R4R: Reproducibility for R

Abstract

Creating a reproducible environment for data analysis pipelines is challenging, due to the wide range of dependencies involved—from data inputs and external tools to system libraries and R packages. Although various tools exist to simplify the process, they often focus exclusively on R package dependencies and omit the system ones, rely on user-supplied metadata, or create an unnecessarily large environment. We present r4r, a tool that automatically traces all dependencies in a pipeline using system call interception. Based on these traces, r4r generates a Docker image containing precisely the dependencies needed for reproducible execution. We demonstrate its effectiveness on a collection of R Markdown notebooks from Kaggle, illustrating how r4r helps ensure fully reproducible workflows.

Pierre Donat-Bouillud

Researcher

My research interests including programming languages, fuzzing and testing.