A Comprehensive Comparison Between R from Revolution Analytics and SAS: Which Tool Should You Choose?

In the realm of data analysis and statistical computing, two prominent tools stand out: R from Revolution Analytics and SAS. Both tools offer robust capabilities, but their design, application fields, and community support vary significantly. This article aims to provide a comprehensive comparison, helping you decide which tool is the best fit for your needs.

Introduction to R from Revolution Analytics

R from Revolution Analytics is an advanced version of the open-source programming language R, specifically designed to address the needs of enterprise-level users. Revolution Analytics enhances R with enterprise-grade features, making it a powerful choice for large organizations.

Introduction to SAS

SAS (Statistical Analysis System) is a comprehensive suite of software products and services used for advanced analytics, business intelligence, data management, and predictive analytics. SAS has been widely used in both academia and industry for decades.

Algorithmic Capabilities

R from Revolution Analytics is renowned for its flexibility and extensive package ecosystem, which includes a vast number of statistical and graphical techniques. Revolution Analytics additionally boasts advanced algorithms for machine learning, data mining, and predictive analytics. This makes R an excellent choice for practitioners seeking a wide array of computational tools.

SAS, on the other hand, is known for its robust data management capabilities, particularly in handling large datasets and maintaining data quality. SAS also includes a broad range of advanced statistical and analytical procedures, making it highly versatile for various business and research applications. Its strong integration with enterprise systems is a clear advantage.

Community and Support

The R from Revolution Analytics community is significant but less extensive compared to the massive community surrounding vanilla R. Revolution Analytics provides commercial support and training, covering all aspects of the R language and enterprise-level usage. This might be a compelling benefit for organizations looking for comprehensive support without the need to manage an open-source community.

In contrast, SAS has a well-established and extensive community. It offers extensive training, support, and a wide range of resources for learning and troubleshooting. SAS also has a dedicated online forum and a vast library of tutorials and case studies, making it accessible even for new users.

Integration and Deployment

R from Revolution Analytics offers excellent integration with cloud platforms, making it a great choice for modern enterprise environments. Revolution Analytics supports integration with various cloud providers, including Amazon Web Services (AWS) and Microsoft Azure, allowing seamless deployment and scalability.

SAS is known for its strong integration with enterprise systems and legacy applications. It can integrate with a wide range of business intelligence tools, data integration platforms, and reporting tools. SAS also has built-in features for deploying and managing code in production environments.

Cost Considerations

R from Revolution Analytics is a commercial product, which means it comes at a cost. While it offers enterprise-grade features and support, the cost can be a barrier for smaller organizations or individuals.

SAS is also a commercial product and can be expensive, especially for large-scale enterprise deployments. However, SAS often provides extensive resources and a substantial return on investment for its advanced analytics capabilities.

Conclusion

Choosing between R from Revolution Analytics and SAS ultimately depends on your specific needs, budget, and the nature of your data analysis projects. R from Revolution Analytics is an excellent choice for teams looking for a flexible and feature-rich tool, especially if you're working with advanced machine learning and data science projects. SAS, on the other hand, offers robust data management, comprehensive analytical procedures, and a strong enterprise support ecosystem, making it highly suitable for businesses with complex data environments.

Whichever tool you choose, both R and SAS are incredibly powerful and have extensive capabilities for data analysis. By understanding the strengths and weaknesses of each, you can make an informed decision that best suits your unique requirements.