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programming language for statistical analysis From Wikipedia, the free encyclopedia
R is a programming language and free software environment for statistics.[6][7][8][9][10][11] 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.
Paradigms | Multi-paradigm: Array programming, object-oriented, imperative, functional |
---|---|
Designed by | Ross Ihaka and Robert Gentleman (statistician) |
Developer | R Core Team[1] |
First appeared | August 1993[2] |
Stable release | 4.4.2[3] (codename "Pile of Leaves") / 31 October 2024 |
License | GNU GPL v2[4] |
Filename extensions |
|
Website | www |
Influenced by | |
Influenced | |
Julia[5] | |
|
The R language was originally made for statistics. But today, it is also used in many scientific fields including ecology.[12][13]
A list of changes in R releases is maintained in various "news" files at CRAN (Comprehensive R Archive Network).[14] 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.[15] CRAN is started on this date, with 3 mirrors that initially hosted 12 packages.[16] |
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.[17] |
1.0 | 2000-02-29 | The developers declared that it is stable enough for production use.[18] |
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[19] |
2.11 | 2010-04-22 | Support for Windows 64 bit systems. |
2.12.2 | 2011-02-25 | Last version to support Windows 2000[20] |
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 |
R has local communities worldwide for users to share ideas and learn.[21][22]
There are a growing number of R events bringing its users together, such as conferences (e.g. useR!, WhyR?, conectaR, SatRdays)[23][24] and other meetups.[25]
The official annual gathering of R users is called "useR!".[26] The first such event was useR! 2004 in May 2004, Vienna, Austria.[27] After skipping 2005, the useR! conference has been held annually.[28] Subsequent conferences have included:[26]
Future conferences planned are as follows:[26][29]
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.
The following examples illustrate the basic syntax of the language and use of the command-line interface.
In R, the generally preferred[30] assignment operator is an arrow made from two characters <-
. Although =
can be used instead.[31]
> 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.
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