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.
The R language was originally made for statistics.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.
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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[...] we recommend the consistent use of the preferred assignment operator '<-' (rather than '=') for assignment.