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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

Vectorization


Most R users should have encountered this first tweak. In essence, vectorization allows R operators to take vectors as arguments for quick processing of multiple values. This is unlike some other programming languages such as C, C++, and Java, in which the processing of multiple values is usually done by iterating through and applying operators on each element of a vector (or array). R, being a flexible language, allows users to program using either iteration or vectorization. However, most of the time, iteration incurs significant and unnecessary computational cost because R is an interpreted, not compiled, language.

Take for example, the following simple code. Its goal is simply to calculate the square of every element in the random vector data. The first approach is to set up a for loop through every element of data and square it individually. Many would be tempted to take this approach because this is how it is done typically in other programming languages. Yet, a far more...

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