SAS vs R - Which is the best statistical language?
When it comes to statistical computing, SAS and R are two of the most popular programming languages that come to mind. Both SAS and R are used extensively in the field of data analytics and statistical computing. In this article, we will compare both languages based on various parameters and try to answer the most common question - Which is the best statistical language?
Popularity
R is an open-source programming language, which means it is free to use and can be modified by anyone. As a result, R has gained a lot of popularity over the years and has a large user community. According to a survey conducted in 2020 by Kaggle, R was the most popular language among data scientists, with 47% of respondents using it. On the other hand, SAS is a commercial language, and it is not free to use. As a result, it has a comparatively smaller user community.
Learning Curve
R is relatively easy to learn and has a simpler syntax compared to SAS. It has a steep initial learning curve, but once you get past that, it becomes much easier to use. SAS, on the other hand, has a steeper learning curve due to its complex syntax and the need to learn specific procedures. However, SAS provides a lot of documentation and resources, which makes learning easier.
Features
Both languages have some unique features. For example, SAS has built-in functions for data cleaning and manipulation. It is also easier to generate reports and visualizations in SAS due to its integration with other SAS tools. R, on the other hand, is known for its wide range of statistical models and packages.
Performance
When it comes to performance, SAS is considered to be faster than R. SAS is optimized for handling large datasets, and it uses less memory than R. However, R has made significant improvements in the past few years, and it now provides good performance for most tasks.
Cost
As mentioned earlier, SAS is a commercial language, and it is not free to use. The cost of SAS can vary depending on the intended use, but it is generally expensive. On the other hand, R is free to use and can be modified as per your needs.
Conclusion
Both SAS and R are powerful statistical programming languages, and the choice between them depends on your specific requirements. R is a better choice for data scientists who work with smaller datasets and need access to a wide range of statistical models. SAS is a better choice for enterprises dealing with larger datasets and who need a complete toolset for data management, analysis, and visualization.
References
- Kaggle Survey 2020: https://www.kaggle.com/kaggle-survey-2020
- SAS vs. R - Outline by AnalyticSoftLab: https://www.analyticsvidhya.com/blog/2015/02/sas-vs-r-make-decision-right-choice/
- R Faster Than SAS! A Question, Not A Title: https://www.r-bloggers.com/2020/09/r-faster-than-sas-a-question-not-a-title/