The digital transformation wave that has taken on the world has given data a special place at the heart of businesses as data-driven operations and data-driven decisions become the norm. Unlike in the past, most data today is unstructured given that they come from a broad range of sources.
For this reason, mining and interpreting this data to extract meaningful insight requires combining the right tools and the right programming language. Data scientists and analysts need to be familiar with at least one language, specifically one used for data analysis. Some common programming languages used in data science and analysis are Python, R, SAS, SQL, Java, JavaScript, and Scala.
However, today we set two languages: R and SAS. For professionals who desire to launch or scale their careers, R programming certification goes a long way if you already have some programming experience. Otherwise, opt for SAS certification and later add R to your skillset. Still, both are important analytics tools with relatively different applications.
What is SAS?
SAS, referring to Statistical Analysis Software, is a software package used for statistical analysis, data management, and data visualization. It was developed by SAS Institute and dates back to the 70s making it one of the earliest programming tools to be used. SAS runs on most OS including Windows, Linux, and other UNIX environments.
The SAS software suite consists of three core components
- Data management function
- Data analysis and reporting function
- Programming language
SAS programming language reads input data from spreadsheets and databases to produce HTML files, RTF files, tables and graphs, PDF documents, and excel files as output. Advantages that SAS offers include extensive statistical functions and GUIs for developing data analysis applications.
The SAS Enterprise Guide features point-and-click, menu, and wizard functions, making it easy for non-technical users to learn and use the software suite to analyze data and publish the outcome. As such, it does not have a steep learning curve like most other programming languages and although programming experience is important, it is not a requirement for users of the SAS suite.
The SAS suite has found wide application in commercial analytics thus attracting big names like Nestle, Volvo, and Barclays but not so much the small start-ups. This is because it is not open-source therefore smaller companies find it expensive. Secondly, since it is not open source, it does not get frequent updates and so may not always have the latest statistical functions.
What is SAS used for?
- Advanced statistical and mathematical analytics
- Applications development
- Multivariate analysis
- Business intelligence
- Operations research and project management
- Predictive analysis
- Report writing and graphics design
What is R?
R is a programming language with data manipulation, statistical analysis, graphical representation, reporting, and other functionalities that are useful to the data science, data analysis, academia, and research communities. R has been adopted by companies like Airbnb, Google, Facebook, and others for data analysis. Unlike SAS, R is open-source and therefore gets updated frequently with the latest data analysis techniques.
Features of R
- R connects to a range of data formats and databases
- It runs on different environments including Windows, Unix, and macOS
- Comes with a range of algorithms, packages, and functions for statistics and data analysis. It features functions like loops, conditionals, user-defined recursive functions for data analysis.
- A wide user-network and vibrant community in addition to the documentation available online.
- Built to interface with other programming languages to enhance its capabilities.
What is R used for?
- Modeling statistical formulas and graphical functions
- Storage and handling for large data sets
- Graphics representations and data visualization
- Social data collection and analysis
- Data scraping from websites
- Train machines for ML predictions
R vs SAS
Both R and SAS are valuable to the data science community in one way or another. They are powerful tools but offer different capabilities and benefits.
| R | SAS | |
| Purpose | R is open-source and therefore relies on users to build and submit their analysis software and data analysis packages. For this reason, it always gets the latest updates of analysis techniques first. | SAS is built for statistical data analysis with access to spreadsheets and databases. It comes with powerful inbuilt packages and a wide range of statistical analysis techniques. |
| Ideal for | Built for statistical inference, data analysis, and machine learning functions. | Mostly suited for commercial analytics |
| Cost | Open-source software that can be used by anyone for free | SAS is a commercial software that comes at a cost |
| Updates | As it is open-source, it gets frequent releases of new techniques that users can access immediately | Has less frequent updates |
| Learning | Users need to first learn how to code as R involves intense coding | SAS is the easiest to learn and can be used well by professionals without prior SQL or programming knowledge. |
| Deep learning | R supports deep learning functionalities effectively | SAS may have integrated deep learning, however, this is not as established as in R and other languages. |
| File sharing | R interfaces with other programming languages and so sharing files is easy | File sharing between SAS users is fast and easy but not possible between SAS and external users |
| Data publishing | Data publishing in R is done in either soft or hard copies. | SAS offers more advanced data publishing as it supports publishing in HTML, PDF, Excel, RTF, and other formats. |
| Graphical capabilities | Offers advanced graphical visualization functions through its wide range of packages like ggplot, dplyr, data.table, RGIS, lactis, and others. | SAS offers equally powerful graphical features only that it lacks customization options. |
| Customer support | Lacks dedicated customer support but enjoys immense support from its vibrant community | Has a dedicated customer support |
| Market share | R market share is growing steadily especially among start-ups and other companies that are open to the dynamism of this space and which don’t shy off from trying out new analysis techniques. | SAS has been a market leader, particularly for large corporations and companies for a long time. However, it faces stiff competition from other emerging statistical and data analysis tools |
Verdict
While SAS is a well known and established data analysis tool with powerful inbuilt packages and great graphical capabilities, it lacks customization options and does not have as frequent releases as R. Again, it takes a hefty financial investment to host and run SAS.
On the other hand, R is a free tool that is constantly updated with the latest statistical analysis techniques and features a range of packages and libraries developed by its user community. It also has powerful graphical capabilities with available customization options on plots.
Both R and SAS are valuable tools in the data science and analysis spaces. However, they have relatively different applications and target markets. For instance, for professionals just launching a career in data analysis or data science SAS would be the best option as it is easier to learn and work with and is very well established in the market.
However, for experienced professionals who would like to add some skills to their toolset, R offers exciting learning and discovery as well as an opportunity for innovation.
