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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

The lapply, vapply, sapply, and apply functions

These functions are equivalent to a for-each loop with the advantage that they are much more efficient in terms of performance. Basically, the function is applied over every item in a vectorized object. Its main structure is:

function(object_to_iterate_on, function, additional_arguments(separated by commas))

vapply() and apply() have additional arguments that will be covered in detail in the expanded explanations of these functions.

The function argument can be an already defined function (with its arguments), as follows:

sample.list <- list(a=runif(100,0,1), b=runif(500,0,100),
c=runif(35,0,200))
sapply(sample.list, quantile, probs=0.75)
##       a.75%       b.75%       c.75% 
##   0.7145661  77.4817679 158.9351519

Also, the function can be defined within the same apply-function call (these types of functions are called anonymous functions), as follows:

sapply(sample.list, function(x) round(sum(x+2)))
##     a     b     c 
##   250 27235 ...
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