intro
In shinyscholar: A Template for Creating Reproducible 'shiny' Applications

**What is shinyscholar?**

Welcome to shinyscholar a template for creating applications that are modular, meet academic standards of attribution and are reproducible outside of the application. By using shinyscholar, to create a template application, developers will be encouraged to produce applications that are maintainable and run reliably without having to learn software development best-practices from scratch. shinyscholar was forked from {wallace} v2.0.5 (CRAN, website) a modular platform for reproducible modelling of species distributions (Kass et. al 2018; Kass et al. 2022). Specifically, it harnesses the higher-level structure and core attributes of Wallace but removes its discipline-specific features, yielding a generic template for developers to make their own applications.

Shinyscholar contains four components (Select, Plot, Reproduce, Template) each of which contain one to four modules (select_query, select_async, select_user, plot_hist, plot_scatter, plot_auto, plot_semi, rep_markdown, rep_renv, rep_refPackages and template_create). Each of the modules in the Select and Plot components calls a function with the same name. The select_query module and underlying function is the most complex, containing various elements for handling errors, both in the module and in the function. The select_async module has the same functionality as select_query but the module runs in the background and the rest of the app remains usable. The other modules are very simple but included to demonstrate how multiple components and modules can be used. Unlike the other modules, plot_auto is written to run without the user pressing a button to run the module and plot_semi requires a button to be pressed but then updates automatically afterwards. The Reproduce component is used to generate an rmarkdown document that reproduces the analysis conducted in the application, download a list of packages that the application uses and download citations to those packages. The Template component can be used to produce and download a template version of an app with the same features.

Attributes of shinyscholar

Many of these attributes derive from {wallace}, but others are new to shinyscholar as described in NEWS. Apps built using shinyscholar should maintain these attributes:

accessible: lowers barriers to implementing complex modular {shiny} apps for scientific analysis by providing an intuitive graphical user interface
open: the code is free to use and modify (GPL 3.0) and can be viewed from inside the application
expandable: users can author and contribute modules that enable new methodological options
flexible: options for user uploads and downloads of results
interactive: includes an embedded zoomable {leaflet} map, sortable {DF} data tables, and visualizations of results
instructive: features guidance text that educates users about theoretical and analytical aspects of each step in the workflow
reproducible: users can download an {rmarkdown} .Rmd file that when run reproduces the analysis, and also save sessions and load them later
reliable: modules and their underlying functions are tested using {testthat} and {shinytest2}

Contact us

Please email us with any other questions.

Acknowledgments

shinyscholar was developed as part of a project to develop digital tools for modelling infectious diseases funded by Wellcome at the University of Leicester. The version of Wallace that shinyscholar was derived from was funded by the Global Biodiversity Information Facility, National Science Foundation and NASA.

References

Kass J. M., Vilela B., Aiello-Lammens M. E., Muscarella R., Merow C., Anderson R. P. (2018). Wallace: A flexible platform for reproducible modeling of species niches and distributions built for community expansion. Methods in Ecology and Evolution, 9(4): 1151-1156. DOI: 10.1111/2041-210X.12945
Kass, J.M., Pinilla-Buitrago, G.E, Paz, A., Johnson, B.A., Grisales-Betancur, V., Meenan, S.I., Attali, D., Broennimann, O., Galante, P.J., Maitner, B.S., Owens, H.L., Varela, S., Aiello-Lammens, M.E., Merow, C., Blair, M.E., Anderson R.P. (2022). wallace 2: a shiny app for modeling species niches and distributions redesigned to facilitate expansion via module contributions. Ecography, 2023(3): e06547. DOI: 10.1111/ecog.06547.