2 min read

Developers will now find it easier to build interactive web applications using R, with RStudio formally announcing that the release of Shiny 1.1.0 is on the horizon. This is expected to be a major release, with support for asynchronous operations and quite a few other important feature updates.

What’s new in Shiny 1.1.0

Shiny 1.1.0 brings asynchronous programming capabilities to R, with the integration of the promises package. The main aim of this is to move away from R’s single-threaded nature and increase the scalability and overall responsiveness of the web application. This is quite an important enhancement, considering a web application traditionally designed in R was quite slow and one-dimensional. Users running a long calculation or task on a web app using Shiny would bring the process to a halt for other users. This will not be the case anymore, with the introduction of asynchronous programming features.

Some of the other significant features introduced in this release include:

  • The functions extractStackTrace and formatStackTrace are deprecated and will be removed in the future versions of Shiny
  • Improved support for JavaScript, with a new function for comparing version strings called Shiny.compareVersion()
  • Improved functionality of stack traces and support for deep stack traces for efficient memory allocation
  • File drag and drop feature breaking in the presence of jQuery 3.0 has been fixed
  • Improved error handling
  • Bug fixes for significant performance improvement, and a lot more.

You can check the full changelog for Shiny 1.1.0 on Shiny’s official Github page.

Shiny has been R’s premier package for designing interactive graphics for web applications, and has been rivalling the likes of Tableau and other Business Intelligence tools. It will be interesting to see how users receive the new features introduced in 1.1.0, especially the asynchronous programming features allowing the web apps to perform faster and more efficiently.

Read Next

Introducing R, RStudio, and Shiny

When do we use R over Python?

Top 5 programming languages for crunching Big Data effectively

Data Science Enthusiast. A massive science fiction and Manchester United fan. Loves to read, write and listen to music.

LEAVE A REPLY

Please enter your comment!
Please enter your name here