Software for data analysis. Programming with R R is an open-source software system that is widely used for computing with data. It provides a general language that is a version of the S language whose development commenced more than thirty years ago, at the Bell Labs, by a group of five to which the author belongs. This text is about using computer software, in particular R, for obtaining information from the data at hand, namely by organising, exploring, visualising, modelling, and so on. The book “is aimed at those who need to select, modify, and create software to explore data, in other words, to program.” As the book makes clear, everything in R is an object and the central computation is a function call. Following an introductory chapter that presents the author’s two guiding principles: the mission to explore and the responsibility to develop trustworthy data analysis, there are chapters on using R; the basics of programming with R; R packages; and managing objects. Chapter 6, on basic data and computations, is about a fifth of the length of the book, and is followed by chapters on data visualization and graphics, and computing with text. The remaining chapters are concerned with new classes; methods and generic functions; interfaces with C and Fortran; interfaces to other systems; how R works. There are many cross- references between the chapters, encouraging browsing, and a short appendix outlines the history of the language. The book has a companion R package, SoDA, that contains the code for most of the examples, as well as a number of the functions and classes developed in the text. The book is aimed at (i) data analysts, namely anyone involved in exploring data, from data arising in scientific research to, say, data collected by the tax office; (ii) researchers in, and teachers of, statistical techniques and theory; (iii) those primarily interested in software and programming. (Source:

References in zbMATH (referenced in 15 articles )

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  1. Borcard, Daniel; Gillet, François; Legendre, Pierre: Numerical ecology with R (2018)
  2. Kleiber, Christian: Book review of: J. M. Chambers, Extending R (2017)
  3. Chambers, John M.: Object-oriented programming, functional programming and \textttR (2014)
  4. Lawrence, Michael; Morgan, Martin: Scalable genomics with \textttRand bioconductor (2014)
  5. Sánchez Lasheras, F.; García Nieto, P. J.; de Cos Juez, F. J.; Vilán Vilán, J. A.: Evolutionary support vector regression algorithm applied to the prediction of the thickness of the chromium layer in a hard chromium plating process (2014)
  6. Kalina, Jan: Implicitly weighted methods in robust image analysis (2012)
  7. Fuentes, Montserrat; Xi, Bowei; Cleveland, William S.: Trellis display for modeling data from designed experiments (2011)
  8. Vasishth, Shravan; Broe, Michael: The foundations of statistics: A simulation-based approach (2011)
  9. Zhang, Hexin; Fang, Xiangzhong; Ma, Xiaojing: Group contingency test for two or several independent samples (2011)
  10. De Cos Juez, F. J.; Nieto, P. J. García; Torres, J. Martínez; Castro, J. Taboada: Analysis of lead times of metallic components in the aerospace industry through a supported vector machine model (2010) ioport
  11. Kohl, Matthias; Ruckdeschel, Peter; Rieder, Helmut: Infinitesimally robust estimation in general smoothly parametrized models (2010)
  12. Wollschläger, Daniel: Foundations of data analysis with R. An application oriented introduction. (2010)
  13. Ramsay, J. O.; Hooker, Giles; Graves, Spencer: Functional data analysis with R and MATLAB (2009)
  14. Chambers, John M.: Software for data analysis. Programming with R (2008)
  15. Cadima, Jorge; Cerdeira, J. Orestes; Minhoto, Manuel: Computational aspects of algorithms for variable selection in the context of principal components (2004)