Book Image

Web Application Development with R Using Shiny - Third Edition

By : Chris Beeley, Shitalkumar R. Sukhdeve

Book Image

Web Application Development with R Using Shiny - Third Edition

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Beginning R and Shiny

Beginning R and Shiny

Code editors and IDEs

Data types and structures

Base graphics and ggplot2

Introduction to the tidyverse

Installing Shiny and running the examples

Shiny First Steps

Shiny First Steps

Types of Shiny application

A minimal example of a full Shiny application

The Gapminder application

Advanced layout features

Integrating Shiny with HTML

Integrating Shiny with HTML

Running the applications and code

Custom HTML links in Shiny

A minimal HTML interface

Including a Shiny app on a web page

Bootstrap 3 and Shiny

Mastering Shiny's UI Functions

Mastering Shiny's UI Functions

Shiny's layout functions

Streamlining the UI by hiding elements

Beautiful tables with DataTable

Reactive user interfaces

Progress bar with shinycssloaders

Alternative Shiny designs

Easy JavaScript and Custom JavaScript Functions

Easy JavaScript and Custom JavaScript Functions

JavaScript and Shiny

Responding to events in JavaScript

Dashboards

Applications in this chapter

Sidebar application with extra styling

Using the grid layout

Power Shiny

Reading client information and GET requests in Shiny

Custom interfaces from GET strings

Downloading graphics and reports

Downloadable reports with knitr

Downloading and uploading data

Interactive plots

Interactive tables

Linking interactive widgets

Adding a password

Code Patterns in Shiny Applications

Code Patterns in Shiny Applications

Reactivity in RShiny

Controlling specific input with the isolate() function

Running reactive functions over time (execution scheduling)

Event-handling using observeEvent and eventReactive

Functions and modules

Handling errors (including validate() and req())

Profiling R code

Debounce and throttle

Persistent Storage and Sharing Shiny Applications

Persistent Storage and Sharing Shiny Applications

Sharing over GitHub

Sharing applications using Git

Persistent data storage

Database using Dplyr, DBI, and POOL

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

5 star

0

4 star

0

3 star

0

2 star

0

1 star

0

Persistent data storage

In data science product development, one of the most important steps is to bring data from various sources and keep it on storage systems. Mostly, data-storage management for data science projects is done with a data warehouse. Nowadays, various technologies have been developed to store and process various types of data, which can be structured, semi-structured, or unstructured. Using Hadoop, HDFS, Hive, MongoDB, SQLite, or MySQL-like tools and technologies are coupled up to develop an ecosystem to make for easy availability and fast processing of data.

In normal software, the data sources are usually RDBMS and meant to deal with online transactional requirements. But in data science projects, the scenarios are quite different. Here, generally historical data is used to present graphs or generate reports. And since Shiny is also considered a tool to present...