At the beginning of this chapter, we showed you how we can deal with an MS Excel file as a database and how an MS Access database table can be imported into R. One of the major problems in R is that its memory is bound by the system virtual memory, and that is why the data should be smaller in size than the memory of a dataset to be able to work with it. But in reality, datasets are often larger than the virtual memory and sometimes the length of the array or vector exceeds the maximum addressable range. To overcome these two limitations, R can be utilized with relational databases. Contributed R packages exist to help in dealing with such large datasets, and they have been highlighted in this chapter, particularly filehash and ff. We also discussed sqldf for faster data manipulation.
Data Manipulation with R - Second Edition
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Data Manipulation with R - Second Edition
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Overview of this book
<p>This book starts with the installation of R and how to go about using R and its libraries. We then discuss the mode of R objects and its classes and then highlight different R data types with their basic operations.</p>
<p>The primary focus on group-wise data manipulation with the split-apply-combine strategy has been explained with specific examples. The book also contains coverage of some specific libraries such as lubridate, reshape2, plyr, dplyr, stringr, and sqldf. You will not only learn about group-wise data manipulation, but also learn how to efficiently handle date, string, and factor variables along with different layouts of datasets using the reshape2 package.</p>
<p>By the end of this book, you will have learned about text manipulation using stringr, how to extract data from twitter using twitteR library, how to clean raw data, and how to structure your raw data for data mining.</p>
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Data Manipulation with R - Second Edition
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Table of Contents (13 chapters)
Data Manipulation with R Second Edition
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introduction to R Data Types and Basic Operations
Basic Data Manipulation
Data Manipulation Using plyr and dplyr
Reshaping Datasets
R and Databases
Text Manipulation
Index
Customer Reviews