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Activity Number: 13 - A Multi-Disciplinary View of Reproducibility
Type: Invited
Date/Time: Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
Sponsor: American Association for the Advancement of Science
Abstract #320382
Title: Creating Optimal Conditions for Reproducible Data Analysis in R with 'Fertile'
Author(s): Benjamin Baumer* and Audrey M. Bertin
Companies: Smth College and MassMutual
Keywords: reproducibility; RStudio; R Markdown; literate programming; statistical computing; R package
Abstract:

The advancement of scientific knowledge increasingly depends on ensuring that data-driven research is reproducible: that two people with the same data obtain the same results. However, while the necessity of reproducibility is clear, there are significant behavioral and technical challenges that impede its widespread implementation and no clear consensus on standards of what constitutes reproducibility in published research. We present fertile, an R package that focuses on a series of common mistakes programmers make while conducting data science projects in R, primarily through the RStudio integrated development environment. fertile operates in two modes: proactively, to prevent reproducibility mistakes from happening in the first place, and retroactively, analyzing code that is already written for potential problems. Furthermore, fertile is designed to educate users on why their mistakes are problematic and how to fix them.


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