Why is SAS and R programming used instead of MS excel?

SAS (Statistical Analysis System) and R programming are often preferred over Microsoft Excel in certain contexts due to their capabilities and strengths in statistical analysis, data manipulation, and programming. While Excel is a powerful tool for general data analysis and visualization, SAS and R offer advantages in specific areas:

1. Statistical Analysis:

   • SAS: It is a specialized statistical software suite widely used in academia and industries like healthcare and finance. SAS offers a comprehensive set of statistical procedures for advanced analytics, including regression analysis, analysis of variance, and multivariate analysis.
   • R Programming: R is a powerful open-source programming language and environment specifically designed for statistical computing and data analysis. It has a vast collection of packages for various statistical techniques, making it a favorite among statisticians and data scientists.

2. Large Datasets:

   • SAS: SAS is known for its efficiency in handling large datasets. It can process and analyze substantial amounts of data efficiently, making it suitable for enterprise-level applications and industries dealing with massive datasets.
   • R Programming: R is also capable of handling large datasets, and its flexibility in data manipulation and analysis is well-suited for tasks involving extensive datasets.

3. Programming and Automation:

   • SAS: SAS is a programming language in itself. Analysts and statisticians can write scripts for data analysis and automate processes, making it suitable for reproducibility and scalability.
   • R Programming: R is a scripting language, and its code is highly readable and modifiable. It allows for the creation of custom functions and scripts, enabling advanced automation and customization.

4. Community and Packages:

   • SAS: It has a strong user community and offers a range of industry-specific solutions. However, its proprietary nature may limit the availability of certain techniques and tools.
   • R Programming: R has a vibrant open-source community. The Comprehensive R Archive Network (CRAN) hosts thousands of packages, covering a wide range of statistical methods, machine learning algorithms, and data visualization techniques.

5. Cost Considerations:

   • SAS: SAS is a commercial software, and its licensing costs can be significant. This may be a factor for smaller organizations or individual users.
   • R Programming: R is open-source and free to use. This makes it an ideal option for users and organizations with budget constraints.

While Microsoft Excel is a versatile tool for many users, SAS and R programming are preferred when advanced statistical analysis, programming flexibility, and the ability to handle large datasets are critical requirements. The choice between these tools often depends on the specific needs and goals of the data analysis task at hand.