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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

Use of built-in functions

As a programming language, R comes with low-level operators, such as basic arithmetic operators that can be used to construct more complex operators or functions. While R provides the flexibility to define functions, a performance comparison between an R function versus an equivalent function in a compiled language would almost always favor the latter. However, R and some CRAN packages provide a rich set of functions that are implemented in compiled languages such as C/C++. It is usually preferable to use these functions rather than to write custom R functions to perform the same task.

Consider a simple example of how to calculate the sums of the rows of the following random matrix data. A code to perform these functions can be constructed by calling the apply() function, and setting the margin to 1 (representing a row operation) and by setting the FUN (or function) argument to sum. Alternatively, R provides a built-in function for this purpose called rowSums. The...

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