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  • Book Overview & Buying Web Application Development with R Using Shiny Second Edition
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Web Application Development with R Using Shiny Second Edition

Web Application Development with R Using Shiny Second Edition

By : Chris Beeley
3.5 (10)
Buy this Book
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Web Application Development with R Using Shiny Second Edition

Web Application Development with R Using Shiny Second Edition

3.5 (10)
By: Chris Beeley
Buy this Book

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.
Table of Contents (9 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Getting Started with R and Shiny!
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1. Getting Started with R and Shiny!
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Installing R
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The R console
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Code editors and IDEs
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Learning R
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Loading data
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Data types and structures
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Base graphics and ggplot2
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Advanced tasks with dplyr and ggvis
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Installing Shiny and running the examples
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Summary
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2. Building Your First Application
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2. Building Your First Application
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Types of Shiny application
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A minimal example of a fully Shiny application
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Widget types
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The Google Analytics application
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Advanced layout features
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Summary
3
3. Building Your Own Web Pages with Shiny
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3. Building Your Own Web Pages with Shiny
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Running the applications and code
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Shiny and HTML
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Custom HTML links in Shiny
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A minimal HTML interface
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JavaScript and Shiny
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jQuery
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Exercise
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Bootstrap 3 and Shiny
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Summary
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4. Taking Control of Reactivity, Inputs, and Outputs
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4. Taking Control of Reactivity, Inputs, and Outputs
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What's new in our application?
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Downloading data from RGoogleAnalytics
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Animation
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Streamline the UI by hiding elements
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Naming tabPanel elements
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Beautiful tables with DataTable
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Reactive user interfaces
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Progress bars
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Advanced reactivity and data handling
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More advanced topics in Shiny
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Advanced graphics options
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Downloading graphics and reports
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Downloadable reports with knitr
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Downloading and uploading data
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Debugging
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Summary
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5. Advanced Applications I – Dashboards
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5. Advanced Applications I – Dashboards
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Applications in this chapter
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Version one – sidebar layout
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Adding icons to your UI
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Using shinyBS to add pop-ups and tooltips
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Adding a pop-up window to an output
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Using shinythemes
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Version two – grid layout (A)
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Version two – grid layout (B)
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Version three – navigation bar
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Version four – dashboard
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Notifications
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Info boxes
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Google Charts gauge
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Resizing the google chart
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Summary
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6. Advanced Applications II – Using JavaScript Libraries in Shiny Applications
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6. Advanced Applications II – Using JavaScript Libraries in Shiny Applications
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The htmlwidgets package
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The application framework
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Dygraphs
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Dygraphs with a prediction
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rCharts
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d3heatmap
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threejs
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Summary
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7. Sharing Your Creations
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7. Sharing Your Creations
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Sharing with the R community
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Using Git and GitHub within Rstudio
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Summary
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Index
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Index

Using Git and GitHub within Rstudio

To install Git, simply go to the URL mentioned earlier and download the .exe file for Windows, or on Ubuntu, run the following command:

sudo apt-get install git

For other flavors of Linux, check the package management system. Having installed Git, you now need to set up a new project within RStudio. A version control with Git (or SVN, a different version control system, which we will not consider here) is only possible when we use a project within RStudio.

Projects in RStudio

Using projects in RStudio is a way to organize your work. Each project has its own working directory with a separate R session, workspace, console input history, and open editor tabs (among other things). Each time a project is opened, each of these will be set to the value currently associated with the project, in effect launching a new R session, loading the data and console history since the last time the project was used (if they are selected as the default behavior or individually...

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