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R
R logo.svg
R terminal.jpg
R terminal
Paradigms Multi-paradigm: Array programming, object-oriented, imperative, functional
Designed by Ross Ihaka and Robert Gentleman (statistician)
Developer R Core Team
First appeared August 1993; 27 years ago (1993-08)
Stable release 4.0.2 ("Taking Off Again") / June 22, 2020; 8 months ago (2020-06-22)
License GNU GPL v2
Filename extensions
  • .r
  • .rdata
  • .rds
  • .rda
Influenced by
Influenced
Julia

R is a programming language and free software environment for statistics. R is a language built for a specific purpose. It is strictly designed for statistical analysis. The algorithms for many statistical models are devised in R. Precisely R is the language of Statistical Analyzers. It’s an open source and the best suite for the statisticians to develop statistical softwares. R is putting utmost efforts to walk parallelly to Python.

Usage in other areas

The R language was originally made for statistics. But today, it is also used in many scientific fields including ecology.

Development history

A list of changes in R releases is maintained in various "news" files at CRAN (Comprehensive R Archive Network). Some highlights are listed below for several major releases.

Release Date Description
0.16 This is the last test version.
0.49 1997-04-23 This is the oldest source release which is currently available on CRAN. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages.
0.60 1997-12-05 R becomes an official part of the GNU Project. The code is hosted and maintained on CVS.
0.65.1 1999-10-07 First versions of update.packages and install.packages functions for downloading and installing packages from CRAN.
1.0 2000-02-29 The developers declared that it is stable enough for production use.
1.4 2001-12-19 S4 methods are introduced and the first version for Mac OS X is made available soon after.
1.8 2003-10-08 Introduced a flexible condition handling mechanism for signalling and handling condition objects.
2.0 2004-10-04 Introduced fast loading of data with minimal expense of system memory.
2.1 2005-04-18 Support for UTF-8 encoding. They also started of internationalization and localization for different languages.
2.6.2 2008-02-08 Last version to support Windows 95, 98, Me and NT 4.0
2.11 2010-04-22 Support for Windows 64 bit systems.
2.12.2 2011-02-25 Last version to support Windows 2000
2.13 2011-04-14 Adding a new compiler function that allows speeding up functions by converting them to byte-code.
2.14 2011-10-31 Added mandatory namespaces for packages. Added a new parallel package.
2.15 2012-03-30 New load balancing functions. Improved serialization speed for long vectors.
3.0.0 2013-04-03 Support for numeric index values 231 and larger on 64 bit systems.
3.3.3 2017-03-06 Last version to support Microsoft Windows XP.
3.4.0 2017-04-21 Just-in-time compilation (JIT) of functions and loops to byte-code enabled by default.
3.5.0 2018-04-23 Packages byte-compiled on installation by default. Compact internal representation of integer sequences. Added a new serialization format to support compact internal representations.
3.6.0 2019-04-26
4.0.0 2020-04-24

Communities

R has local communities worldwide for users to share ideas and learn.

There are a growing number of R events bringing its users together, such as conferences (e.g. useR!, WhyR?, conectaR, SatRdays) and other meetups.

useR! conferences

The official annual gathering of R users is called "useR!". The first such event was useR! 2004 in May 2004, Vienna, Austria. After skipping 2005, the useR! conference has been held annually. Subsequent conferences have included:

Future conferences planned are as follows:

The R Journal

The R Journal is the open access refereed journal of the R project. It features articles on the use and development of the R language.

Basic syntax

The following examples illustrate the basic syntax of the language and use of the command-line interface.

In R, the generally preferred assignment operator is an arrow made from two characters <-. Although = can be used instead.

> x <- 1:6  # Create vector.
> y <- x^2  # Create vector by formula.
> print(y)  # Print the vector’s contents.
[1]  1  4  9 16 25 36

> mean(y)  # Arithmetic mean of vector.
[1] 15.16667

> var(y)  # Sample variance of vector.
[1] 178.9667

> model <- lm(y ~ x)  # Linear regression model y = A + B * x.
> print(model)  # Print the model’s results.

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept)            x 
     -9.333        7.000

> summary(model)  # Display an in-depth summary of the model.

Call:
lm(formula = y ~ x)

Residuals:
      1       2       3       4       5       6
 3.3333 -0.6667 -2.6667 -2.6667 -0.6667  3.3333

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  -9.3333     2.8441  -3.282 0.030453 * 
x             7.0000     0.7303   9.585 0.000662 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.055 on 4 degrees of freedom
Multiple R-squared:  0.9583, Adjusted R-squared:  0.9478
F-statistic: 91.88 on 1 and 4 DF,  p-value: 0.000662

> par(mfrow = c(2, 2))  # Create a 2 by 2 layout for figures.
> plot(model)  # Output diagnostic plots of the model.

Diagnostic plots from plotting “model” (q.v. “plot.lm()” function). Notice the mathematical notation allowed in labels (lower left plot).



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