SAS vs R
A Short Description of Both Software Suites
SAS and R, both are important data analytics tools used in today’s tech world. Both tools are extensively used by Data Scientists and Data Analysts. Making a choice between SAS and R has been a longstanding debate in the world of Data Science.
Statistical Analysis System (SAS) language is a programming language that is used to read in data from spreadsheets and databases and output the results of statistical analysis in tables and graphs and as RTF, HTML, and PDF docs. SAS is commonly used for financial analytics capabilities. SAS is easy to learn, and it offers great technical support. It can be considered as an expensive alternative to R;
R is mostly used by the research community, professors and researchers, among other faculties. Since, R is an open-source tool, you can get the latest version as soon as it is released. R is mainly used for statistical analysis, graphical representations, and reporting. Here, we take a simplified yet concise look at the various features, functions, and strengths and weaknesses of each of these tools.
Features of SAS and R
Parameters of Comparison
Ease of Learning
SAS is very good when it comes to picking a new tool to learn without any prior programming language experience and thus can be an excellent experience for beginners.
R is bit tougher to learn as compared to SAS. It is not a high-level programming language and hence even a small mistake can turn out to be a huge problem.
In terms of handling and managing data, SAS is in a better position since the data is increasing at a huge pace day by day and SAS is better at handling data. Furthermore, R works only on RAM, and increasing the RAM as and when the data increases is not a feasible option.
Graphics is a very important aspect of any Data Science or Data Analytics capabilities. Ability to visualize and analyze data is a crucial part. R is the winner in this area, thanks to the availability of various packages like ggplot, Latice, and RGIS.
SAS is not great at graphical capabilities. Though Base SAS has some graphical capabilities.
Working with Big Data
While working with Big Data, R has some very good features which can be utilized by Big Data, Data Science, and Data Analytics communities. If you are looking for deploying analytics at scale for Machine Learning capabilities, then R is the language to choose. Of late, SAS is taking fast strides to execute analytics also. But still, SAS lags R when it comes to integrating successfully with Big Data tools like Hadoop and others.
Since R is an open-source programming language, it can be used by anybody. It thus finds a widespread usage among small and medium enterprises. SAS, on the other hand, is extremely costly and is useful for large organizations. SAS is mostly used for data warehousing, data quality, data analytics, and reporting capabilities.
There are numerous packages in R which provide advanced graphical capabilities. It incorporates the latest features quickly as the packages get added on by programmers across the world. Currently, R is in popular demand. Although, SAS has been the market leader in corporate jobs, it is very expensive for start-ups.
R has the biggest online community but without customer service support which makes it difficult for people to tackle any technical issue. Whereas, SAS has dedicated customer service, along with its community. Hence, installation and other technical challenges get easily sorted.
The choice between SAS and R always depends on organizational requirements. Large-scale organizations usually opt SAS over R, while the start-ups prefer the latter option.