Bøker i Chapman & Hall/CRC: The R Series-serien

Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.

This book helps readers choose the right statistical method to analyze their sensory data. Each chapter presents the nature of the sensory evaluation and its objectives, the sensory particularities related to the sensory evaluation, details about the data set obtained, and the statistical analyses required. Using real examples, the authors then illustrate step by step how the analyses are performed in R. The chapters conclude with variants and extensions of the methods.

Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of the methodology, this book brings together the theoretical and methodological aspects of MFA. It also covers principal component analysis, multiple correspondence analysis, factor analysis for mixed data, hierarchical MFA, and more. The book also includes examples of applications and details on how to implement MFA using an R package, with the data and R scripts available online.

This practical guide shows how to use R and its add-on packages to obtain numerical solutions to complex mathematical problems commonly faced by scientists and engineers. Providing worked examples and code, the text not only addresses necessary aspects of the R programming language but also demonstrates how to produce useful graphs and statistically analyze and fit data to linear and nonlinear models. It covers Monte Carlo, stochastic, and deterministic methods and explores topics such as numerical differentiation and integration, interpolation and curve fitting, and optimization.

This book provides a practical, easy-to-understand guide to carrying out multilevel regression/growth curve analysis (GCA) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. With a minimum of statistical theory and technical jargon, the author focuses on the concrete issue of applying GCA to behavioral science data and individual differences. Throughout the book, R code illustrates how to implement the analyses and generate the graphs. The example datasets, code, and more are available on the author¿s website.

This work covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. Each part presents contributions from leaders who have developed software and other products that have advanced the field, including Sweave, open source software packages, and good programming practices. Supplementary material is available online.

Focusing on graphic user interfaces (GUIs) within the R language, this book shows programmers and users how to develop their own GUIs, enabling them to interface with other languages. The text opens the possibilities of R 's huge and growing set of statistical methods. The authors cover four different packages for writing GUIs: gWidgets, RGtk2, Qt, and Tcl Tk. Supported by a package in CRAN that contains all of the code along with additional examples, the text is filled with numerous examples ranging from the very simple to detailed illustrations of how to code actual interfaces.

Showing how data mining can improve the performance of organizations, this book and its R-based software provide the skills and tools needed to successfully develop advanced analytics capabilities. It explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. The authors also give insight into some of the challenges faced when deploying these tools. Readers can access a powerful, GUI-enhanced customized R package online as well as example data sets on the book¿s website.

This book demystifies the computational aspects of actuarial science, showing that even complex computations can usually be done without too much trouble. Using simple R code, the book helps readers understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. Datasets used in the text are available in an R package (CASdatasets).

This book demystifies the computational aspects of actuarial science, showing that even complex computations can usually be done without too much trouble. Using simple R code, the book helps readers understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. Datasets used in the text are available in an R package (CASdatasets).

This book explains the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book¿s collection of projects, exercises, and sample solutions encompass practical topics pertaining to data processing and analysis. The book can be used for self-study or as supplementary reading in a statistical computing course, allowing students to gain valuable data science skills.

This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used.

This book is designed to introduce the concept of advanced business analytic approaches and would the first to cover the gamut of how to use the R programming language to apply descriptive, predictive, and prescriptive analytic methodologies for problem solving.

With new chapters on ODEs and Markov chains, the second edition of this highly recommended, best-selling book introduces scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data. Requiring no prior knowledge of programming or probability, the book shows them how to turn algorithms into code. It includes case studies that demonstrate the simulation techniques as well as numerous student projects and exercises.

"This book describes interactive data visualization using the R package plotly. It focuses on tools and techniques that data analysts should find useful for asking follow-up questions from their data using interactive web graphics. A basic understanding of R is assumed"--

This third edition of Paul Murrell's classic book on using R for graphics represents a major update, with a complete overhaul in focus and scope. It focuses primarily on the two core graphics packages in R - graphics and grid - and has a new section on integrating graphics. This section includes three new chapters: importing external images in to R; integrating the graphics and grid systems; and advanced SVG graphics. The emphasis in this third edition is on having the ability to produce detailed and customised graphics in a wide variety of formats, on being able to share and reuse those graphics, and on being able to integrate graphics from multiple systems. This book is aimed at all levels of R users. For people who are new to R, this book provides an overview of the graphics facilities, which is useful for understanding what to expect from R's graphics functions and how to modify or add to the output they produce. For intermediate-level R users, this book provides all of the information necessary to perform sophisticated customizations of plots produced in R. For advanced R users, this book contains vital information for producing coherent, reusable, and extensible graphics functions.

Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.

This book explains how to use stated preference survey methods to measure people¿s preferences based on decision making in hypothetical choice situations. Along with giving introductory explanations of the methods, the book collates information on existing R functions and packages as well as those prepared by the authors. It focuses on core SP methods, including contingent valuation, discrete choice experiments, and best¿worst scaling. All the example data sets and code are available online.

Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. This second edition continues to encompass the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. It includes R code for all examples and R notes to help explain the R programming concepts. This edition also features a new chapter on nonparametric regression and smoothing.

Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.

Up-to-Date Guidance from One of the Foremost Members of the R Core Team Written by John M. Chambers, the leading developer of the original S software, Extending R covers key concepts and techniques in R to support analysis and research projects. It presents the core ideas of R, provides programming guidance for projects of all scales, and introduces new, valuable techniques that extend R. The book first describes the fundamental characteristics and background of R, giving readers a foundation for the remainder of the text. It next discusses topics relevant to programming with R, including the apparatus that supports extensions. The book then extends R's data structures through object-oriented programming, which is the key technique for coping with complexity. The book also incorporates a new structure for interfaces applicable to a variety of languages. A reflection of what R is today, this guide explains how to design and organize extensions to R by correctly using objects, functions, and interfaces. It enables current and future users to add their own contributions and packages to R. A 2017 Choice Outstanding Academic Title

This book provides a general introduction to the R Commander graphical user interface (GUI) to R for readers who are unfamiliar with R. It is suitable for use as a supplementary text in a basic or intermediate-level statistics course. It is not intended to replace a basic or other statistics text but rather to complement it, although it does promote sound statistical practice in the examples. The book should also be useful to individual casual or occasional users of R for whom the standard command-line interface is an obstacle.tinyurl.com/RcmdrBookThe site includes data files used in the book and an errata list.http://socserv.mcmaster.ca/jfox/Books/RCommander/Writing-Rcmdr-Plugins.pdfWriting R Commander Plug-in Packages

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.