$\mathtt{\text{code::proof}}$: Prepare for \emph{most} weather conditions

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 $\mathtt{\text{code::proof}}$, confidence in computational algorithms.