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Web Application Development with R Using Shiny Second Edition - Second Edition

By : Chris Beeley

3.5 (10)

Web Application Development with R Using Shiny Second Edition

3.5 (10)

By: Chris Beeley

Overview of this book

R is a highly flexible and powerful tool for analyzing and visualizing data. Most of the applications built using various libraries with R are desktop-based. But what if you want to go on the web? Here comes Shiny to your rescue! Shiny allows you to create interactive web applications using the excellent analytical and graphical capabilities of R. This book will guide you through basic data management and analysis with R through your first Shiny application, and then show you how to integrate Shiny applications with your own web pages. Finally, you will learn how to finely control the inputs and outputs of your application, along with using other packages to build state-of-the-art applications, including dashboards.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Getting Started with R and Shiny!

1. Getting Started with R and Shiny!

Installing R

The R console

Code editors and IDEs

Learning R

Loading data

Data types and structures

Base graphics and ggplot2

Advanced tasks with dplyr and ggvis

Installing Shiny and running the examples

Summary

2. Building Your First Application

2. Building Your First Application

Types of Shiny application

A minimal example of a fully Shiny application

Widget types

The Google Analytics application

Advanced layout features

Summary

3. Building Your Own Web Pages with Shiny

3. Building Your Own Web Pages with Shiny

Running the applications and code

Shiny and HTML

Custom HTML links in Shiny

A minimal HTML interface

JavaScript and Shiny

jQuery

Exercise

Bootstrap 3 and Shiny

Summary

4. Taking Control of Reactivity, Inputs, and Outputs

4. Taking Control of Reactivity, Inputs, and Outputs

What's new in our application?

Downloading data from RGoogleAnalytics

Animation

Streamline the UI by hiding elements

Naming tabPanel elements

Beautiful tables with DataTable

Reactive user interfaces

Progress bars

Advanced reactivity and data handling

More advanced topics in Shiny

Advanced graphics options

Downloading graphics and reports

Downloadable reports with knitr

Downloading and uploading data

Debugging

Summary

5. Advanced Applications I – Dashboards

5. Advanced Applications I – Dashboards

Applications in this chapter

Version one – sidebar layout

Adding icons to your UI

Using shinyBS to add pop-ups and tooltips

Adding a pop-up window to an output

Using shinythemes

Version two – grid layout (A)

Version two – grid layout (B)

Version three – navigation bar

Version four – dashboard

Notifications

Info boxes

Google Charts gauge

Resizing the google chart

Summary

6. Advanced Applications II – Using JavaScript Libraries in Shiny Applications

6. Advanced Applications II – Using JavaScript Libraries in Shiny Applications

The htmlwidgets package

The application framework

Dygraphs

Dygraphs with a prediction

rCharts

d3heatmap

threejs

Summary

7. Sharing Your Creations

7. Sharing Your Creations

Sharing with the R community

Using Git and GitHub within Rstudio

Summary

Index

Index

Summary

In this chapter, we have used a lot of different JavaScript libraries and R packages and have produced a diverse range of plots, many of which you will find useful in your own applications. It's universally true that the actual plotting instructions themselves are very simple (although you can highly customize the output with different arguments) as long as your data is in the right format. It's worth taking the time to understand the types of inputs the packages accept, and if you don't get the results, you want a good place to start fixing the problem by looking at the structure of the input (run str(theInput) to have R show you the structure of an input). Another potential source of problems is to ensure that Shiny passes arguments to your functions in the correct way. Keep in mind that they will often be strings that will not always function in the same way as in standard R plotting instructions (for example, in the rCharts example, when using a formula-based...

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