Advancing reproducible research by publishing R markdown notebooks as interactive sandboxes using the learnr package

Tso, Chak Hau Michael ORCID: https://orcid.org/0000-0002-2415-0826; Hollaway, Michael ORCID: https://orcid.org/0000-0003-0386-2696; Killick, Rebecca; Henrys, Peter ORCID: https://orcid.org/0000-0003-4758-1482; Monteith, Don ORCID: https://orcid.org/0000-0003-3219-1772; Watkins, John ORCID: https://orcid.org/0000-0002-3518-8918; Blair, Gordon Shaw ORCID: https://orcid.org/0000-0001-6212-1906. 2022 Advancing reproducible research by publishing R markdown notebooks as interactive sandboxes using the learnr package. The R Journal, 14 (1). 255-263. 10.32614/RJ-2022-021

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Abstract/Summary

Various R packages and best practices have played a pivotal role to promote the Findability, Accessibility, Interoperability, and Reuse (FAIR) principles of open science. For example, (1) well-documented R scripts and notebooks with rich narratives are deposited at a trusted data centre, (2) R Markdown interactive notebooks can be run on-demand as a web service, and (3) R Shiny web apps provide nice user interfaces to explore research outputs. However, notebooks require users to go through the entire analysis, while Shiny apps do not expose the underlying code and require extra work for UI design. We propose using the learnr package to expose certain code chunks in R Markdown so that users can readily experiment with them in guided, editable, isolated, executable, and resettable code sandboxes. Our approach does not replace the existing use of notebooks and Shiny apps, but it adds another level of abstraction between them to promote reproducible science.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.32614/RJ-2022-021
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-)
ISSN:	2073-4859
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Related URLs:	Dataset Dataset
Date made live:	05 Dec 2023 13:59 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536361

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