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R High Performance Programming

You're reading from   R High Performance Programming Overcome performance difficulties in R with a range of exciting techniques and solutions

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781783989263
Length 176 pages
Edition 1st Edition
Languages
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Authors (2):
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Tjhi W Chandra Tjhi W Chandra
Author Profile Icon Tjhi W Chandra
Tjhi W Chandra
Aloysius Shao Qin Lim Aloysius Shao Qin Lim
Author Profile Icon Aloysius Shao Qin Lim
Aloysius Shao Qin Lim
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Table of Contents (12) Chapters Close

Preface 1. Understanding R's Performance – Why Are R Programs Sometimes Slow? FREE CHAPTER 2. Profiling – Measuring Code's Performance 3. Simple Tweaks to Make R Run Faster 4. Using Compiled Code for Greater Speed 5. Using GPUs to Run R Even Faster 6. Simple Tweaks to Use Less RAM 7. Processing Large Datasets with Limited RAM 8. Multiplying Performance with Parallel Computing 9. Offloading Data Processing to Database Systems 10. R and Big Data Index

Using compiled languages in R

Code compilation can provide modest gains in computational performance, but there are limits to these gains because the compiled code still needs to be evaluated by R in a dynamic fashion. For example, we explained in Chapter 3, Simple Tweaks to Make R Run Faster, how R, being a dynamically typed language, needs to check the type of an object before applying any operations. In the case of mov.avg(), every time R encounters the + operator, it needs to check that x is a numeric vector, as it could have been modified between each iteration of the for loop. In contrast, a statically typed language performs these checks at compile time, resulting in much faster run time performance.

For this and many other reasons, R's dynamic nature poses barriers to computational performance. The only way to break through these barriers is to turn to compiled languages such as C and use them from within R. This section assumes that you have some basic knowledge of compiled...

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