R in a Nutshell. A Desktop Quick Reference (ebook)(audiobook)(audiobook)

Autor:: Joseph Adler

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Okładka książki/ebooka R in a Nutshell. A Desktop Quick Reference

Okładka książki R in a Nutshell. A Desktop Quick Reference

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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

"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."

Spis treści

R in a Nutshell
A Note Regarding Supplemental Files
Preface
- Why I Wrote This Book
- When Should You Use R?
- R License Terms
- Examples
- How This Book Is Organized
- Conventions Used in This Book
- Using Code Examples
- How to Contact Us
- Safari Books Online
- 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
  - 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 Packages on the Web
    - Finding and Installing Packages Inside R
      - Windows and Linux GUIs
      - Mac OS X GUI
      - R console
      - Installing from the command line
  - 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
- 11. High-Performance R
  - Use Built-in Math Functions
  - Use Environments for Lookup Tables
  - Use a Database to Query Large Data Sets
  - Preallocate Memory
  - Monitor How Much Memory You Are Using
    - Monitoring Memory Usage
    - Increasing Memory Limits
    - Cleaning Up Objects
  - Functions for Big Data Sets
  - Parallel Computation with R
  - 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
III. Working with Data
- 12. 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
- 13. 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
  - 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
  - Data Cleaning
  - Finding and Removing Duplicates
  - Sorting
- 14. 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 parameter by name
    - Basic Graphics Functions
      - points
      - lines
      - curve
      - text
      - abline
      - polygon
      - segments
      - legend
      - title
      - axis
      - box
      - mtext
      - trans3d
- 15. 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
IV. 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
    - Principal Components Regression and Partial Least Squares Regression
  - Nonlinear Models
    - Generalized Linear Models
    - 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
- 24. 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
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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