Book Image

Data Manipulation with R - Second Edition

By : Jaynal Abedin, Kishor Kumar Das
Book Image

Data Manipulation with R - Second Edition

By: Jaynal Abedin, Kishor Kumar Das

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
Jaynal Abedin | Kishor Kumar Das
Mar, 2015 | 130 pages
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Table of Contents (13 chapters)
Data Manipulation with R Second Edition
Data Manipulation with R Second Edition
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introduction to R Data Types and Basic Operations
Introduction to R Data Types and Basic Operations
Getting different versions of R
Installing R on different platforms
Installing and using R libraries
Comparing R with other software
R as an enterprise solution
Writing commands in R
R data types and basic operations
The R object structure and mode conversion
Factor and its types
Missing values in R
Summary
2
Basic Data Manipulation
Basic Data Manipulation
Acquiring data
Vector and matrix operations
Factor manipulation
Factors from numeric variables
Date processing using lubridate
Character manipulation
Subscripting and subsetting
Summary
3
Data Manipulation Using plyr and dplyr
Data Manipulation Using plyr and dplyr
Applying the split-apply-combine strategy
Introducing the plyr and dplyr libraries
Comparing base R and plyr
Powerful data manipulation with dplyr
Summary
4
Reshaping Datasets
Reshaping Datasets
Typical layout of a dataset
New layout of a dataset
Reshaping the dataset from the typical layout
Reshaping the dataset with the reshape package
The reshape2 package
Summary
5
R and Databases
R and Databases
R and different databases
Relational databases in R
R and sqldf
Data manipulation using sqldf
Summary
6
Text Manipulation
Text Manipulation
Text data and its source
Text processing using default functions
Working with Twitter data
Summary
Index
Index

Reshaping the dataset from the typical layout

In this section, we will see how we can convert a typical long layout to a typical wide layout, and vice versa. To perform this conversion, we will use the built-in reshape() function. This takes several arguments, but we will use the following arguments:

  • data: This argument specifies the dataset that we want to change the layout of.

  • direction: This argument specifies whether the data is long or wide. Note that, here, long and wide indicate the typical layout.

  • idvar: This argument specifies the identification variable. It could be a single variable or multiple variables.

  • timevar: This argument specifies how many times the values of idvar repeat for each subject.

The following example converts the students' data that was created earlier from a long layout to a wide layout:

# Reshaping dataset using reshape function

wide_students <- reshape(students,direction="wide",idvar="sid",timevar="exmterm")

wide_students
sid math.1 literature.1 language.1...
Previous Section
End of Section 4
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