This book is often called the ``Blue Book'', and introduced what is now known as S version 2.

This is also called the ``White Book'', and introduced S version 3, which added structures to facilitate statistical modeling in S.

This ``Green Book'' describes version 4 of S, a major revision of S designed by John Chambers to improve its usefulness at every stage of the programming process.

A highly recommended book on how to do statistical data analysis using R or S-Plus. In the first chapters it gives an introduction to the S language. Then it covers a wide range of statistical methodology, including linear and generalized linear models, non-linear and smooth regression, tree-based methods, random and mixed effects, exploratory multivariate analysis, classification, survival analysis, time series analysis, spatial statistics, and optimization. The `on-line complements' available at the books homepage provide updates of the book, as well as further details of technical material.

This provides an in-depth guide to writing software in the S language which forms the basis of both the commercial S-Plus and the Open Source R data analysis software systems.

Integrates theory of statistics with the practice of statistics through a collection of case studies (``labs''), and uses R to analyze the data.

A comprehensive guide to the use of the `nlme' package for linear and nonlinear mixed-effects models.

This book, written in Spanish, is oriented to researchers interested in applying multivariate analysis techniques to real processes. It combines the theoretical basis with applied examples coded in R.

A companion book to a text or course on applied regression (such as ``Applied Regression, Linear Models, and Related Methods'' by the same author). It introduces S, and concentrates on how to use linear and generalized-linear models in S while assuming familiarity with the statistical methodology.

The first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion of topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results.

A contemporary presentation of statistical methods featuring 200 graphical displays for exploring data and displaying analyses. Many of the displays appear here for the first time. Discusses construction and interpretation of graphs, principles of graphical design, and relation between graphs and traditional tabular results. Can serve as a graduate-level standalone statistics text and as a reference book for researchers. In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic regression, and ARIMA time series modeling. Concepts and techniques are illustrated with a variety of case studies. S-Plus, R, and SAS executable functions are provided and discussed. S functions are provided for each new graphical display format. All code, transcript and figure files are provided for readers to use as templates for their own analyses.

There are few books covering introductory statistics using R, and this book fills a gap as a true ``beginner'' book. With emphasis on data analysis and practical examples, `Using R for Introductory Statistics' encourages understanding rather than focusing on learning the underlying theory. It includes a large collection of exercises and numerous practical examples from a broad range of scientific disciplines. It comes complete with an online resource containing datasets, R functions, selected solutions to exercises, and updates to the latest features. A full solutions manual is available from Chapman & Hall/CRC.

This paper would be a short introduction and overview about the language and environment for statistical analysis R, without entering in specific details too much computational. I give a look about this opensource software pointing out its main features, its functionalities, its pros and cons describing some libraries and the kind of analysis they support. I supply a summary, with a short description, about many resources concerning R that can be found in the Web: the most are in English language, but there are also some in the Italian language. The aim of this work is to contribute in increasing of the use of the R environment in Italy among statistical researchers trying to ``advertise'' this software and its opensource philosophy.