R in a Nutshell
O'Reilly Media, Inc, USA (Verlag)
978-1-4493-1208-4 (ISBN)
Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop.
- Get started quickly with an R tutorial and hundreds of examples
- Explore R syntax, objects, and other language details
- Find thousands of user-contributed R packages online, including Bioconductor
- Learn how to use R to prepare data for analysis
- Visualize your data with R’s graphics, lattice, and ggplot2 packages
- Use R to calculate statistical fests, fit models, and compute probability distributions
- Speed up intensive computations by writing parallel R programs for Hadoop
- Get a complete desktop reference to R
Why learn R?
Because it's rapidly becoming the standard for developing statistical software. 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. The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.
- Understand the basics of the language, including the nature of R objects
- Learn how to write R functions and build your own packages
- Work with data through visualization, statistical analysis, and other methods
- Explore the wealth of packages contributed by the R community
- Become familiar with the lattice graphics package for high-level data visualization
- Learn about bioinformatics packages provided by Bioconductor
Joseph Adler has years of experience working with lots of popular data mining packages, including databases (including Oracle, PostgreSQL, and MS Access), statistical analysis tools (SAS, SPSS, S-Plus, and R), and data mining tools (SAS Enterprise Miner, Insightful Miner, Oracle Data Mining, Weka, and SPSS Clementine). He is currently leading a project at Verisign to pick a data mining package for enterprise deployment.
R Basics
Chapter 1 Getting and Installing R
R Versions
Getting and Installing Interactive R Binaries
Chapter 2 The R User Interface
The R Graphical User Interface
The R Console
Batch Mode
Using R Inside Microsoft Excel
RStudio
Other Ways to Run R
Chapter 3 A Short R Tutorial
Basic Operations in R
Functions
Variables
Introduction to Data Structures
Objects and Classes
Models and Formulas
Charts and Graphics
Getting Help
Chapter 4 R Packages
An Overview of Packages
Listing Packages in Local Libraries
Loading Packages
Exploring Package Repositories
Installing Packages From Other Repositories
Custom Packages
The R Language
Chapter 5 An Overview of the R Language
Expressions
Objects
Symbols
Functions
Objects Are Copied in Assignment Statements
Everything in R Is an Object
Special Values
Coercion
The R Interpreter
Seeing How R Works
Chapter 6 R Syntax
Constants
Operators
Expressions
Control Structures
Accessing Data Structures
R Code Style Standards
Chapter 7 R Objects
Primitive Object Types
Vectors
Lists
Other Objects
Attributes
Chapter 8 Symbols and Environments
Symbols
Working with Environments
The Global Environment
Environments and Functions
Exceptions
Chapter 9 Functions
The Function Keyword
Arguments
Return Values
Functions as Arguments
Argument Order and Named Arguments
Side Effects
Chapter 10 Object-Oriented Programming
Overview of Object-Oriented Programming in R
Object-Oriented Programming in R: S4 Classes
Old-School OOP in R: S3
Working with Data
Chapter 11 Saving, Loading, and Editing Data
Entering Data Within R
Saving and Loading R Objects
Importing Data from External Files
Exporting Data
Importing Data From Databases
Getting Data from Hadoop
Chapter 12 Preparing Data
Combining Data Sets
Transformations
Binning Data
Subsets
Summarizing Functions
Data Cleaning
Finding and Removing Duplicates
Sorting
Data Visualization
Chapter 13 Graphics
An Overview of R Graphics
Graphics Devices
Customizing Charts
Chapter 14 Lattice Graphics
History
An Overview of the Lattice Package
High-Level Lattice Plotting Functions
Customizing Lattice Graphics
Low-Level Functions
Chapter 15 ggplot2
A Short Introduction
The Grammar of Graphics
A More Complex Example: Medicare Data
Quick Plot
Creating Graphics with ggplot2
Learning More
Statistics with R
Chapter 16 Analyzing Data
Summary Statistics
Correlation and Covariance
Principal Components Analysis
Factor Analysis
Bootstrap Resampling
Chapter 17 Probability Distributions
Normal Distribution
Common Distribution-Type Arguments
Distribution Function Families
Chapter 18 Statistical Tests
Continuous Data
Discrete Data
Chapter 19 Power Tests
Experimental Design Example
t-Test Design
Proportion Test Design
ANOVA Test Design
Chapter 20 Regression Models
Example: A Simple Linear Model
Details About the lm Function
Subset Selection and Shrinkage Methods
Nonlinear Models
Survival Models
Smoothing
Machine Learning Algorithms for Regression
Chapter 21 Classification Models
Linear Classification Models
Machine Learning Algorithms for Classification
Chapter 22 Machine Learning
Market Basket Analysis
Clustering
Chapter 23 Time Series Analysis
Autocorrelation Functions
Time Series Models
Additional Topics
Chapter 24 Optimizing R Programs
Measuring R Program Performance
Optimizing Your R Code
Other Ways to Speed Up R
Chapter 25 Bioconductor
An Example
Key Bioconductor Packages
Data Structures
Where to Go Next
Chapter 26 R and Hadoop
R and Hadoop
Other Packages for Parallel Computation with R
Where to Learn More
Appendix R Reference
base
boot
class
cluster
codetools
foreign
grDevices
graphics
grid
KernSmooth
lattice
MASS
methods
mgcv
nlme
nnet
rpart
spatial
splines
stats
stats4
survival
tcltk
tools
utils
Bibliography
Colophon
"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians." --Martin Schultz, Arthur K. Watson Professor of Computer Science, Yale University
| Erscheint lt. Verlag | 13.11.2012 |
|---|---|
| Zusatzinfo | illustrations |
| Verlagsort | Sebastopol |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Gewicht | 921 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra | |
| ISBN-10 | 1-4493-1208-X / 144931208X |
| ISBN-13 | 978-1-4493-1208-4 / 9781449312084 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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