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Web Application Development with R Using Shiny

Web Application Development with R Using Shiny - Third Edition

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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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...

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