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R is an open source mathematical computer application and scripting language that is widely used in the fields of operations research and statistics. It has significant statistical functionality.
R was was first developed in the Department of Statistics at the University of Auckland, New Zealand by Ross Ihaka and Robert Gentleman, although, as with most open source projects, a large group of people from its user community have contributed to its development by contributing code and reporting bugs. A core team of about 20 people currently maintain the R code for the R project.
The R language is based on the S language that was originally developed by Bell Laboratories. In many respects R is an open source implementation of S and a lot of code written for S runs unaltered under R.
Can I do this in R?
There are many user-created R libraries available, from statistics and graphing libraries with broad purposes to specific applications for various fields. For more on libraries/packages, see this page and the landing page for R packages.
R vs. SAS vs. other languages
Some R users argue that R is better than SAS and seek to convert SAS users to R. They argue that a single line of R code can do the same job that takes many lines of SAS code. They also claim that R is ahead of SAS in its statistical capabilities. However, this view is not universally held, with other users pointing out specific areas of weakness.
While R has been enthusiastically received by academia, the corporate world has not been so quick to adopt it. Although the R software is distributed freely, it still has a business cost. R has a steep learning curve and businesses that decide to use R will need to invest in ongoing staff training and support in order to keep using it. There is no vendor support, so any software maintenance and security risks need to be carried by the business.
Organizations that have adopted R note that while R has excellent analysis capabilities, it is not a total solution to their data management needs. While R can process a stand alone set of data very effectively, it needs to be used in conjunction with a relational database management system if one is using it in a data warehousing environment.
KDnuggets has data from a survey of analytic professionals that may provide some insight as to why one language is used over another. Prescient Healthcare lists reasons that SAS may be preferred to open source languages.
Whatever the case, it is important to consider the business/organizational need, experience of the team, and other advantages and disadvantages of each language.