R in a Nutshell. 2nd Edition - Helion

ebook

Autor: Joseph Adler
ISBN: 978-14-493-5822-8
stron: 724, Format: ebook
Data wydania: 2012-09-26
Księgarnia: Helion

Cena książki: 126,65 zł (poprzednio: 147,27 zł)
Oszczędzasz: 14% (-20,62 zł)

Osoby, które kupiły tę książkę, wybierały także »

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

Osoby które kupowały "R in a Nutshell. 2nd Edition", wybierały także:

Windows Media Center. Domowe centrum rozrywki 66,67 zł, (8,00 zł -88%)
Ruby on Rails. Ćwiczenia 18,75 zł, (3,00 zł -84%)
Przywództwo w świecie VUCA. Jak być skutecznym liderem w niepewnym środowisku 58,64 zł, (12,90 zł -78%)
Scrum. O zwinnym zarządzaniu projektami. Wydanie II rozszerzone 58,64 zł, (12,90 zł -78%)
Od hierarchii do turkusu, czyli jak zarządzać w XXI wieku 58,64 zł, (12,90 zł -78%)

Spis treści

R in a Nutshell. A Desktop Quick Reference. 2nd Edition eBook -- spis treści

R in a Nutshell
Preface
- Why I Wrote This Book
- When Should You Use R?
- Whats 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