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PRINTED BOOKS
Author Adler, Joseph, M.Eng.

Title R in a nutshell / Joseph Adler.

Published Sebastopol, Calif. : O'Reilly, [2012]
©2012

Copies

Location Call No. Status
 UniM Cres  519.502855133 ADLE    AVAILABLE
Edition 2nd ed.
Physical description xix, 699 : illustrations ; 23 cm
Notes Previous ed.: Sebastapol, Calif.: O'Reilly, 2010.
"A desktop quick reference"--Cover.
Bibliography Includes bibliographical references (pages 675-676) and index.
Contents pt. I R Basics -- 1.Getting and Installing R -- R Versions -- Getting and Installing Interactive R Binaries -- Windows -- Mac OS X -- Linux and Unix Systems -- 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 -- Installing Packages From Other Repositories -- Custom Packages --
Contents note continued: Creating a Package Directory -- Building the Package -- pt. 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 --
Contents note continued: 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 --
Contents note continued: S3 Classes -- S3 Methods -- Using S3 Classes in S4 Classes -- Finding Hidden S3 Methods -- pt. III Working with Data -- 11.Saving, Loading, and Editing Data -- Entering Data Within R -- Entering Data Using R Commands -- Using the Edit GUI -- Saving and Loading R Objects -- Saving Objects with save -- Importing Data from External Files -- Text Files -- Other Software -- Exporting Data -- Importing Data From Databases -- Export Then Import -- Database Connection Packages -- RODBC -- DBI -- TSDBI -- Getting Data from Hadoop -- 12.Preparing Data -- Combining Data Sets -- Pasting Together Data Structures -- Merging Data by Common Fields -- Transformations -- Reassigning Variables -- The Transform Function -- Applying a Function to Each Element of an Object -- Binning Data -- Shingles -- Cut -- Combining Objects with a Grouping Variable -- Subsets -- Bracket Notation -- subset Function -- Random Sampling -- Summarizing Functions -- tapply, aggregate --
Contents note continued: Aggregating Tables with rowsum -- Counting Values -- Reshaping Data -- Data Cleaning -- Finding and Removing Duplicates -- Sorting -- pt. IV Data Visualization -- 13.Graphic -- 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 -- Basic Graphics Functions -- 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 -- Bivariate Trellis Plots -- Trivariate Plots -- Other Plots -- Customizing Lattice Graphics -- Common Arguments to Lattice Functions -- trellis.skeleton -- Controlling How Axes Are Drawn -- Parameters -- plot.trellis --
Contents note continued: 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 -- pt. 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 -- Non-Parametric Tests -- 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 --
Contents note continued: Fitting a Model -- Helper Functions for Specifying the Model -- Getting Information About a Model -- Refining the Model -- Details About the lm Function -- Assumptions of Least Squares Regression -- Robust and Resistant Regression -- 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 -- 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 --
Contents note continued: Machine Learning Algorithms for Classification -- k Nearest Neighbors -- Classification Tree Models -- 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 -- pt. 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 -- Lookup Performance in R -- 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 -- High-Performance R Binaries -- 25.Bioconductor -- An Example -- Loading Raw Expression Data -- Loading Data from GEO -- Matching Phenotype Data -- Analyzing Expression Data --
Contents note continued: 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.Rand Hadoop -- R and Hadoop -- Overview of Hadoop -- RHadoop -- Hadoop Streaming -- Learning More -- Other Packages for Parallel Computation with R -- Segue -- doMC -- Where to Learn More.
Subject R (Computer program language)
Mathematical statistics -- Data processing.
ISBN 9781449312084 (paperback)
144931208X (paperback)

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