SAS to R Code Conversion powered by GenAI

The conversion of SAS code to R represents an innovative and highly efficient way to transition from legacy SAS code to modern R code, enabling businesses and data professionals to unlock the benefits of open-source data analysis and statistical modeling. By using AI-driven automation, this process can be done faster, more accurately, and with reduced costs, ensuring that organizations are able to keep pace with the evolving data landscape. Converting SAS to R requires a thorough understanding of both languages and their respective libraries. By systematically translating data steps, procedures, can create a robust R equivalent that retains the functionality of the original SAS code. R integrates seamlessly with modern data science tools, machine learning frameworks, and cloud-based platforms, making it a popular choice for modern data scientists. It involves translating data manipulation, statistical analysis, and visualization tasks from the SAS programming language to R. Understanding the SAS code structure and classification algorithms are used to identify the group or class that a data point belongs to i.e. ETL, EDA, ML and General group. Brillio’s code conversion solution can lead to more efficient workflows, enhanced analytical power, and reduced costs. The solution primarily utilizes Amazon S3, Lambda, AWS Bedrock services for code conversion.

• Automate the conversion process, translating SAS code to R syntax while maintaining functionality.

• Comprehensive mapping of SAS functions to their R equivalents, ensuring that statistical analyses and data manipulations are preserved during the transition.

• Support for converting SAS datasets into R Data Frames, allowing for seamless data handling and manipulation.

• Opportunities to optimize and refactor code during conversion, improving performance and efficiency in the R environment.

• Cost savings: R is open-source and free to use, eliminating licensing fees associated with SAS. This can significantly reduce costs for organizations.

• Increased Flexibility: R offers a wide range of libraries and tools for data analysis, machine learning, and visualization, allowing users to choose the best tools for their specific needs.

• Access to Modern Data Science Techniques: R provides access to cutting-edge libraries and frameworks for machine learning and deep learning, enabling the application of advanced analytics.

• Integration Capabilities: R easily integrates with other programming languages, databases, and cloud services, allowing for more comprehensive data workflows.

• Rich Ecosystem: R has a vast ecosystem of packages and libraries for various tasks, from data manipulation to visualization, making it easier to perform complex analyses.

• Scalability: R can handle larger datasets and more complex computations efficiently.

• User-Friendly Syntax: R’s syntax is generally considered more intuitive and easier to read than SAS, making it more accessible for new users and facilitating quicker onboarding.