R in a Nutshell by Adler, Joseph -- Read -- Imperial Library of Trantor

Index

R in a Nutshell 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

I. R Basics

1. Getting and Installing R

R Versions Getting and Installing Interactive R Binaries

Windows Mac OS X Linux and Unix Systems

Installation using package management systems Installing R from downloaded files

2. The R User Interface

The R Graphical User Interface

Windows Mac OS X Linux and Unix

The R Console

Command-Line Editing

Batch Mode Using R Inside Microsoft Excel RStudio Other Ways to Run R

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

4. R Packages

An Overview of Packages Listing Packages in Local Libraries Loading Packages

Loading Packages on Windows and Linux Loading Packages on Mac OS X

Exploring Package Repositories

Exploring R Package Repositories on the Web Finding and Installing Packages Inside R

Windows and Linux GUIs Mac OS X GUI R console Installing from the command line

Installing Packages From Other Repositories Custom Packages

Creating a Package Directory Building the Package

II. The R Language

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

NA Inf and -Inf NaN NULL

Coercion The R Interpreter Seeing How R Works

6. R Syntax

Constants

Numeric Vectors Character Vectors Symbols

Operators

Order of Operations Assignments

Expressions

Separating Expressions Parentheses Curly Braces

Control Structures

Conditional Statements Loops

Accessing Data Structures

Data Structure Operators Indexing by Integer Vector Indexing by Logical Vector Indexing by Name

R Code Style Standards

7. R Objects

Primitive Object Types Vectors Lists Other Objects

Matrices Arrays Factors Data Frames Formulas Time Series Shingles Dates and Times Connections

Attributes

Class

8. Symbols and Environments

Symbols Working with Environments The Global Environment Environments and Functions

Working with the Call Stack Evaluating Functions in Different Environments Adding Objects to an Environment

Exceptions

Signaling Errors Catching Errors

9. Functions

The Function Keyword Arguments Return Values Functions as Arguments

Anonymous Functions Properties of Functions

Argument Order and Named Arguments Side Effects

Changes to Other Environments Input/Output Graphics

10. Object-Oriented Programming

Overview of Object-Oriented Programming in R

Key Ideas Implementation Example

Object-Oriented Programming in R: S4 Classes

Defining Classes New Objects Accessing Slots Working with Objects Creating Coercion Methods Methods Managing Methods Basic Classes More Help

Old-School OOP in R: S3

S3 Classes S3 Methods Using S3 Classes in S4 Classes Finding Hidden S3 Methods

III. Working with Data

11. Saving, Loading, and Editing Data

Entering Data Within R

Entering Data Using R Commands Using the Edit GUI

Windows Data Editor Mac OS X Data Editor X Windows (Linux) Data Editor

Saving and Loading R Objects

Saving Objects with save

Importing Data from External Files

Text Files

Delimited files Fixed-width files Other functions to parse data

Other Software

Exporting Data Importing Data From Databases

Export Then Import Database Connection Packages RODBC

Getting RODBC working

Installing the RODBC package Installing ODBC drivers Example: SQLite ODBC on Mac OS X Example: SQLite ODBC on Windows

Using RODBC

Opening a channel Getting information about the database Getting data Closing a channel

DBI

Opening a connection Getting DB information Querying the database Cleaning up

TSDBI

Getting Data from Hadoop

12. Preparing Data

Combining Data Sets

Pasting Together Data Structures

Paste rbind and cbind An extended example

Merging Data by Common Fields

Transformations

Reassigning Variables The Transform Function Applying a Function to Each Element of an Object

Applying a function to an array Applying a function to a list or vector the plyr library

Binning Data

Shingles Cut Combining Objects with a Grouping Variable

Subsets

Bracket Notation subset Function Random Sampling

Summarizing Functions

tapply, aggregate Aggregating Tables with rowsum Counting Values Reshaping Data

Transposing matrices and data frames Reshaping data frames and matrices Using the Reshape Library

Melting and Casting Examples of reshape melt Cast

Data Cleaning Finding and Removing Duplicates Sorting

IV. Data Visualization

13. Graphics

An Overview of R Graphics

Scatter Plots Plotting Time Series Bar Charts Pie Charts Plotting Categorical Data Three-Dimensional Data Plotting Distributions Box Plots

Graphics Devices Customizing Charts

Common Arguments to Chart Functions Graphical Parameters

Annotation Margins Multiple plots Text properties

Text size Typeface Alignment and spacing Rotation

Line properties Colors Axes Points Graphical parameters by name

Basic Graphics Functions

points lines curve text abline polygon segments legend title axis box mtext trans3d

14. Lattice Graphics

History An Overview of the Lattice Package

How Lattice Works A Simple Example Using Lattice Functions Custom Panel Functions

High-Level Lattice Plotting Functions

Univariate Trellis Plots

Bar charts Dot plots Histograms Density plots Strip plots Univariate quantile-quantile plots

Bivariate Trellis Plots

Scatter plots Box plots in lattice Scatter plots matrices Bivariate quantile-quantile plots

Trivariate Plots

Level plots Contour plots Cloud plots Wire-frame plots

Other Plots

Customizing Lattice Graphics

Common Arguments to Lattice Functions trellis.skeleton Controlling How Axes Are Drawn Parameters plot.trellis strip.default simpleKey

Low-Level Functions

Low-Level Graphics Functions Panel Functions

15. ggplot2

A Short Introduction The Grammar of Graphics A More Complex Example: Medicare Data Quick Plot Creating Graphics with ggplot2 Learning More

V. Statistics with R

16. Analyzing Data

Summary Statistics Correlation and Covariance Principal Components Analysis Factor Analysis Bootstrap Resampling

17. Probability Distributions

Normal Distribution Common Distribution-Type Arguments Distribution Function Families

18. Statistical Tests

Continuous Data

Normal Distribution-Based Tests

Comparing means Comparing paired data Comparing variances of two populations Comparing means across more than two groups Pairwise t-tests between multiple groups Testing for normality Testing if a data vector came from an arbitrary distribution Testing if two data vectors came from the same distribution Correlation tests

Non-Parametric Tests

Comparing two means Comparing more than two means Comparing variances Difference in scale parameters

Discrete Data

Proportion Tests Binomial Tests Tabular Data Tests Non-Parametric Tabular Data Tests

19. Power Tests

Experimental Design Example t-Test Design Proportion Test Design ANOVA Test Design

20. Regression Models

Example: A Simple Linear Model

Fitting a Model Helper Functions for Specifying the Model Getting Information About a Model

Viewing the model Predicting values using a model Analyzing the fit

Refining the Model

Details About the lm Function

Assumptions of Least Squares Regression Robust and Resistant Regression

Resistant regression Robust regression Comparing lm, lqs, and rlm

Subset Selection and Shrinkage Methods

Stepwise Variable Selection Ridge Regression Lasso and Least Angle Regression elasticnet Principal Components Regression and Partial Least Squares Regression

Nonlinear Models

Generalized Linear Models glmnet Nonlinear Least Squares

Survival Models Smoothing

Splines Fitting Polynomial Surfaces Kernel Smoothing

Machine Learning Algorithms for Regression

Regression Tree Models

Recursive partitioning trees Patient rule induction method Bagging for regression Boosting for regression Random forests for regression

MARS Neural Networks Project Pursuit Regression Generalized Additive Models Support Vector Machines

21. Classification Models

Linear Classification Models

Logistic Regression Linear Discriminant Analysis Log-Linear Models

Machine Learning Algorithms for Classification

k Nearest Neighbors Classification Tree Models

Bagging Boosting

Neural Networks SVMs Random Forests

22. Machine Learning

Market Basket Analysis Clustering

Distance Measures Clustering Algorithms

23. Time Series Analysis

Autocorrelation Functions Time Series Models

VI. Additional Topics

24. Optimizing R Programs

Measuring R Program Performance

Timing Profiling Monitor How Much Memory You Are Using Profiling Memory Usage

Optimizing Your R Code

Using Vector Operations

Iterative algorithms and vector operations Transforming problems to use built-in functions

Lookup Performance in R

Lookups and R objects Using environment objects in place of vectors

Use a Database to Query Large Data Sets Preallocate Memory Cleaning Up Memory Functions for Big Data Sets

Other Ways to Speed Up R

The R Byte Code Compiler

Manual compilation Inspecting byte code Just-in-time compilation

High-Performance R Binaries

Revolution R Building your own

Building on Microsoft Windows Building R on Unix-like systems Building R on Mac OS X

25. Bioconductor

An Example

Loading Raw Expression Data Loading Data from GEO Matching Phenotype Data Analyzing Expression Data

Key Bioconductor Packages Data Structures

eSet AssayData AnnotatedDataFrame MIAME Other Classes Used by Bioconductor Packages

Where to Go Next

Resources Outside Bioconductor Vignettes Courses Books

26. R and Hadoop

R and Hadoop

Overview of Hadoop

Map/Reduce Distributed data storage Managing a cluster of servers Java framework When should you consider Hadoop?

RHadoop

Make sure Hadoop is installed locally Installing RHadoop locally An example RHadoop application Details of rmr Learning more

Hadoop Streaming Learning More

Other Packages for Parallel Computation with R

Segue doMC

Where to Learn More

A. R Reference

base

Functions Data Sets

boot

Functions Data Sets

class

Functions

cluster

Functions Data Sets

codetools foreign

Functions

grDevices

Functions Data Sets

graphics

Functions

grid KernSmooth

Functions

lattice

Functions Data Sets

MASS

Functions Data Sets

methods

Functions

mgcv nlme nnet

Functions

rpart

Functions Data Sets

spatial

Functions

splines

Functions

stats

Functions Data Set

stats4

Functions

survival

Functions Data Sets

tcltk tools

Functions Data Sets

utils

Functions

Bibliography Index About the Author Colophon Copyright

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