A single dataset can be rearranged in many different ways but, before going into this rearrangement, let's look at how we usually perceive a dataset. Whenever we think about any dataset, we think of a two-dimensional arrangement, where a row represents a subject's (a subject could be a person and is typically the respondent in a survey) information for all the variables in a dataset, and a column represents the information for each characteristic for all subjects. This means rows indicate records, and columns indicate variables, characteristics, or attributes. This is the typical layout of a dataset. In this arrangement, one or more variables might play the role of an identifier, and others are measured characteristics. For the purpose of reshaping, we could group the variables into two groups: identifier variables and measured variables. They are explained here:
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