shiny.semantic lets you create a beautiful, non-standard UI for Shiny applications. It has a universal input binding method for your custom user interfaces which enables you to create various types of inputs.

CRAN
shiny.semantic demo

shiny.info helps display simple diagnostic information for developers that show up in the corner of a Shiny app. It makes debugging much easier and more intuitive.

CRAN
shiny.info demo

semantic.dashboard is a package for quickly creating beautiful and customizable dashboards. Designed to provide as much compatibility with shiny.dashboard syntax as possible, which makes the programmer’s life much easier. Dashboards allow you to easily structure your app, making it more user-friendly.

CRAN
semantic.dashboard demo

shiny.router helps with introducing URL routing to the Shiny app. Thanks to that users can navigate directly to the subpage of the dashboard, or you can fetch the parameters straight from the URL address.

CRAN
shiny.router demo

shiny.i18n can make the process of implementing translations of your app to any language a breeze. It offers intuitive syntax and flexible format of the translation file that makes work easier for both programmers and linguists.

CRAN
shiny.i18n demo

Shiny.worker allows you to delegate heavy computation tasks to a separate process, such that it does not freeze your Shiny app.

CRAN
shiny.worker demo

This R package enables using React in Shiny apps and is used by the shiny.fluent package. It contains R and JS code which is independent of the React library (e.g. Fluent UI) that is being wrapped.

CRAN
Fluent UI demo

As Fluent UI is built in React, shiny.fluent is based on another package called shiny.react, which allows for using React libraries in Shiny. shiny.fluent gives your apps a beautiful and professional look, a rich set of easy-to-use components in Shiny, and the fast speed of development that Shiny is famous for.

Github
Fluent UI demo

This is a package for scalable and reproducible data validation. It provides functions for validating datasets in %>% pipelines (validate_if, validate_cols, and validate_rows), predicate functions from assertr package (in_set, within_bounds, etc.), and functions for creating user-friendly reports that you can send to email, store in logs folder, or generate automatically with RStudio Connect.

CRAN
data.validator demo