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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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Toc

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

Removing intermediate data when it is no longer needed


In large R programs, objects are created in many places. Often, an object that is created in an earlier part of the program is not needed in later parts of the program. When faced with memory limits, it is useful to free up memory taken up by objects when they are no longer needed, so that subsequent parts of the program can run successfully.

The main tool for this is the rm() function that removes a given list of objects from the current R environment.

In the following example, we have a data frame containing 500,000 transactions from a retail store and the items within each transaction. Each row of the data frame represents a unique transaction-item pair that occurred in a sales database. Although, we have to generate the data for this example in a real business context, this data could be extracted from a retailer's sales database:

trans.lengths <- rpois(5e5, 3) + 1L
trans <- rep.int(1:5e5, trans.lengths)
items <- unlist(lapply...
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