Book Image

Web Application Development with R Using Shiny Second Edition - Second Edition

By : Chris Beeley
Book Image

Web Application Development with R Using Shiny Second Edition - Second Edition

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 (14 chapters)
Web Application Development with R Using Shiny Second Edition
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Downloadable reports with knitr


This same function can very easily allow your users to produce custom reports in HTML, pdf, or MS Word ready to be downloaded to their machines, using the knitr package (http://yihui.name/knitr/). Knitr is a user-contributed package that allows reports to be generated dynamically from a mixture of a static report format interleaved with the output from R.

So, for example, titles and text can be fixed, but each time the report is run, different outputs will be produced within the document depending on the state of the data when the output is generated. Knitr can be used with the RMarkdown format described in Chapter 2, Building Your First Application. Here is the simple RMarkdown document within the Google Analytics application:

# Summary report
## Text summary
This report summarises data between `r strftime(input$dateRange[1], format = "%d %B %Y")` and `r strftime(input$dateRange[2], format = "%d %B %Y")`.

## Trend graph
```{r fig.width=7, fig.height=6, echo...