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R High Performance Programming

You're reading from  R High Performance Programming

Product type Book
Published in Jan 2015
Publisher
ISBN-13 9781783989263
Pages 176 pages
Edition 1st Edition
Languages

Table of Contents (17) Chapters

R High Performance Programming
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Understanding R's Performance – Why Are R Programs Sometimes Slow? 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

Converting R expressions to SQL


While SQL is a powerful and flexible language used to manipulate data in a database, not everyone is proficient in it. Fortunately, the R community has developed a few packages that translate familiar R syntax into SQL statements that are then executed on the database. We will look at two of them—dplyr and PivotalR.

Using dplyr

The dplyr package is a handy package designed to allow the manipulation of table-like data with a standard set of operations and transformations, no matter where the data is stored—in a data frame, data table, or database. It supports SQLite, PostgreSQL, MySQL, Amazon RedShift, Google BigQuery, and MonetDB databases.

The dplyr package provides a way to specify a set of operations to be performed on the data without actually performing the computations on the database server until we instruct R to do so, by calling the collect()function. By pooling a few operations together (as opposed to executing them one by one), the database server...

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