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If you're considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You'll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports. 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…mehr

Produktbeschreibung
If you're considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You'll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports. 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
  • Produktdetails
  • In a Nutshell (O'Reilly)
  • Verlag: O'Reilly Media
  • 2nd ed.
  • Seitenzahl: 724
  • Erscheinungstermin: 30. Oktober 2012
  • Englisch
  • Abmessung: 228mm x 153mm x 43mm
  • Gewicht: 996g
  • ISBN-13: 9781449312084
  • ISBN-10: 144931208X
  • Artikelnr.: 34197127
Autorenporträt
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 from MIT. He is the inventor of several patents for computer security and cryptography, and the author of Baseball Hacks. Currently, he is a senior data scientist at LinkedIn.
Inhaltsangabe
Preface
Why I Wrote This Book
When Should You Use R?
What's New in the Second Edition?
R License Terms
Examples
How This Book Is Organized
Conventions Used in This Book
Using Code Examples
Safari® Books Online
How to Contact Us
Acknowledgments
R Basics
Chapter 1: Getting and Installing R
1.1 R Versions
1.2 Getting and Installing Interactive R Binaries
Chapter 2: The R User Interface
2.1 The R Graphical User Interface
2.2 The R Console
2.3 Batch Mode
2.4 Using R Inside Microsoft Excel
2.5 RStudio
2.6 Other Ways to Run R
Chapter 3: A Short R Tutorial
3.1 Basic Operations in R
3.2 Functions
3.3 Variables
3.4 Introduction to Data Structures
3.5 Objects and Classes
3.6 Models and Formulas
3.7 Charts and Graphics
3.8 Getting Help
Chapter 4: R Packages
4.1 An Overview of Packages
4.2 Listing Packages in Local Libraries
4.3 Loading Packages
4.4 Exploring Package Repositories
4.5 Installing Packages From Other Repositories
4.6 Custom Packages
The R Language
Chapter 5: An Overview of the R Language
5.1 Expressions
5.2 Objects
5.3 Symbols
5.4 Functions
5.5 Objects Are Copied in Assignment Statements
5.6 Everything in R Is an Object
5.7 Special Values
5.8 Coercion
5.9 The R Interpreter
5.10 Seeing How R Works
Chapter 6: R Syntax
6.1 Constants
6.2 Operators
6.3 Expressions
6.4 Control Structures
6.5 Accessing Data Structures
6.6 R Code Style Standards
Chapter 7: R Objects
7.1 Primitive Object Types
7.2 Vectors
7.3 Lists
7.4 Other Objects
7.5 Attributes
Chapter 8: Symbols and Environments
8.1 Symbols
8.2 Working with Environments
8.3 The Global Environment
8.4 Environments and Functions
8.5 Exceptions
Chapter 9: Functions
9.1 The Function Keyword
9.2 Arguments
9.3 Return Values
9.4 Functions as Arguments
9.5 Argument Order and Named Arguments
9.6 Side Effects
Chapter 10: Object-Oriented Programming
10.1 Overview of Object-Oriented Programming in R
10.2 Object-Oriented Programming in R: S4 Classes
10.3 Old-School OOP in R: S3
Working with Data
Chapter 11: Saving, Loading, and Editing Data
11.1 Entering Data Within R
11.2 Saving and Loading R Objects
11.3 Importing Data from External Files
11.4 Exporting Data
11.5 Importing Data From Databases
11.6 Getting Data from Hadoop
Chapter 12: Preparing Data
12.1 Combining Data Sets
12.2 Transformations
12.3 Binning Data
12.4 Subsets
12.5 Summarizing Functions
12.6 Data Cleaning
12.7 Finding and Removing Duplicates
12.8 Sorting
Data Visualization
Chapter 13: Graphics
13.1 An Overview of R Graphics
13.2 Graphics Devices
13.3 Customizing Charts
Chapter 14: Lattice Graphics
14.1 History
14.2 An Overview of the Lattice Package
14.3 High-Level Lattice Plotting Functions
14.4 Customizing Lattice Graphics
14.5 Low-Level Functions
Chapter 15: ggplot2
15.1 A Short Introduction
15.2 The Grammar of Graphics
15.3 A More Complex Example: Medicare Data
15.4 Quick Plot
15.5 Creating Graphics with ggplot2
15.6 Learning More
Statistics with R
Chapter 16: Analyzing Data
16.1 Summary Statistics
16.2 Correlation and Covariance
16.3 Principal Components Analysis
16.4 Factor Analysis
16.5 Bootstrap Resampling
Chapter 17: Probability Distributions
17.1 Normal Distribution
17.2 Common Distribution-Type Arguments
17.3 Distribution Function Families
Chapter 18: Statistical Tests
18.1 Continuous Data
18.2 Discrete Data
Chapter 19: Power Tests
19.1 Experimental Design Example
19.2 t-Test Design
19.3 Proportion Test Design
19.4 ANOVA Test Design
Chapter 20: Regression Models
20.1 Example: A Simple Linear Model
20.2 Details About the lm Function
20.3 Subset Selection and Shrinkage Methods
20.4 Nonlinear Models
20.5 Survival Models
20.6 Smoothing
20.7 Machine Learning Algorithms for Regression
Chapter 21: Classification Models
21.1 Linear Classification Models
21.2 Machine Learning Algorithms for Classification
Chapter 22: Machine Learning
22.1 Market Basket Analysis
22.2 Clustering
Chapter 23: Time Series Analysis
23.1 Autocorrelation Functions
23.2 Time Series Models
Additional Topics
Chapter 24: Optimizing R Programs
24.1 Measuring R Program Performance
24.2 Optimizing Your R Code
24.3 Other Ways to Speed Up R
Chapter 25: Bioconductor
25.1 An Example
25.2 Key Bioconductor Packages
25.3 Data Structures
25.4 Where to Go Next
Chapter 26: R and Hadoop
26.1 R and Hadoop
26.2 Other Packages for Parallel Computation with R
26.3 Where to Learn More
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