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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

Swapping active and nonactive data


In some situations, large objects that are removed to free up memory are needed later in the program. R provides tools to save data to the disk and reload them later when enough memory is available. Returning to the retail sales data example, suppose that we need the sales.data data frame for further processing after mining for frequent itemsets. We can save it to the disk using saveRDS() and reload it later using readRDS():

trans.list <- split(sales.data$item, sales.data$trans)
saveRDS(sales.data, "sales.data.rds")
rm(sales.data)
trans.arules <- as(trans.list, "transactions")
rm(trans.list)
freq.itemsets <- apriori(trans.arules, list(support = 0.3))
sales.data <- readRDS("sales.data.rds")
# Perform further processing with sales.data

The saveRDS() and readRDS() functions save one object at a time without the name of the object. For example, the name sales.data is not saved. However, the column names trans and items are saved. As an alternative...

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