A Short Description of Both Software Suites
SAS and R 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.
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Statistical Analysis System (SAS) is a programming language that is used to read 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 programming language has a wide range of statistical functions and a huge amount of graphical user interfaces (GUIs) for deploying in data analytics applications. SAS is easy to learn, and it offers great technical support. It can be considered as an expensive alternative to R; nonetheless, SAS is a proprietary tool.
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R is mostly used by the research community, professors and researchers, among other faculties. Since R is an open-source tool, we 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.
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Features of SAS and R
|A market leader in commercial analytics space
||An effective data handling and storage facility
|Provides a graphical point-and-click user interface
||A comprehensive and integrated collection of intermediate tools for data analysis
|Data can be published in HTML, PDF, Excel, and other formats using the Output Delivery System
||Offers graphical facilities for data analysis and the display can be done in either soft or hard copy
|Retrieves data from various sources and performs statistical analysis
||Includes conditionals, loops, user-defined recursive functions, and input and output facilities
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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. We can analyze SQL code, integrate it with macros, and so on. Learning SAS can be an excellent experience for beginners.
R is a bit tougher to learn as compared to SAS. Before learning R, we must have a basic knowledge of programming. R is not a high-level programming language. Due to its low-level programming nature, even a small mistake can turn out to be a huge problem. Thus, when it comes to picking the first programming language to learn, R is not suggestible.
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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 it. Furthermore, R works only on RAM, and increasing the RAM as and when the data increases is not a feasible option. This is where R uses packages of plyr and dplyr.
Graphics is a very important aspect of any Data Science or Data Analytics capabilities. The 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 offers improvisation of some graphical capabilities, these capabilities are not widely known, and so R gets a clear lead in this aspect.
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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. R has very good integration with Hadoop, and it also has a parallelization capability. If we 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 within Hadoop without the need to move cluster data. But still, SAS lags behind 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 and, as R is freely available to everyone, it finds widespread usage among small and medium enterprises. R is also easily scalable, thanks to its various packages and libraries which can be used for any application and function. SAS, on the other hand, is extremely useful for large organizations. SAS is mostly used for end-to-end infrastructure deployment, data warehousing, data quality, data analytics, and reporting capabilities.
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R has the most advanced graphical capabilities as compared to SAS. There are numerous packages which provide advanced graphical capabilities. R 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. However, gaps in the market share are vanishing fast.
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.
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Recommendation: Which one is better?
SAS programming syntax is a high-level language, easy to learn, and designed as an SQL DML (Data Manipulation Language). It has decent functional graphical capabilities, but customization on plots is very difficult. SAS updates its capabilities only in new version rollouts; it releases updates in a controlled environment which are well tested.
- Companies currently employing SAS: Bing, Ford, Uber, and Foursquare
- Companies currently employing R: Barclays, Nestlé, HSBC, and Volvo
As said earlier, SAS is the prevalent tool in the market analytics space, but it is an expensive option and is not always updated with the latest statistical functions. R is an open-source alternative and is typically used for academic and research purposes. Due to its open-source nature, the latest functions are added quickly.
The hoice between SAS and R always depends on organizational requirements. Large-scale organizations usually opt for SAS over R. Further, for a beginner wishing to learn a programming language, R is not the correct option. Because, it is lengthy and complex to learn. The learner must also consider his/her career phase and financial stability while making a choice between SAS and R.
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