Python is a programming language, used for much more than just statistics, but it's that: a programming language.
R is a statistics package, not more. Doing ETL with it is a very painful experience.
SAS is a complete data warehousing framework, covering everything, from ETL to reporting to data mining, providing a metadata repository, managing a development process, managing access (user, permissions), letting you build web applications in a rather easy way (stored processes). On top of that, its statistic procedures are certified for use in environments where such certification is mandatory (that's why it is so popular with the pharma industry).
Why SAS is still widely used while Python and R are so popular? Does it have some unique functions or properties?
Such a broad question. I give a very broad answer to your question: Yes.
Python is a programming language, used for much more than just statistics, but it's that: a programming language.
R is a statistics package, not more. Doing ETL with it is a very painful experience.
SAS is a complete data warehousing framework, covering everything, from ETL to reporting to data mining, providing a metadata repository, managing a development process, managing access (user, permissions), letting you build web applications in a rather easy way (stored processes). On top of that, its statistic procedures are certified for use in environments where such certification is mandatory (that's why it is so popular with the pharma industry).
One thing that SAS brings to the table is 50 years of code development. A great deal of code written in SAS 50 years ago will still run analysis with the current versions once the appropriate connections between SAS and the data storage is made. So there are a lot of examples and well tested code.
SAS runs on a wide variety of operating systems.
When SAS has problems there is an actual place you can get support.
My introduction to R left a very bad taste as in a class room setup by the teacher for 25 or so students half of the computers would not run one or more of the instructor designed examples. We were not working with big data sets and couldn't keep everything running for 5 days.
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