R (programming language)

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The R language was originally made for statistics.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.
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 codice_1. Although codice_2 can be used instead.
&gt; x &lt;- 1:6 # Create vector.
&gt; y &lt;- x^2 # Create vector by formula.
&gt; print(y) # Print the vector’s contents.
[1] 1 4 9 16 25 36
&gt; mean(y) # Arithmetic mean of vector.
[1] 15.16667
&gt; var(y) # Sample variance of vector.
[1] 178.9667
&gt; model &lt;- lm(y ~ x) # Linear regression model y = A + B * x.
&gt; print(model) # Print the model’s results.
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x 
 -9.333 7.000
&gt; 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(&gt;|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
&gt; par(mfrow = c(2, 2)) # Create a 2 by 2 layout for figures.
&gt; plot(model) # Output diagnostic plots of the model.