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Learning Shiny

Learning Shiny

By : Hernan Resnizky
2.9 (7)
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Learning Shiny

Learning Shiny

2.9 (7)
By: Hernan Resnizky

Overview of this book

Make the most of R’s dynamic capabilities and implement web applications with Shiny About This Book Present interactive data visualizations in R within the Shiny framework Construct web dashboards in a simple, intuitive, but fully flexible environment Apply your skills to create a real-world web application with this step-by-step guide Who This Book Is For If you are a data scientist who needs a platform to show your results to a broader audience in an attractive and visual way, or a web developer with no prior experience in R or Shiny, this is the book for you. What You Will Learn Comprehend many useful functions, such as lapply and apply, to process data in R Write and structure different files to create a basic dashboard Develop graphics in R using popular graphical libraries such as ggplot2 and GoogleVis Mount a dashboard on a Linux Server Integrate Shiny with non-R-native visualization, such as D3.js Design and build a web application In Detail R is nowadays one of the most used tools in data science. However, along with Shiny, it is also gaining territory in the web application world, due to its simplicity and flexibility. Shiny is a framework that enables the creation of interactive visualizations written entirely in R and can be displayed in almost any ordinary web browser. It is a package from RStudio, which is an IDE for R. From the fundamentals of R to the administration of multi-concurrent, fully customized web applications, this book explains how to achieve your desired web application in an easy and gradual way. You will start by learning about the fundamentals of R, and will move on to looking at simple and practical examples. These examples will enable you to grasp many useful tools that will assist you in solving the usual problems that can be faced when developing data visualizations. You will then walk through the integration of Shiny with R in general and view the different visualization possibilities out there. Finally, you will put your skills to the test and create your first web application! Style and approach This is a comprehensive, step-by-step guide that will allow you to learn and make full use of R and Shiny’s capabilities in a gradual way, together with clear, applied examples.
Table of Contents (13 chapters)
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1
1. Introducing R, RStudio, and Shiny
12
Index

server.R coding


After analysis and the application's design, this is definitely the most important stage of coding. A well-programmed backend is the key to performance and consequently effective visualizations. In this part, as in every backend process, the focus should be on producing the output with as little code and processing as possible. In order to achieve this, the key is to avoid repetition.

In our example, all the outputs are produced from the same dataset, which is basically the data source filtered by the corresponding input values. As it was explained in Chapter 5, Shiny in Depth – A Deep Dive into Shiny's World, there is no need to generate the same object for every visualization, as a reactive object can be used instead. In this example, a reactive object is precisely used:

#Reactive subset

data.sset <- reactive({
  input$submitter
  isolate({
    subset(data.adult, sex %in% input$gender & age >= input$minage &
    age <= input$maxage & marital.status ...

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