R Shiny demonstrator application for advancing reproducible research

https://doi.org/10.5285/df57b002-2a42-4a7d-854f-870dd867618c

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This model code provides an example to demonstrate a new application of the 'learnr' R package to help authors to make elements of their research analysis more readily reproducible to users. It turns a R Markdown document to guided, editable, isolated, executable, and resettable code sandboxes where users can readily experiment with altering the codes exposed

Publication date: 2022-03-24

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The model code depends on the 'learnr' R package.

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This application is available under the terms of the Open Government Licence

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Tso, C.-.M. (2022). R Shiny demonstrator application for advancing reproducible research. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/df57b002-2a42-4a7d-854f-870dd867618c

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Citations

Yang, J., Lin, L., & Chu, H. (2021). BayesSenMC: an R package for Bayesian Sensitivity Analysis of Misclassification. In The R Journal (Vol. 13, Issue 2, p. 123). The R Foundation https://doi.org/10.32614/RJ-2021-097

Tso, C.-H. M., Monteith, D., Scott, T., Watson, H., Dodd, B., Pereira, M. G., Henrys, P., Hollaway, M., Rennie, S., Lowther, A., Watkins, J., Killick, R., & Blair, G. (2022). The evolving role of weather types on rainfall chemistry under large reductions in pollutant emissions. In Environmental Pollution (Vol. 299, p. 118905) https://doi.org/10.1016/j.envpol.2022.118905