Sunday, February 23, 2020

What is R-Programming and Why learning R is important?


R is an Open Sourced powerful programming language and also known as an Advanced Statistical language which was developed in 1993 by Robert Gentleman and Ross Ihaka in the University of Auckland, Auckland, New Zealand. R is cross-plateform compatible, so it can be installed on Windows, MAC OSX and Linux. Due to having fantastic features, it is extensively used by Software Programmers, Statisticians, Data Scientists, and Data Miners. 
It is mostly used for statistical computing and graphics supported by the R Foundation for Statistical Computing. So, it is one of the most popular analytics tool used in Data Analytics and Business Analytics.


Why learning R is important?
There are many important features which force us to learn this mind blowing language as mentioned below -
  • It is a free and open-source programming language issued under GNU (General Public License).
  • It’s easy to create R packages for solving particular problems
  • It has cross-platform interoperability which means that it has distributions running on Windows, Linux, and Mac. R code can easily be ported from one platform to another.
  • It uses an interpreter instead of a compiler, which makes the development of code easier.
  • It effectively associates different databases, and it does well in bringing in information from Microsoft Excel, as well as, Microsoft Access, MySQL, SQLite, Oracle, etc.
  • It is a flexible language that bridges the gap between Software Development and Data Analysis.
  • It provides a wide variety of packages with the diversity of codes, functions, and features tailored for data analysis, statistical modeling, visualization, Machine Learning, and importing and manipulating data.
  • It integrates various powerful tools to communicate reports in different forms like CSV, XML, HTML, and pdf, and also through interactive websites, with the help of R packages.


Comparison with SAS, SPSS, and Stata
  • R is comparable to popular commercial statistical packages such as SAS, SPSS, and Stata, but R is available to users at no charge under a free software license.
  • In January 2009, the New York Times ran an article charting the growth of R, the reasons for its popularity among data scientists and the threat it poses to commercial statistical packages such as SAS.
  • In June 2017 data scientist Robert Muenchen published a more in-depth comparison between R and other software packages, "The Popularity of Data Science Software".
  • R is more procedural-code oriented than either SAS or SPSS, both of which make heavy use of pre-programmed procedures (called "procs") that are built-in to the language environment and customized by parameters of each call. R generally processes data in-memory, which limits its usefulness in processing extremely large files.

Commercial support for R
  • Although R is an open-source project supported by the community developing it, some companies strive to provide commercial support and/or extensions for their customers. This section gives some examples of such companies.
  • In 2007, Richard Schultz, Martin Schultz, Steve Weston and Kirk Mettler founded Revolution Analytics to provide commercial support for Revolution R, their distribution of R, which also includes components developed by the company.
  • Major additional components include: ParallelR, the R Productivity Environment IDE, RevoScaleR (for big data analysis), RevoDeployR, web services framework, and the ability for reading and writing data in the SAS file format.
  • In October 2011, Oracle announced the Big Data Appliance, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with Exadata hardware.
  • IBM offers support for in-Hadoop execution of R, and provides a programming model for massively parallel in-database analytics in R.
  • Tibco offers a runtime-version R as a part of Spotfire.
  • Mango Solutions offers a validation package for R, ValidR,to make it compliant with drug approval agencies, like FDA.

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Source : wikipedia 

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