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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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Author (1):
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Hernan Resnizky Hernan Resnizky
Author Profile Icon Hernan Resnizky
Hernan Resnizky
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Table of Contents (19) Chapters Close

Learning Shiny
Credits
About the Author
Acknowledgements
About the Reviewers
www.PacktPub.com
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

Pre-application processing


This first coding stage must include all the processes that are completely independent from the application. Although they can be automatically scheduled eventually (for example, if the data source changes over time and has to be refreshed), we can think of processes that need to be done just once whenever the data source changes.

In our example, we will include the elimination of variables and the recoding. After this process, the processed data sources have to be saved, of course. In the following piece of code, we will load the dataset in the same way as we did before and eliminate the corresponding columns:

#Retrieve Data

data.adult <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", header = F)

names(data.adult) <- c("age", "workclass", "fnlwgt", "education", "education.num", "marital.status", "occupation", "relationship", "race", "sex", "capital.gain", "capital.loss", "hours.per.week", "native.country","earnings...
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