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  • 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)
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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

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
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
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
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
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
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
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
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
7. Sharing Your Creations
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7. Sharing Your Creations
Sharing with the R community
Using Git and GitHub within Rstudio
Summary
8
Index
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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