Buying Options
R in a Nutshell

Save a Tree - Go Digital What is this?

Ebook $35.99
Add to Cart
Print+Ebook $54.99
Add to Cart
Print $49.99
Add to Cart
Print £38.50 What is this?
Add to Cart
Safari Books Online
Read Now >

R is rapidly becoming the standard for developing statistical software, and R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.

Full Description
Table of Contents
  1. R Basics

    1. Chapter 1 Getting and Installing R

      1. R Versions
      2. Getting and Installing Interactive R Binaries
    2. Chapter 2 The R User Interface

      1. The R Graphical User Interface
      2. The R Console
      3. Batch Mode
      4. Using R Inside Microsoft Excel
      5. Other Ways to Run R
    3. Chapter 3 A Short R Tutorial

      1. Basic Operations in R
      2. Functions
      3. Variables
      4. Introduction to Data Structures
      5. Objects and Classes
      6. Models and Formulas
      7. Charts and Graphics
      8. Getting Help
    4. Chapter 4 R Packages

      1. An Overview of Packages
      2. Listing Packages in Local Libraries
      3. Loading Packages
      4. Exploring Package Repositories
      5. Custom Packages
  2. The R Language

    1. Chapter 5 An Overview of the R Language

      1. Expressions
      2. Objects
      3. Symbols
      4. Functions
      5. Objects Are Copied in Assignment Statements
      6. Everything in R Is an Object
      7. Special Values
      8. Coercion
      9. The R Interpreter
      10. Seeing How R Works
    2. Chapter 6 R Syntax

      1. Constants
      2. Operators
      3. Expressions
      4. Control Structures
      5. Accessing Data Structures
      6. R Code Style Standards
    3. Chapter 7 R Objects

      1. Primitive Object Types
      2. Vectors
      3. Lists
      4. Other Objects
      5. Attributes
    4. Chapter 8 Symbols and Environments

      1. Symbols
      2. Working with Environments
      3. The Global Environment
      4. Environments and Functions
      5. Exceptions
    5. Chapter 9 Functions

      1. The Function Keyword
      2. Arguments
      3. Return Values
      4. Functions As Arguments
      5. Argument Order and Named Arguments
      6. Side Effects
    6. Chapter 10 Object-Oriented Programming

      1. Overview of Object-Oriented Programming in R
      2. Object-Oriented Programming in R: S4 Classes
      3. Old-School OOP in R: S3
    7. Chapter 11 High-Performance R

      1. Use Built-in Math Functions
      2. Use Environments for Lookup Tables
      3. Use a Database to Query Large Data Sets
      4. Preallocate Memory
      5. Monitor How Much Memory You Are Using
      6. Functions for Big Data Sets
      7. Parallel Computation with R
      8. High-Performance R Binaries
  3. Working with Data

    1. Chapter 12 Saving, Loading, and Editing Data

      1. Entering Data Within R
      2. Saving and Loading R Objects
      3. Importing Data from External Files
      4. Exporting Data
      5. Importing Data from Databases
    2. Chapter 13 Preparing Data

      1. Combining Data Sets
      2. Transformations
      3. Binning Data
      4. Subsets
      5. Summarizing Functions
      6. Data Cleaning
      7. Finding and Removing Duplicates
      8. Sorting
    3. Chapter 14 Graphics

      1. An Overview of R Graphics
      2. Graphics Devices
      3. Customizing Charts
    4. Chapter 15 Lattice Graphics

      1. History
      2. An Overview of the Lattice Package
      3. High-Level Lattice Plotting Functions
      4. Customizing Lattice Graphics
      5. Low-Level Functions
  4. Statistics with R

    1. Chapter 16 Analyzing Data

      1. Summary Statistics
      2. Correlation and Covariance
      3. Principal Components Analysis
      4. Factor Analysis
      5. Bootstrap Resampling
    2. Chapter 17 Probability Distributions

      1. Normal Distribution
      2. Common Distribution-Type Arguments
      3. Distribution Function Families
    3. Chapter 18 Statistical Tests

      1. Continuous Data
      2. Discrete Data
    4. Chapter 19 Power Tests

      1. Experimental Design Example
      2. t-Test Design
      3. Proportion Test Design
      4. ANOVA Test Design
    5. Chapter 20 Regression Models

      1. Example: A Simple Linear Model
      2. Details About the lm Function
      3. Subset Selection and Shrinkage Methods
      4. Nonlinear Models
      5. Survival Models
      6. Smoothing
      7. Machine Learning Algorithms for Regression
    6. Chapter 21 Classification Models

      1. Linear Classification Models
      2. Machine Learning Algorithms for Classification
    7. Chapter 22 Machine Learning

      1. Market Basket Analysis
      2. Clustering
    8. Chapter 23 Time Series Analysis

      1. Autocorrelation Functions
      2. Time Series Models
    9. Chapter 24 Bioconductor

      1. An Example
      2. Key Bioconductor Packages
      3. Data Structures
      4. Where to Go Next
  1. Appendix R Reference

    1. base

    2. boot

    3. class

    4. cluster

    5. codetools

    6. foreign

    7. grDevices

    8. graphics

    9. grid

    10. KernSmooth

    11. lattice

    12. MASS

    13. methods

    14. mgcv

    15. nlme

    16. nnet

    17. rpart

    18. spatial

    19. splines

    20. stats

    21. stats4

    22. survival

    23. tcltk

    24. tools

    25. utils

  2. Bibliography

  3. Colophon

View Full Table of Contents
Product Details
R in a Nutshell
Joseph Adler
O'Reilly Media
  • Print
  • Ebook
  • Safari Books Online
Print Release:
December 2009
Ebook Release:
December 2009
Print ISBN:
| ISBN 10:
Ebook ISBN:
| ISBN 10:
Customer Reviews
About the Author
  1. Joseph Adler

    Joseph Adler has many years of experience in data mining and data analysis at companies including DoubleClick, American Express, and VeriSign. He graduated from MIT with an Sc.B and M.Eng in Computer Science and Electrical Engineering. He is the inventor of several patents for computer security and cryptography, and the author of Baseball Hacks and R in a Nutshell.

    View Joseph Adler's full profile page.


The animal on the cover of R in a Nutshell is a harpy eagle (Harpia harpyja). Black feathers line the top half of the bird, while white feathers mostly make up the balance, although the underside of its wings may be striped black-and-white. Unlike other species of birds, male and female harpy eagles appear virtually identical.

These eagles-the most powerful, carnivorous raptors in the Americas-typically inhabit tropical rain forests. They prey upon animals that live in trees: sloths, monkeys, opossums, and even other birds, such as macaws.

The eagle is named after the harpies of ancient Greek mythology, female wind spirits who were said to be human from the chest to their ankles and eagle from the neck up. Mythological harpies tormented people as they carried them to the underworld with their clawed feet; perhaps similarly, harpy eagles' talons violently pierce and subdue their prey before the eagles carry them back to their nests.

Harpy eagles also inspire modern-day life: the eagle is the national bird of Panama and is pictured on the country's coat of arms. The bird also inspired the design of Fawkes the Phoenix in the Harry Potter film series.

The cover image is from Cassell's Natural History. The cover font is Adobe ITC Garamond. The text font is Linotype Birka; the heading font is Adobe Myriad Condensed; and the code font is LucasFont's TheSansMonoCondensed.

  • Book cover of R in a Nutshell

Wik portal where you can upload your ideas and interests and educational mind set for a free website account please apply here