Tooling for Reproducible Research: Considerations for the Past and Future of Data Analysis

The concept of reproducible research has evolved significantly over the past 30 years, with the idea growing in popularity, awareness, and acceptance. Upon its introduction to the statistical and broader scientific community, computational reproducibility was proposed as an essential concept for communicating the process of computational research and for being able to understand what exactly was done to produce a result. However, in the early stages, computational reproducibility faced at least one significant challenge, which was the lack of tools to make it easier for people to implement reproducible workflows. Fritz Leisch made major contributions to this area with his development of Sweave for the R programming language and his general promotion of software tools for reproducibility. We consider these contributions in the context of the history of reproducible research and consider what the implications are for the future of data analysis.