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

TL;DR

This paper introduces exttt{code::proof}, a practical workflow for creating reproducible research compendia with unit tests, aimed at simplifying complex data analysis processes and ensuring confidence in computational results.

Contribution

It presents a toolchain walkthrough for setting up reproducible research environments with unit tests, complementing proof-based approaches for reliable data analysis.

Findings

Provides a practical method for reproducible research setup

Enhances confidence in computational algorithms through unit testing

Addresses complex integration of data analysis tools

Abstract

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.