  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.
