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Learning Shiny

You're reading from   Learning Shiny Make the most of R's dynamic capabilities and implement web applications with Shiny

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781785280900
Length 246 pages
Edition 1st Edition
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Authors (2):
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Hernan Resnizky Hernan Resnizky
Author Profile Icon Hernan Resnizky
Hernan Resnizky
Hernan Resnizky Hernan Resnizky
Author Profile Icon Hernan Resnizky
Hernan Resnizky
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introducing R, RStudio, and Shiny FREE CHAPTER 2. First Steps towards Programming in R 3. An Introduction to Data Processing in R 4. Shiny Structure – Reactivity Concepts 5. Shiny in Depth – A Deep Dive into Shiny's World 6. Using R's Visualization Alternatives in Shiny 7. Advanced Functions in Shiny 8. Shiny and HTML/JavaScript 9. Interactive Graphics in Shiny 10. Sharing Applications 11. From White Paper to a Full Application Index

ggplot2 – first steps


ggplot2 (an acronym for Grammar of Graphics plot) is the most popular graphical package in R. This relies mainly on the fact that almost anything can be drawn with it. This huge flexibility, however, implies more complexity while drawing.

The underlying concept of ggplot2 is an empty canvas. Instead of specifying the type of plot and the data to be visualized, the ggplot2 functions expect vectors that denote positions, widths, sizes, and so on. From its conceptual point of view, this is very similar to an HTML document; this is an empty space that is filled with different objects to which different characteristics are specified. The following example is the equivalent of plot(iris$Sepal.Length):

ggplot.graph <- ggplot(data=iris)
ggplot.graph <- ggplot.graph + geom_point(aes(1:150,Sepal.Length))

plot(ggplot.graph)

This example is a typical ggplot code. As the reader might have already realized, its construction differs significantly from the other packages seen...

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