In this section, we will develop a forecasting and machine learning application using R Shiny. As programmers, we tend to be more intent on developing code than necessarily getting involved in developing web-based frontends and dashboards. The latter is best developed using JavaScript, Node.js, and HTML/CSS.

Most languages provide a means to expose the functionality of the underlying code to the end user through GUI-based tools. Frameworks such as Qt can be leveraged using C++, Python, and other languages to build robust frontend interfaces. And today, iOS and Android are two of the most commonly used platforms for mobile UI and app development.

The equivalent to such technologies that allow users to expose the functionality of their backend code through web-based or desktop interactive components in R is Shiny. The platform provides a rich, mature, and user friendly interface through which developers can create complex interactive web pages fully integrated with the R ecosystem.

In addition, there are several other packages that have been developed by R users across the world that extend the functionality of Shiny and provide new features that allow users to create advanced visual effects. Such packages have permitted R users to forego learning JavaScript as they have made it incredibly easy to create and launch web applications, all from within the familiar RStudio ecosystem.

In this chapter, we will look at the basics of developing R Shiny applications and also create a fully-featured web-based R a priori application. RStudio has a very close integration with the R Shiny framework and users can develop the apps directly from RStudio. In this chapter, the following topics will be discussed:

• Getting started with R Shiny applications
• Developing an R Shiny application for forecasting