Book Image

Social Media Mining with R

By : Richard Heimann, Nathan Danneman
Book Image

Social Media Mining with R

By: Richard Heimann, Nathan Danneman

Overview of this book

<p>The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. However, analyzing this ever-growing pile of data is quite tricky and, if done erroneously, could lead to wrong inferences.</p> <p>By using this essential guide, you will gain hands-on experience with generating insights from social media data. This book provides detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to help you accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.</p> <p>The book begins by introducing you to the topic of social media data, including its sources and properties. It then explains the basics of R programming in a straightforward, unassuming way. Thereafter, you will be made aware of the inferential dangers associated with social media data and how to avoid them, before describing and implementing a suite of social media mining techniques.</p> <p>Social Media Mining in R provides a light theoretical background, comprehensive instruction, and state-of-the-art techniques, and by reading this book, you will be well equipped to embark on your own analyses of social media data.</p>
Table of Contents (14 chapters)
Social Media Mining with R
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Style and workflow


Statistical programmers can think of R code—like other languages—as being dysfunctional, functional but awkward, or graceful. Graceful code is clear and readable, which helps prevent errors. Here are a few tips on writing graceful R code:

  • Filenames should end in .R and be meaningful.

  • Variable names should be short. If necessary, use a period to delineate multiword variable names (for example, my.variable).

  • Keep every line short. R will not terminate a command until all parentheses are closed, so feel free to wrap commands across lines for brevity.

  • Use spaces before and after assignment, after commas, and around parentheses and operators (such as +) for readability.

  • Use the left arrow with hyphen (<-) for assignment, never the single equals sign.

For more details on writing good R code, refer to the guide at http://google-styleguide.googlecode.com/svn/trunk/Rguide.xml. Again, though R can be used interactively from within the terminal, it is best practice to develop code within...