Joseph Adler - R in a Nutshell, 2nd Edition [2012, PDF, ENG] torrent

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R in a Nutshell, 2nd Edition

Год: October 2012
Автор: Joseph Adler
Издательство: O'Reilly Media
ISBN: 978-1-4493-1208-4
Язык: Английский
Формат: PDF
Качество: Изначально компьютерное (eBook)
Интерактивное оглавление: Да
Количество страниц: 724
Описание:
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
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[spoiler="Оглавление"]
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
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