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R High Performance Programming

R High Performance Programming

By : Aloysius Shao Qin Lim, Tjhi W Chandra
4.4 (14)
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R High Performance Programming

R High Performance Programming

4.4 (14)
By: Aloysius Shao Qin Lim, Tjhi W Chandra

Overview of this book

This book is for programmers and developers who want to improve the performance of their R programs by making them run faster with large data sets or who are trying to solve a pesky performance problem.
Table of Contents (12 chapters)
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11
Index

Preallocating memory

Most strongly typed programming languages like C, C++, and Java generally require a vector (or array) to be declared prior to any operation applied on it. This declaration in effect preallocates the memory space that the vector requires. There are special occasions where dynamic memory allocation is used, but this is seldom the first choice mainly because dynamic memory allocation slows down a program. Every time a vector is resized, the program needs to perform extra steps that include copying the vector to a larger or smaller memory block and deleting the old vector. These steps are not needed if the memory is preallocated.

When it comes to preallocating memory, R is no different from the other programming languages. However, being an interpreted language, it imposes less control, thus it is easy for users to overlook this—R will not throw any compilation error if a vector's memory is not preallocated. Nevertheless, not preallocating memory in R can result...

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