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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

Chapter 3. Simple Tweaks to Make R Run Faster

Improving the speed of an R code does not necessarily involve advanced optimization techniques like parallelizing the code or making it run in the database. Indeed, there are a number of simple tweaks that, while not always obvious, can make R run significantly faster. In this chapter, some of these tweaks are described. By no means do they capture all possible simple means to optimize the R code. However, they constitute some of the most fundamental, and hence often-encountered, opportunities to gain some speedups.

This chapter presents these tweaks in the order of decreasing generality—the more general ones are those found in almost all R codes, regardless of their application. Each tweak is accompanied by an example code that is intentionally kept simple so as not to obscure the explanation of the intended concept with unnecessary application-specific knowledge. In all these examples, artificial datasets are generated using...

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