Software for Data Analysis: Programming with R (Statistics and Computing)

John Chambers Has Been The Principal Designer Of The S Language Since Its Beginning, And In 1999 Received The Acm System Software Award For S, The Only Statistical Software To Receive This Award. He Is Author Or Coauthor Of The Landmark Books On S. Now He Turns To R, The Enormously Successful Open-source System Based On The S Language. R's International Support And The Thousands Of Packages And Other Contributions Have Made It The Standard For Statistical Computing In Research And Teaching. This Book Guides The Reader Through Programming With R, Beginning With Simple Interactive Use And Progressing By Gradual Stages, Starting With Simple Functions. More Advanced Programming Techniques Can Be Added As Needed, Allowing Users To Grow Into Software Contributors, Benefiting Their Careers And The Community. R Packages Provide A Powerful Mechanism For Contributions To Be Organized And Communicated. The Techniques Covered Include Such Modern Programming Enhancements As Classes And Methods, Namespaces, And Interfaces To Spreadsheets Or Data Bases, As Well As Computations For Data Visualization, Numerical Methods, And The Use Of Text Data. Introduction: Principles And Concepts -- Using R -- Programming With R: The Basics -- R Packages -- Objects -- Basic Data And Computations -- Data Visualization And Graphics -- Computing With Text -- New Classes -- Methods And Generic Functions -- Interfaces I: Using C And Fortran -- Interfaces Ii: Between R And Other Systems -- How R Works -- Errata And Notes For “software For Data Analysis: Programming With R”. John M. Chambers. Includes Bibliographical References (p. 479-480) And Indexes.

Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods.

This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples.

The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings.

The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists.

"This book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefitting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated." "The techniques covered include such modern programming enhancements as classes and methods, namespaces, and interfaces to spreadsheets or databases, as well as computations for data visualization, numerical methods, and the use of text data."--Jacket Front Matter....Pages 1-14 Introduction: Principles and Concepts....Pages 1-10 Using R....Pages 11-36 Programming with R: The Basics....Pages 37-78 R Packages....Pages 79-110 Objects....Pages 111-138 Basic Data and Computations....Pages 139-236 Data Visualization and Graphics....Pages 237-288 Computing with Text....Pages 289-330 New Classes....Pages 331-380 Methods and Generic Functions....Pages 381-410 Interfaces I: Using C and Fortran....Pages 411-428 Interfaces II: Between R and Other Systems....Pages 429-452 How R Works....Pages 453-474 Errata and Notes for “Software for Data Analysis: Programming with R”....Pages 499-504 Back Matter....Pages 1-26 John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved. ± This book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stated, starting with simple functions. ... The techniques covered include such modern programming enhancements as classes and methods, namespaces, and interfaces to spreadsheets or databases, as well as computations for data visualization, numerical methods, and the use of text data. » -- Quatrième de couverture "This book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stated, starting with simple functions. ... The techniques covered include such modern programming enhancements as classes and methods, namespaces, and interfaces to spreadsheets or databases, as well as computations for data visualization, numerical methods, and the use of text data."--Page 4 of cover