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 an Analysis-suite software and R is a programming language.**

## 1. R 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.

### a). R Advantages

- 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)*

### b). R Disadvantages

- R language architecture model is out of date.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.

## SAS

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.### a). SAS Advantages

- 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.

### b). SAS Disadvantages

- 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.

**References**

Tags
r-vs-sas