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Computational tools for data analysis are being released daily onrepositories such as the Comprehensive R Archive Network. How we integratethese tools to solve a problem in research is increasingly complex andrequiring frequent updates. To mitigate these \emph{Kafkaesque} computationalchallenges in research, this manuscript proposes \emph{toolchain walkthrough},an opinionated documentation of a scientific workflow. As a practicalcomplement to our proof-based argument~(Gray and Marwick, arXiv, 2019) forreproducible data analysis, here we focus on the practicality of setting up areproducible research compendia, with unit tests, as a measure of\texttt{code::proof}, confidence in computational algorithms.

\texttt{code::proof}: Prepare for \emph{most} weather conditions