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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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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

Basic summary functions

In this section, table() and aggregate() will be covered. They are basic processing functions that come in the base package.

  • table(): This creates a contingency table with the specified vectors. Although its output is of the table type, it works similar to an array:
    sample.data <-data.frame(var1 =rep(c("Male","Female"),10), var2 =rep(c("A","B","C","D")))
    example.table<-table(sample.data$var1, sample.data$var2)
    example.table
    ##         
    ##          A B C D
    ##   Female 0 5 0 5
    ##   Male   5 0 5 0
    example.table[2,2]
    ## [1] 0
    

    The output of table() can be indexed in the same way as an array.

  • aggregate(): This performs one or more functions over a vector split by a factor variable. aggregate() has basically two ways of usage:
    • With vectors: One or more vectors are passed to the x argument while one or more factor vectors are passed in the by argument. FUN is the aggregation function to be used:
      > data(iris...
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