In today’s rapidly evolving digital landscape, there’s a noticeable shift from using the legacy SAS proprietary software to R, a modern, open-source software environment. Although the transition is still in its early stages, data experts at ProCogia are leading this movement, supporting progressive clients eager to make the switch.
Why migrate from SAS to R?
Cost Savings
One of the main advantages is cost savings. SAS is expensive, requiring a base license plus costly extensions for advanced analysis. In contrast, R is free and can achieve nearly everything SAS can do at a fraction of the cost. While clients already anticipate the high costs associated with SAS, R provides a remarkable cost-saving alternative without compromising quality.
Simple Data Structures
Another key issue is reliability and data structures. While SAS is known for its reliability with simple data tables, today’s data demands a more robust solution capable of handling complex and unstructured data. R, with its versatile data structures, can manage unstructured data, unlike SAS, and supports advanced algorithms due to its flexibility and adaptability.
Collaboration
Collaboration is another significant incentive to migrate from SAS to R. In the open-source ecosystem, R encourages unlimited collaboration among peers. Git repositories enable real-time coordination, enhancing accountability and traceability. Conversely, SAS requires users to work on individual documents, leading to cumbersome version control and limited online interaction.
Interoperability
Interoperability is another notable benefit of R. It can seamlessly interact with other languages like Python, enhancing its interoperability and making it easier to integrate diverse tools. This interoperability, combined with peer collaboration, makes R a strong choice for organizations looking to modernize their data processes.
Advanced Analytics
R also excels at advanced analytics, such as machine learning and neural networks, providing insightful reporting and data analysis. In contrast, SAS, primarily developed for basic procedural programming, struggles to keep pace with modern statistical techniques and data complexities.
SAS
SAS is a legacy based programming language which has primarily been used for validation processes. This high cost statistical software suite was developed to perform basic procedural programming using statistics and analytics and has mainly been used by clinical trial companies, healthcare organisations, pharmaceutical industries and marketing agencies.
R Programming
Alongside being widely available and free to use, R benefits from its strong user community. As universities continue to increase the number of courses specialising in R, the community will continue to grow and evolve. This community works together to continually develop new packages and maintain existing ones. This increasing momentum reflects the speed of R progression.
A Stark Comparison
Comparing the two programming environments reveals stark differences. SAS is a legacy-based programming language mainly used for validation processes in clinical trials, healthcare, pharmaceuticals, and marketing. Its procedural approach limits its ability to handle unstructured data and advanced analytics. On the other hand, R, a modern, open-source software environment, benefits from a strong user community and rapidly progresses in functionality due to increasing university courses and industry adoption. This has made R a preferred choice for advanced analytics, collaboration, and cost efficiency.
Expertise with R
Migrating from SAS to R may seem challenging initially, but ProCogia’s team of seasoned data specialists simplifies the process. If you have a final SAS data set, it can be directly imported into R, while procedural programs need conversion. ProCogia also provides bespoke training sessions, workshops, and consultations to ensure a smooth transition. We recommend adopting R Server Pro and R Connect to centralize R systems and dashboards, offering insightful data displays.
In conclusion, ProCogia’s team is well-equipped to help businesses transition smoothly from SAS to R, unlocking the benefits of cost savings, collaboration, interoperability, and advanced analytics.
