SAS vs R: The SAS language is a programming language used for statistical analysis, originated by the project at the North Carolina State University. It can input data from common spreadsheets and several databases and output the
results of statistical analysis in form of tables, graphs, and as RTF, HTML, and PDF documents.
R language is software environment for graphics that is supported by the the R Foundation for basically Statistical Computing. The R language is widely used among data miners for developing statistical software and data analysis . R is a GNU package The source code for the R software environment is written primarily in C, Fortran, and R. Language R is freely available under the GNU General Public License and is pre-compiled binary versions that are provided for various operating systems. While R has a command line interface, there are several graphical front-ends also available.
R vs SAS: Comparision and Opinion
Attributes For Comparison
Let’s compare these languages over the following attributes:
- Cost of availability
- Ease of learning
- Capabilities of Data handling
- Graphical capabilities
- Advancements in tool
- Job scenario
- Customer service supports and Community
These comparisons are being done from point of view of an analyst. So, if you are getting a thought for purchasing up a tool for your company, you might not be getting a complete answer here. The information listed below will still be useful. For each attribute, a score to each of these 3 languages (1 – Low; 5 – High) is given.
The value for these parameters will be varying depending on what point of career you are in and what are your ambitions.
- Availability / Cost
SAS is a commercial purpose software. It is expensive and still beyond the reach for most of the professionals . However, it holds up the highest market share in Private Organizations. So, until and unless you are in an Organization which has in past invested in SAS, it might get difficult to access one.
R programming and Python, on the other hand, are free and could be downloaded by anyone from anywhere. Here are the scoring points on this parameter:
SAS – 2
R – 5
- Ease of Learning
SAS programming language is easy and quick to learn and provides an easy option (PROC SQL) for those people who already know SQL. Even otherwise, it has a good and stable GUI interface in its repository. In terms of resources, there are tutorials available over several websites of various university and SAS has a comprehensive documentation. There are even certifications from SAS training institutes, but they again going to come at a cost.
SAS – 4.5
R – 2.5
- Data Handling Capabilities
This used to be an advantage for SAS till some time back. The R computes every thing in memory (RAM) and hence these computations were limited by the amount of RAM over 32-bit machines. This is no longer the case now . All three languages have good data handling capabilities and also options for parallel computations. This I feel is no longer could be considered a big differentiation. Also, I might not be aware of the latest innovation in each of this ecosystem and hence I see all 3 as equally capable.
SAS – 4
R – 4
- Graphical Capabilities
SAS has decent functional and graphical capabilities. However, it is just functional. R has the most advanced graphical capabilities among the compared languages. There are numerous packages which provide you advanced graphical capabilities.
SAS – 3
R – 4.5
- Advancements in Tool
All the compared ecosystems have all the basic and most needed functions available. This feature will only matters if you are working with latest technologies and algorithms.
Due to their open nature, R & Python get latest features comparatively quickly (R more so compared to Python). SAS, on the other hand, updates its capabilities in the newer version emerging in the market. Since R has been used up widely in academics in past, development of new techniques is relatively fast.
Having said this, SAS releases updates in a controlled environment, hence they are well tested. R & Python, on the other hand, consists of an open contribution and there are chances of errors in latest developments.
SAS – 4
R – 4.5
- Job Scenario
Globally, SAS programming language is still the market leader in the available corporate jobs. Most of the big organizations are still working on SAS. R / Python, on the other hand, are better options for start-ups and companies looking for issues of cost efficiency. Also, a number of jobs on R / Python have been reported to increase over past few years. Here is a trend widely published over the internet, which shows that the trend for R and SAS jobs. The jobs for python indata analysis will have the same trend as that of R jobs:
In India, specifically, the gap in SAS vs. R is bigger. My estimation is that SAS would have about 70% of market share, R would be around 15% and Python less than 5%. However, the trends are similar to the global trends.
SAS – 4.5
R – 3.5
- Customer Service Support & Community
The R has one of the biggest support coomunity but still you cannot have the services for the customer support. You will get a lot of help, though. Similar is the case for python, though at a lower scale. R data analysis is also one of the powerful tool Using R, one can tackle all the data analysis problems very easily.
SAS, on the other hand has dedicated customer services along with the community. So, if you have problems with installation or any other technical challenges, you will get the help easily.
SAS – 4
R – 3.5
Clearly, there is no prediction of the winner in this competitive environment between SAS and R. It will be a pre-mature process to place bets on what will prevail, given the prevailing dynamic nature of the industry. Depending on your circumstances . You can add your own weights and come up with what might be getting suitable for you. Here are a few specific scenarios:
- If you are a fresher entering into the analytics industry (specifically so in India), I would recommend you to learn SAS as your first language. It is quite easy to learn and holds highest job market share.
- If you are the one who has already spent time in the industry, you should try and diversify your expertise for learning a new tool.
- For experts and professors in industry, people should know at least 2 of these. That would add a lot of flexibility for future and open up fresh and new opportunities.
- If you are in a start-up / freelancing, R or R programming language / Python is much more useful
View points may vary over your own perceptions. Now, its your turn to share your views and thoughts through the comments below.