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Web Application Development with R Using Shiny

Web Application Development with R Using Shiny

By : Chris Beeley, Shitalkumar R. Sukhdeve
3.8 (4)
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Web Application Development with R Using Shiny

Web Application Development with R Using Shiny

3.8 (4)
By: Chris Beeley, Shitalkumar R. Sukhdeve

Overview of this book

Web Application Development with R Using Shiny helps you become familiar with the complete R Shiny package. The book starts with a quick overview of R and its fundamentals, followed by an exploration of the fundamentals of Shiny and some of the things that it can help you do. You’ll learn about the wide range of widgets and functions within Shiny and how they fit together to make an attractive and easy to use application. Once you have understood the basics, you'll move on to studying more advanced UI features, including how to style apps in detail using the Bootstrap framework or and Shiny's inbuilt layout functions. You'll learn about enhancing Shiny with JavaScript, ranging from adding simple interactivity with JavaScript right through to using JavaScript to enhance the reactivity between your app and the UI. You'll learn more advanced Shiny features of Shiny, such as uploading and downloading data and reports, as well as how to interact with tables and link reactive outputs. Lastly, you'll learn how to deploy Shiny applications over the internet, as well as and how to handle storage and data persistence within Shiny applications, including the use of relational databases. By the end of this book, you'll be ready to create responsive, interactive web applications using the complete R (v 3.4) Shiny (1.1.0) suite.
Table of Contents (11 chapters)
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Running reactive functions over time (execution scheduling)

The core of the Shiny application is the reactive engine, which tells Shiny when to execute each component. We are now familiar with the reactive flow of various components. Reactive observers have a flag that indicates whether they have been invalidated. Whenever the values of the input object change, all of the descendants in that graph are invalidated. Such invalidated observers are also called dirty or clean. Along with this, the arrows in the flow diagram that have been followed are removed.

Let's discuss an example of a single reactive source and endpoint:

server<- function(input, output) { 
output$Plot_output<- renderPlot({ 
hist(rnorm(input$User_input)) 
  }) 
} 

The flow diagram is as follows:

As soon as the input values change, all the descendants are invalidated and a flush event is triggered:

...
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