Statistical analysis should know by every software engineer. R is an open source statistical programming language. SAS is licensed analysis suite for statistics. The two are very much popular in Machine learning and data analytics projects.
SAS is analysis suite software and R is a programming language
- R supports both statistical analysis and Graphics
- R is an open source project.
- R is 18th most popular Language
- R packages are written in C, C++, Java, Python and.Net
- R is popular in Machine learning, data mining and Statistical analysis projects.
- SAS is a statistical analysis suite.
- Developed to process data sets in mainframe computers.
- Later developed to support multi-platforms. Like Mainframe, Windows, and Linux
- SAS has multiple products. SAS/ Base is very basic level.
- SAS is popular in data related projects.
Learn SAS vs R
Top Differences between SAS Vs R Programming
- The data integration from any data source is faster in SAS.
- The licensed software suite, so you will get support from SAS organization for any issues.
- SAS has multiple products. Most popular in creating reports and statistical analysis.
- Best suited for data-oriented projects.
- Mining of text is hard in SAS.
- Graphical visualization is not present in SAS.
- SAS is not suitable for Machine learning projects.
- The SAS software is expensive.
- SAS studio is a useful tool to work on it.
- R is flexible since a lot of packages are available.
- R is best suited for data related projects and Machine learning.
- Less cost since it is open source language.
- R Studio is the best tool to develop R programming modules.
Ref: imartcus.org (read more advantages)
- R language architecture model is out of date. So may not use it for critical applications.
- R is not suitable for Server programming, due to lack of security.
- R code you cannot use in web browsers.