R and the R Project

The R-Project: a bit of history

R is a programming environment for data analysis, graphics and statistical computing. The R language is widely used among statisticians for developing statistical software and data analysis.

R was initially developed in early 90s by Robert Gentleman and Ross Ihaka at the Department of Statistics of the University of Auckland as a dialect of the S language.

The R name is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of S.

What is S and a bit of history

S is a statistical programming language developed by John Chambers and others in Bell Laboratories.

A bit of history:

  • 1976: the first version of S was developed as an internal statistical analysis environment. It was originally implemented as Fortran libraries.
  • 1980: the first version of S distributed outside of Bell Laboratories. In 1981, source version were made available.
  • 1984: Richard A. Becker and John M. Chambers, “S. An Interactive Environment for Data Analysis and Graphics”. (Brown Book). Historical interest only.
  • 1988: Richard A. Becker, John M. Chambers and Allan R. Wilks, “The New S Language”. London: Chapman & Hall. (Blue Book). It introduced what is now known as S version 2. The system was rewritten in C and began to resemble the system that we have today.
  • 1992: John M. Chambers and Trevor J. Hastie, “Statistical Models in S”. (White Book). It introduced S version 3, often abbreviated S3, which added structures to facilitate statistical modeling in S.
  • 1998: John M. Chambers, “Programming with Data”. (Green Book). It introduced S version 4, often abbreviated S4, which provided advanced object-oriented features. S4 classes differ markedly from S3 classes.

The S language itself has not changed dramatically since 1998.

What is S-PLUS and a bit of history

S-PLUS is a commercial implementation of the S programming language.

S-PLUS provides a number of fancy features (GUIs, mostly) on top of it, hence the “PLUS”.

A bit of history:

  • 1993: Statistical Sciences, Inc. acquires the exclusive license to distribute S and merges with MathSoft.
  • 2001: MathSoft sells its Cambridge-based Engineering and Education Products Division (EEPD). It changes name to Insightful Corporation.
  • 2004: Insightful purchases the S language from Lucent Technologies for $2 million.
  • 2008: TIBCO acquires Insightful Corporation.

R: a bit of history

  • 1993: First announcement of R to the public.
  • 1995: Martin Maechler convinces Ross Ihaka and Robert Gentleman to use the GNU General Public License to make R free software.
  • 1997: The R Development Core Team is formed. The team controls the source code for R.
  • 2000: R version 1.0.0 released. Developers considered R stable enough for production use.
  • 2004: R version 2.0.0 released. Introduced lazy loading, which enables fast loading of data with minimal expense of system memory.
  • 2013: R version 3.0.0 released. Introduced long vectors.

The R-project and R licence

R is supported by a wide community of academic users, professors, companies and developers. This community composes the so-called “R-project”. The “R-project” is supported by the “R Foundation”. The R Foundation is a not for profit organisation.

R is an official part of the Free Software Foundation’s GNU project. The R Foundation has similar goals to other open source software foundations like the Apache Foundation or the GNOME Foundation. R is free and open source software. It is released under the GPL (version 2) licence.

R is free:

  • you can have R without paying for it (freeware);
  • you can copy and re-use the software (free software);
  • you can access source code and modify it (open source).

R Commercial Support

Revolution R

Revolution Analytics (www.revolutionanalytics.com) was founded in 2007 to provide commercial support for Revolution R. Revolution R is the distribution of R developed by Revolution Analytics which also includes components developed by the company.

Revolution R Enterprise includes all of R’s advanced data analysis and graphics capabilities, plus additional components. Major additional components include: ParallelR (for parallel computing), the R Productivity Environment IDE, RevoScaleR (for big data analysis), RevoDeployR (web services framework and the ability for reading and writing data in the SAS file format).

What R does?

R provides a suite of software facilities for:

  • matrix algebra;
  • hash tables and regular expressions;
  • reading and manipulating data;
  • computation;
  • programming language: loops, subroutines, functions, etc.;
  • conducting statistical analyses;
  • graphics and tables;
  • displaying the results.

On the contrary, R:

  • it is not a database, but it connects to databases;
  • it does not provide a graphical interface, but it uses Java, TclTk and, under Windows, COM to provide graphical interfaces;
  • it is not a spreadsheet, but it connects to spreadsheets;
  • it does not provide commercial support. Revolution R is a commercially supported distribution of R.

In conclusion, R is an interpreted computer language. R provides a platform for the development and implementation of new algorithms and technology transfer. Most user-visible functions are written in R itself, calling upon a smaller set of internal primitives. It is possible to interface procedures written in C, C+, or FORTRAN languages for efficiency, and to write additional primitives. System commands can be called from within R.

R advantages and disadvantages

Main R advantages are:

  • Fast and free.
  • State of the art: Statistical researchers provide their methods as R packages. SPSS and SAS are years behind R!
  • Excellent for graphics.
  • Mx, WinBugs, and other programs use or will use R.
  • Active user community.
  • Excellent for simulation, programming, computer intensive analyses, etc.
  • Forces you to think about your analysis.
  • Interfaces with database storage software (SQL).

Main R disadvantages are:

  • Not user friendly at start: steep learning curve, minimal GUI.
  • Sometimes, figuring out correct methods or how to use a function on your own can be frustrating.
  • Easy to make mistakes and not know.
  • Working with large datasets is limited by RAM.
  • Data preparation and cleaning can be messier and more mistake prone in R vs SPSS or SAS.

R Resources

R-project website

The R-project website (www.r-project.org) is the starting point for R materials.

The website contains:

  • the software and packages;
  • the search engine interface (the same queries can be submitted with the RSiteSearch(‘query’) function within R);
  • the on-line documentation both in HTML and in PDF format. The HTML version can be accessed with the help.start() function within R;
  • the R Journal. The R Journal is the open access, refereed journal of the R project. It features short to medium length articles covering topics that might be of interest to users or developers of R;
  • the interface to the mailing list;
  • the wiki, suggested books and many others.

The on-line documentation includes the following manuals. These manuals have been written by the R Development Core Team itself and contain precious information.

  • An Introduction to R gives an introduction to the language and how to use R for doing statistical analysis and graphics.
  • Writing R Extensions covers how to create your own packages, write R help files, and the foreign language (C, C++, Fortran, …) interfaces.
  • R Data Import/Export describes the import and export facilities available either in R itself or via packages which are available from CRAN.
  • R Installation and Administration.

Other manuals and tutorials provided by R users can be downloaded from the R-project website (cran.r-project.org/other-docs.html).

Mailing lists is the most important tool to contact the R community. Mailing lists can be accessed from the R-project website (www.r-project.org/mail.html).

There are four general mailing lists devoted to R:

  • R-announce: This list is for major announcements about the development of R and the availability of new code.
  • R-packages: This list is for announcements as well, usually on the availability of new or enhanced contributed packages (on CRAN, typically).
  • R-help: The “main” R mailing list, for discussion about problems and solutions using R, announcements about the availability of new functionality for R and documentation of R, comparison and compatibility with S-plus, and for the posting of nice examples and benchmarks.
  • R-devel: This list is intended for questions and discussion about code development in R.

Other on-line resources

It is very difficult estimate how many sites about R are on-line. However, Google returns 224.000.000 sites searching “R stat blog”. Also if only the 0.1% of these sites talk about R, it means almost 220.000 sites about R.

R-bloggers (www.r-bloggers.com) is a blog aggregator of content collected from bloggers who write about R. R-bloggers contains R news and tutorials contributed by hundreds of R bloggers.

Other useful websites about R are:


A partially annotated list of books that are related to S or R may be found in the R-project website (www.r-project.org/doc/bib/R-books.html).

The following book may be considered the milestone book about R: – William N. Venables and Brian D. Ripley. Modern Applied Statistics with S. Fourth Edition. Springer, New York, 2002. ISBN 0-387-95457-0.

Other suggested books are:

  • Everitt and Hothorn (2009). A handbook of statistical analyses using R. Chapman & Hall/CRC.
  • Chambers (2008). Software for Data Analysis, Springer.
  • Chambers (1998). Programming with Data, Springer.
  • Murrell (2005). R Graphics, Chapman & Hall/CRC Press.
  • Dalgard (2002). Introductory Statistics with R. Springer.
  • Kabakoff (2011). R in Action. Manning.
  • Braun and Murdoch (2007). A First Course in Statistical Programming with R. Cambridge University Press.

Springer is developing a series of books called Use R!.