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code::proof: Prepare for Most Weather Conditions

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Statistics and Data Science (RSSDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1150))

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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 Kafkaesque computational challenges in research, this manuscript proposes 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 reproducible research compendia, with unit tests, as a measure of code::proof, confidence in computational algorithms.

Thank you to Ben Marwick, Hien Nguyen, Emily Kothe, James Goldie, Mathew Ling, J.D. Long, Kate Smith-Miles, Greg Wilson, Kerrie Mengersen, Jacinta Holloway, Alex Hayes, Noam Ross, Rowland Mosbergen, Luke Prendergast, Dale Maschette, Elio Campitelli, Thomas Lumley, and Daniel S. Katz for advising on particular aspects of this manuscript.

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Notes

  1. 1.

    Stack Overflow (https://stackoverflow.com/) is forum for asking tightly scoped programming questions.

  2. 2.

    Visit the discussion on metaresearch and RSEs on the research compendium associated with this manuscript as an example of why this paper, and its companion [8], have so many acknowledgements. Canonical literature is not yet established in the field of RSE, and thus leaders of RSE projects, such as Alex Hayes’ maintenance of the broom:: [23]. This has propelled Hayes rapidly to the level of expert, by virtue of the pioneering collaborative structure of the package, where hundreds of statistical modellers contribute integrated code.

  3. 3.

    As in the companion manuscript [8], we focus on R packages, but the reader is invited to consider these as examples rather than definitive guidance. The same arguments hold for other languages, such as Python, and associated tools.

  4. 4.

    As opposed to ofttimes unattainable or impractical best practices [33] in scientific computing.

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Authors and Affiliations

  1. La Trobe University, Melbourne, Australia

    Charles T. Gray

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  1. Charles T. Gray
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Correspondence to Charles T. Gray .

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  1. La Trobe University, Bundoora, VIC, Australia

    Hien Nguyen

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Gray, C.T. (2019). code::proof: Prepare for Most Weather Conditions. In: Nguyen, H. (eds) Statistics and Data Science. RSSDS 2019. Communications in Computer and Information Science, vol 1150. Springer, Singapore. https://doi.org/10.1007/978-981-15-1960-4_2

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  • DOI: https://doi.org/10.1007/978-981-15-1960-4_2

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