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R in a Nutshell (In a Nutshell)by Joseph Adler
Synopses & Reviews
Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.
Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve — rather than rely on tools to think for you.
"Finally, a concise reference for understanding how to conquer piles of data." --Austin King, Senior Web Developer, Mozilla
"An indispensable text for aspiring data scientists." --Michael E. Driscoll, CEO/Founder, Dataspora
Book News Annotation:
This concise guide to R, an open source software package, shows how to program in R for developing statistical software. Early sections cover basics of getting and running R and the R language. A section on working with data covers data processing in R, and also treats summary statistics and charts, without relying on too much math. Chapters on statistics with R cover statistical tests and models including probability distributions, regression models, time series analysis, bioinformatics. Examples are given from medicine, business, and sports. An appendix describes functions and data sets included with the base distribution of R. Adler has written other books on computing. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)
Perform data analysis with R quickly and efficiently with the task-oriented recipes in this cookbook. Although the R language and environment include everything you need to perform statistical work right out of the box, its structure can often be difficult to master. R Cookbook will help both beginners and experienced data programmers unlock and use the power of R.
This practical book provides a collection of concise recipes that will help you be productive with R immediately. Youll get the job done faster and learn more about R in the process.
Key topics include:
These days it seems like everyone is collecting data. But all of that data is just raw information — to make that information meaningful, it has to be organized, filtered, and analyzed. Anyone can apply data analysis tools and get results, but without the right approach those results may be useless.
In Real World Data Analysis, author Philipp Janert teaches you how to think about data: how to effectively approach data analysis problems, and how to extract all of the available information from your data. Janert covers univariate data, data in multiple dimensions, time series data, graphical techniques, data mining, machine learning, and many other topics. He also reveals how seat-of-the-pants knowledge can lead you to the best approach right from the start, and how to assess results to determine if they're meaningful.
About the Author
Philipp K. Janert is Chief Consultant at Principal Value, LLC. He has worked for small start-ups and in large corporate environments, both in the US and overseas, including several years at Amazon.com, where he initiated and led several projects to improve Amazon's order fulfillment processes. Philipp K. Janert has written about software and software development for the O'Reilly Network, IBM developerWorks, IEEE Software, and Linux Magazine. He holds a Ph.D. in Theoretical Physics from the University of Washington. Visit his website at www.principal-value.com.
Table of Contents
Preface R Basics Chapter 1: Getting and Installing R Chapter 2: The R User Interface Chapter 3: A Short R Tutorial Chapter 4: R Packages The R Language Chapter 5: An Overview of the R Language Chapter 6: R Syntax Chapter 7: R Objects Chapter 8: Symbols and Environments Chapter 9: Functions Chapter 10: Object-Oriented Programming Chapter 11: High-Performance R Working with Data Chapter 12: Saving, Loading, and Editing Data Chapter 13: Preparing Data Chapter 14: Graphics Chapter 15: Lattice Graphics Statistics with R Chapter 16: Analyzing Data Chapter 17: Probability Distributions Chapter 18: Statistical Tests Chapter 19: Power Tests Chapter 20: Regression Models Chapter 21: Classification Models Chapter 22: Machine Learning Chapter 23: Time Series Analysis Chapter 24: Bioconductor R Reference Bibliography Colophon
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