R - Shiny

Themeable HTML components use Sass to generate CSS rules from Bootstrap Sass variables, functions, and/or mixins (i.e., stuff inside of theme). bs_dependencies() makes it a bit easier to create themeable components by compiling sass::sass() (input) together with Bootstrap Sass inside of a theme, and packaging up the result into an htmltools::htmlDependency(). Themable components can also be dynamically themed inside of Shiny (i.e., they may be themed in 'real-time' via bs_themer(), and more generally, update their styles in response to shiny::session's setCurrentTheme() method). Dynamically themeable components provide a "recipe" (i.e., a function) to bs_dependency_defer(), describing how to generate new CSS stylesheet(s) from a new theme. This function is called when the HTML page is first rendered, and may be invoked again with a new theme whenever shiny::session's setCurrentTheme() is called.

Audit your Shiny apps at each commit. Multiple levels of testings are offered: startup and crash tests, performance tests (load test and global code profiling), reactivity audit as well as output tests. All results are gathered in an HTML report uploaded and available to everyone on any CI/CD plaform or RStudio Connect.

Master Shiny development with our comprehensive learning path covering fundamentals, UI design, server logic, advanced concepts, and production deployment. Transform from R user to professional web app developer through hands-on tutorials and real-world projects.

Master Shiny’s reactive programming model with comprehensive coverage of reactive expressions, observers, event handling, and advanced patterns. Learn to build efficient, dynamic applications with proper reactive design.

Master enterprise-grade UI/UX design for Shiny applications using Bootstrap 5, bslib theming, and professional design systems. Learn to create accessible, responsive interfaces that meet corporate standards for biostatistics and clinical research applications.

☁️ ☁️ ☁️ Beautiful Bubbles in Shiny and RMarkdown Backgrounds - feddelegrand7/bubblyr

This article shows how to use battery R framework to create architecture for big shiny applications

Build Robust R shiny Apps with Elegant and Highly Customizabel ‘Card’ Features Using card.pro R package

Invite JavaScript into your Data Science workflow.

Take your R/Shiny apps to the next level with JavaScript. Learn best practices for typing, documentation, and integrating React with Rhino.

1 Using R in the back-end .. but not with R-Shiny LIBData Portal

Shiny is a package that makes it easy to create interactive web apps using R and Python.

Shiny is an R package that enables building interactive web applications that can execute R code on the backend.

Shiny is a package that makes it easy to create interactive web apps using R and Python.

Purposefully simple helper functions, tools, and a framework for creating reusable applications fit for production in clinical systems.

Landing page for the ShinyUiEditor: A drag and drop interface for building Shiny apps.

Keycloak Authentication for R/Shiny.

Find out how the server-side of a Shiny application works by exploring the Shiny source code and understanding WebSockets and HTTP requests.

The 4th Shiny Contest is now in full swing! This community event celebrates learning, sharing, and recognizing outstanding work within the Shiny developer community.

Resources for demoing local and Launcher jobs with RStudio - sol-eng/background-jobs

Shiny is a package that makes it easy to create interactive web apps using R and Python.

Some examples of running R applications on Google Kubernetes Engine

🐝 Awesome R packages that offer extended UI or server components for the R web framework Shiny - nanxstats/awesome-shiny-extensions

Standalone code for the "Outstanding Shiny UI" Book - DivadNojnarg/outstanding-shiny-ui-code

Some Shiny applications. Contribute to dreamRs/shinyapps development by creating an account on GitHub.

Shiny App for Load Balancing. Contribute to aephidayatuloh/shiny-loadbalancer development by creating an account on GitHub.

Article compares different technologies to scale R Shiny Apps, technologies used: ShinyProxy, Shiny Server Open Source & Pro and solution based Docker and Load Balancer.

shiny-loadblancer-internal.R · GitHub

Yes, Shiny apps can be fast and scalable. But only if you build them in the right way and use the proper tools.