Введение в статистический пакет R: типы переменных, структуры данных, чтение и запись информации, графика

R - это одновременно и свободно распространяемая программная среда с открытым кодом, развиваемая в рамках проекта GNU, и язык программирования для статистической обработки данных и работы с графикой. R можно применять везде, где нужна работа с данными. Это и сама математическая статистика во всех её приложениях, и первичный анализ данных, и математическое моделирование. Основная мощь R лучше всего проявляется именно при статистическом анализе данных: от вычисления средних величин до серьёзных операций с временными рядами. С помощью R можно подготовить данные для исследования, которое может быть осуществлено с помощью реализованных в различных функциях статистических методов, а затем вывести полученные результаты для дальнейшего анализа. Сейчас практически во всех западноевропейских и американских университетах изучают и используют R, ежегодно издаются многостраничные учебники и монографии относительно как работы с самим пакетом R, так и его применения при исследовании и обработке данных в статистике, медицине, экологии, финансовом анализе, актуарной математике и пр. Многие компании также применяют R, например Boeing. В первой части описаны типы переменных, структуры данных, чтение и запись информации, графика.

Since its inception, R has become one of the preeminent programs for statistical computing and data analysis. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data.

In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks.

Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book.

Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions.

Phil Spector is Applications Manager of the Statistical Computing Facility and Adjunct Professor in the Department of Statistics at University of California, Berkeley.

r, An Open Source Software, Has Become The de Facto Statistical Computing Environment. It Has An Excellent Collection Of Data Manipulation And Graphics Capabilities. It Is Extensible And Comes With A Large Number Of Packages That Allow Statistical Analysis At All Levels – From Simple To Advanced – And In Numerous Fields Including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences And Much More. The Software Is Maintained And Developed By Academicians And Professionals And As Such, Is Continuously Evolving And Up To Date. statistics And Data With R Presents An Accessible Guide To Data Manipulations, Statistical Analysis And Graphics Using R.

assuming No Previous Knowledge Of Statistics Or R, The Book Includes:

• a Comprehensive Introduction To The R Language.

• an Integrated Approach To Importing And Preparing Data For Analysis, Exploring And Analyzing The Data, And Presenting Results.

• over 300 Examples, Including Detailed Explanations Of The R Scripts Used Throughout.

• over 100 Moderately Large Data Sets From Disciplines Ranging From Biology, Ecology And Environmental Science To Medicine, Law, Military And Social Sciences.

• a Parallel Discussion Of Analyses With The Normal Density, Proportions (binomial), Counts (poisson) And Bootstrap Methods.

• two Extensive Indexes That Include References To Every R Function (and Its Arguments And Packages Used In The Book And To Every Introduced Concept.

an Accompanying Wiki Website, Http://turtle.gis.umn.edu includes All The Scripts And Data Used In The Book. The Website Also Features A Solutions Manual, Providing Answers To All Of The Exercises Presented In The Book. Visitors Are Invited To Download/upload Data And Scripts And Share Comments, Suggestions And Questions With Other Visitors. Students, Researchers And Practitioners Will Find This To Be Both A Valuable Learning Resource In Statistics And R And An Excellent Reference Book.

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. The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis. Building on the success of the author's bestselling Statistics: An Introduction using R, The R Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines. Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities. Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences. R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels – from simple to advanced – and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. Statistics and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R. Assuming no previous knowledge of statistics or R, the book includes: A comprehensive introduction to the R language. An integrated approach to importing and preparing data for analysis, exploring and analyzing the data, and presenting results. Over 300 examples, including detailed explanations of the R scripts used throughout. Over 100 moderately large data sets from disciplines ranging from Biology, Ecology and Environmental Science to Medicine, Law, Military and Social Sciences. A parallel discussion of analyses with the normal density, proportions (binomial), counts (Poisson) and bootstrap methods. Two extensive indexes that include references to every R function (and its arguments and packages used in the book) and to every introduced concept. The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. Coupled with the large variety of easily available packages, it allows access to both well-established and experimental statistical techniques. However techniques that might make sense in other languages are often very ine?cient in R, but, due to Rs ?- ibility, it is often possible to implement these techniques in R. Generally, the problem with such techniques is that they do not scale properly; that is, as the problem size grows, the methods slow down at a rate that might be unexpected. The goal of this book is to present a wide variety of data - nipulation techniques implemented in R to take advantage of the way that R works,ratherthandirectlyresemblingmethodsusedinotherlanguages. Since this requires a basic notion of how R stores data, the ?rst chapter of the book is devoted to the fundamentals of data in R. The material in this chapter is a prerequisite for understanding the ideas introduced in later chapters. Since one of the ?rst tasks in any project involving data and R is getting the data into R in a way that it will be usable, Chapter 2 covers reading data from a variety of sources (text ?les, spreadsheets, ?les from other programs, etc. ), as well as saving R objects both in native form and in formats that other programs will be able to work with. The R Language Is Recognised As One Of The Most Powerful And Flexible Statistical Software Packages, And It Enables The User To Apply Many Statistical Techniques That Would Be Impossible Without Such Software To Help Implement Such Large Data Sets. Preface -- 1. Getting Started -- 2. Essentials Of The R Language -- 3. Data Input -- 4. Dataframes -- 5. Graphics -- 6. Tables -- 7. Mathematics -- 8. Classical Tests -- 9. Statistical Modelling -- 10. Regression -- 11. Analysis Of Variance -- 12. Analysis Of Covariance -- 13. Generalized Linear Models -- 14. Count Data -- 15. Count Data In Tables -- 16. Proportion Data -- 17. Binary Response Variables -- 18. Generalized Additive Models -- 19. Mixed-effects Models -- 20. Non-linear Regression -- 21. Tree Models -- 22. Time Series Analysis -- 23. Multivariate Statistics -- 24. Spatial Statistics -- 25. Survival Analysis -- 26. Simulation Models -- 27. Changing The Look Of Graphics -- References And Further Reading -- Index. Michael J. Crawley. Includes Bibliographical References (p. [873]-876) And Index. "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 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. Everyone using R needs to work with data, data almost always comes from an external source that has to be imported into R. The tasks covered in this book are essential tasks in R