing science with data Speakers Head of Statistics Department – Ministry of Research and Higher Education, Luxembourg Abstract Data scientists and researchers write a lot of code but often without the practices that make it robust and reproducible. That gap can lead to serious consequences, such as the infamous Reinhart-Rogoff Excel […]

Documentation for ryxpress

🇬🇧 Best practices for doing science with data Speakers Head of Statistics Department – Ministry of Research and Higher Education, Luxembourg Abstract Data scientists and researchers write a lot of code but often without the practices that make it robust and reproducible. That gap can lead to serious consequences, such as the infamous Reinhart-Rogoff Excel […]

Overview of programmatic ways to analyze and edit Markdown files: Markdown, R Markdown, Quarto, Hugo files, you name it.