Computational tools for data analysis are being released daily on repositories such as the Comprehensive R Archive Network. How we integrate these tools to solve a problem in research is increasingly complex and requiring frequent updates. To mitigate these \emph{Kafkaesque} computational challenges in research, this manuscript proposes \emph{toolchain walkthrough}, an opinionated documentation of a scientific workflow. As a practical complement to our proof-based argument~(Gray and Marwick, arXiv, 2019) for reproducible data analysis, here we focus on the practicality of setting up a reproducible research compendia, with unit tests, as a measure of \texttt{code::proof}, confidence in computational algorithms.
@article{arxiv.1910.06964,
title = {\texttt{code::proof}: Prepare for \emph{most} weather conditions},
author = {Charles T. Gray},
journal= {arXiv preprint arXiv:1910.06964},
year = {2019}
}
Comments
This manuscript was presented by invitation at The Research School on Statistics and Data Science 2019 (RSSDS2019) [https://sites.google.com/view/rssds2019/home] and will be published with the workshop proceedings in Springer Communications in Computer and Information Science