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SAS
SAS is the integrated system of software solutions, and it is the leader in the data analytics field. This software has a lot of features like good GUI and others to provide excellent technical support. SAS helps you to do the following tasks
- Data Entry, retrieval and management
- Report writing and graphics design
- Statistical and mathematical analysis
- Business Forecasting and decision support
- Operations Research and Project management
- Applications development
SAS is used by reputed companies like Barclays, Nestle, HSBC, Volvo and BNP Paribas.
R
R is a programming language for statistical computing and graphics which was created in the year 1995 by Ross Ihaka and Robert Gentleman. It offers a wide range of statistical and graphical techniques. It is an open source route which is highly extensible. It is a simple and effective programming language. It is more than just a statistics system. It does the following work
- Easily manipulates packages
- Manipulates strings
- Works with regular and irregular time series
- Visualize data
- Machine learning
- R is used by top rated companies like Bank of America, bing, Ford, Uber and Foursquare.
Reasons for comparison
Industries are growing dynamically. As the field grows, there are a lot of technological advancements in each language.
If you are new to the data analytics field, then you might be learning a new one because of your interest or most of the times driven by what your organisation works with. You might challenges and frustrations because of upgrades in the tools and software programs.
Comparison of the languages is a worthy consideration now. Any comparison which was done before few years will not be relevant to the current situation. Comparisons will also help in choosing the best among the three.
SAS can satisfy all your data science needs, but it is not suitable for the long run. Companies are now moving fast towards open source programming languages which is easy to access and use.
SAS being the restrictive and closed tool it is not preferred much these days.
R and SAS are open source tools which will help you increase your data science knowledge, learn new technologies and algorithms. Knowing about R and SAS automatically makes you eligible for data science jobs these days.
Comparison based on ten things
1. Tasks
With the help of SAS, you can perform the following tasks: statistical and mathematical analysis, app development, business forecasting and decision support, report writing and graphics design, data entry, retrieval and management.
With the help of R – visualise data, machine learning, manipulate strings, works with regular and irregular time series, efficiently manages packages.
2. Cost
SAS is commercial, and it has an expensive license instead. R is free and open-source and can be easily downloaded.
3. Support
SAS has excellent support even in critical areas. R does not have any support, but you can get the help from the community of its users (programmers, students, academicians).
4. Updates
Every new or updated feature for SAS should be bought. You cannot download it for free. New features of R are in open access.
5. Ease of learning
SAS is very easy for those who have basic knowledge of SQL. It has a good GUI interface. There are many tutorials and well-designed documentation. R requires a good understanding of coding. It is a low-level language, and that’s why more code lines are needed.
6. Handling of Data
All they have rather good capacities for data handling. They both support parallel computations. But SAS is more safe and smooth. R works only on RAM.
7. Graphical Capacities
SAS has rather good functional graphical capabilities, but in the case of customisation, you should be aware of the nuances of SAS Graph package (they are not well-documented). R has different packages (GGPlot, Lattice, GGVIS, RGIS, etc.) that ensure excellent graphical capacities.
8. Visualization support
SAS Visual Analytics is the primary visualisation platform for SAS (it is too expensive). The visualisation tools for R are free.
9. Job
R has more job opening. It is mostly used by startups to get the cost efficiency. Big companies prefer SAS.
10. Language for Big Data Applications
In the case of Big Data, end-to-end apps are preferable than ad-hoc or standalone. R integrates with Hadoop and supports parallelisation. SAS is not so flexible, but still, it runs analytics inside Hadoop.
