Learning R: A Step-by-Step Function Guide to Data Analysis

by Richard Cotton

Overview

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you'll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.

The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you've learned, and concludes with exercises, most of which involve writing R code.

• Write a simple R program, and discover what the language can do
• Use data types such as vectors, arrays, lists, data frames, and strings
• Execute code conditionally or repeatedly with branches and loops
• Apply R add-on packages, and package your own work for others
• Learn how to clean data you import from a variety of sources
• Understand data through visualization and summary statistics
• Use statistical models to pass quantitative judgments about data and make predictions
• Learn what to do when things go wrong while writing data analysis code

Product Details

ISBN-13:	9781449357108
Publisher:	O'Reilly Media, Incorporated
Publication date:	09/30/2013
Pages:	396
Product dimensions:	6.90(w) x 9.00(h) x 0.90(d)

About the Author

Richie is a data scientist with a background in chemical health and safety, and has worked extensively on tools to give non-technical users access to statistical models. He is the author of the R packages "assertive" for checking the state of your variables and "sig" to make sure your functions have a sensible API. He runs The Damned Liars statistics consultancy.

Table of Contents

• Preface
• The R Language
  • Chapter 1: Introduction
  • Chapter 2: A Scientific Calculator
  • Chapter 3: Inspecting Variables and Your Workspace
  • Chapter 4: Vectors, Matrices, and Arrays
  • Chapter 5: Lists and Data Frames
  • Chapter 6: Environments and Functions
  • Chapter 7: Strings and Factors
  • Chapter 8: Flow Control and Loops
  • Chapter 9: Advanced Looping
  • Chapter 10: Packages
  • Chapter 11: Dates and Times
• The Data Analysis Workflow
  • Chapter 12: Getting Data
  • Chapter 13: Cleaning and Transforming
  • Chapter 14: Exploring and Visualizing
  • Chapter 15: Distributions and Modeling
  • Chapter 16: Programming
  • Chapter 17: Making Packages
• Appendixes
• Bibliography
• Index
• Colophon