R language in Data Science is a programming language and software environment for statistical computing and graphics.
The R language is widely used among statisticians and data miners for developing statistical software and data analysis.
R is a GNU package which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues.
R in Data Science
R’s a great choice for basic data analysis and visualization work and is widely used by Data Scientists – the other competing data science language is Python.
R is considered the more “authentic” data science and statistical programming language compared to Python – it has a greater depth of statistical functionality.
Why use R?
R provides a powerful, extensible environment, and as noted above, it has a wide range of statistics and data visualization capabilities.
R is easy to install and use, and it’s free – downloadable from the CRAN R project website.
R in Microsoft
R is now embedded in Microsoft SQL 2016 and Azure and hence is growing in popularity in a more corporate environment where MS SQL is widely used.
R’s power, flexibility and price (it is free) likely contribute to its popularity among researchers across scientific disciplines. Users around the world are consistently writing new programs, called packages, to carry out data analytic tasks that range from very simple to exceedingly complex. If there is a statistical method, somebody has written an R program to carry it out.
With R Server being able to run both on-premises and in the cloud on Azure Virtual Machines, you can model on-premises and score in the cloud without the need for rewrites.
Microsoft R Server is built to handle massive data sizes and computation on hundreds of nodes. Its big-data capable architecture includes ScaleR algorithms optimised for fast parallel execution, DistributedR parallel computing framework for managing compute resources, and ConnectR for versatile connections to data sources.