R programming

 The R programming language is an open source scripting language for predictive analytics and data visualization.

The initial version of R was released in 1995 to allow academic statisticians and others with sophisticated programming skills to perform complex data statistical analysis and display the results in any of a multitude of visual graphics. The "R" name is derived from the first letter of the names of its two developers, Ross Ihaka and Robert Gentleman, who were associated with the University of Auckland at the time.

The R programming language includes functions that support linear modeling, non-linear modeling, classical statistics, classifications, clustering and more. It has remained popular in academic settings due to its robust features and the fact that it is free to download in source code form under the terms of the Free Software Foundation's GNU general public license. It compiles and runs on UNIX platforms and other systems including Linux, Windows and MacOS.

The appeal of the R language has gradually spread out of academia into business settings, as many data analysts who trained on R in college prefer to continue using it rather than pick up a new tool with which they are inexperienced.

The R software environment

The R language programming environment is built around a standard command-line interface. Users leverage this to read data and load it to the workspace, specify commands and receive results. Commands can be anything from simple mathematical operators, including +, -, * and /, to more complicated functions that perform linear regressions and other advanced calculations.

Users can also write their own functions. The environment allows users to combine individual operations, such as joining separate data files into a single document, pulling out a single variable and running a regression on the resulting data set, into a single function that can be used over and over.

Looping functions are also popular in the R programming environment. These functions allow users to repeatedly perform some action, such as pulling out samples from a larger data set, as many times as the user wants to specify.