Updated June 9, 2023
Difference Between SAS vs R vs Python
If you choose the analytics profession, the major question that arises in your mind is, “Which is the best tool for the job ?”
It has been a battle for years, and deciding between the programming languages best suited for data analysis is always hard. Traditionally this question was raised against SAS vs R, but now Python has joined this discussion. So which is better, SAS vs R vs Python?
A few years back, tracing the career path with these tools was difficult. But fortunately, this turned out to be a blessing in disguise. But now, before deciding what technique to apply, analytics professionals are searching for the best tool to perform that task.
There is heavy competition between SAS vs R vs Python. But the honest answer is that each tool is unique in its own way. There is no universal winner in this context. Each tool has its own strength and weakness.
A professional analytic needs to know each tool’s strengths and weaknesses to decide which is best to use for their profession.
Head-to-Head Comparison Between SAS vs R vs Python (Infographics)
Following are the top differences between SAS vs R vs Python.
Now let’s look at what the tools are about and what they are used for.
Description SAS vs R vs Python
Here is a short description of the 3 tools:
It is an integrated system of software solutions and the leader in the data analytics field. This software has a lot of features like good GUI and others to provide awesome technical support. It 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
Reputed companies like Barclays, Nestle, HSBC, Volvo, and BNB Paribas use it.
R is a programming language for statistical computing and graphics, created in 1995 by Ross Ihaka and Robert Gentleman. R offers a wide range of statistical and graphical techniques. It is an open-source route that is highly extensible. It is a simple and effective programming language and 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.
Python is an object-oriented programming language that has clear syntax and readability. Python was created in 1991 by Guido Van Rossum. It is easy to learn and will help you work more quickly and effectively. It has become more popular in a short period of time because of its simplicity.
Famous companies like ABN-AMRO, Quora, Google, and Reddit use Python.
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, you might be learning a new one because of your interest or, most of the time, driven by what your organization works with. You might have challenges and frustrations because of tool and software program upgrades.
Comparison of the languages is a worthy consideration now. Any comparison in a few years will not be relevant to the current situation. Comparisons will also help in choosing the best among the three.
These languages are compared to the following factors in this article. You may not purchase a tool based on the following comparisons, but it will definitely help you choose one that suits your career.
1. Open Source vs Closed system
It is a closed source and does not support transparent functionalities. Whereas R and Python is the open-source counterpart of SAS and contains detailed transparency of all its functionalities and algorithms.
Knowing the functionality is more time-consuming as it takes a long process. It is also counter-productive.
It is one of the most expensive software in the world. Millions of dollars need to be invested in getting a SAS license. Therefore it can be used only by large-scale companies.
There are only a few companies that use SAS. If you are a SAS professional, then you need to choose a workplace where they use SAS. If you join a company where they don’t use SAS, your career will be redirected to a new path.
R is open-source software that can be downloaded for free by anyone. On the other hand, Python is also free, open-source software that anyone can download.
It is easy to learn, especially for people who already know SQL. Also, it has a stable GUI interface. Tutorials of SAS are available on various sites, and it has comprehensive documentation.
Python is very easy to learn in the data analytics world. Python does not have a widespread GUI interface, but Python notebooks have become popular. They provide you with the features of documentation and tutorial.
R is a low-level programming language that requires longer codes for shorter procedures. It would help if you had a deeper insight into coding in R.
It requires you to buy new products to get to know about the advanced features of SAS. It does not offer you an option to download any feature and use it instantly and also has strict licensing limitations.
Whereas in R and Python, you are allowed to access or upgrade to advanced features like parallel processing, multicore packages, etc., to help you do repetitive operations.
5. Data handling capabilities
All three languages are equally good at data handling and have an option for parallel computations. There is not much difference between the three in this factor. A few innovations might be made to improve these languages’ standard.
6. Graphical capabilities
With reference to this factor, R has the best graphical capabilities when compared with the other two.
It has basic graphical capabilities, but it is only functional. Customization of plots is difficult, and it needs in-depth knowledge to know about the SAS Graph package.
Python can use native libraries (matplotlib)or derived libraries that allow calling for R functions.
R has excellent graphical capabilities among the three. They have advanced packages for graphical capabilities.
7. Advancements in tool
All three languages have the basic and most required functions, but the latest technologies and functions matter greatly if your work expects it.
R and Python are open sources, so they get enhanced to the latest technologies and features more quickly than the other two languages. Development of new techniques is very fast in R.
On the other hand, SAS takes time to update to the latest features and capabilities as it works in a controlled environment.
There is one main advantage of SAS working in a controlled environment. They are well-tested, so the chances of errors are very less.
But Python and R work in open-source. They are quickly updated to the latest technologies, but they are more open to errors.
8. Job Scenario
R and Python have had job openings recently and are expected to increase.
They are used by companies that look for cost efficiency. They are the best option for a start-up company.
It is used widely by big organizations and corporate companies.
A recent study has proved that Python jobs for data analytics will also increase in the same way as R.
9. Support for Visualization
Visualization is a fundamental part of data science. The main visualization platform of SAS is called SAS Visual Analytics. This is too costly to use.
R and Python have a lot of visualization tools for free. It does not require you to sign a contract and pay for each and every activity like in SAS.
10. Customer Support and Community
Based on customer support and service, SAS is the best when compared with the other two languages. It has dedicated customer support and service and a community. You can contact the support center directly if you have any technical problems.
R has a big online community but no customer support center. You will get help from them but not instantly.
Python, too, does not have a customer support center. It provides help to its customers but not to the level of SAS.
11. Industry trends
The trend of job market is moving fast toward open-source technologies. R, Hadoop, and Python are all major examples of this. It is also one such technology but the only paid product. People prefer R and Python to SAS because it provides no extra benefits over the free products. Only a few companies go for SAS these days for certain reasons.
R and Python come for free and can be downloaded with ease.
Thousands of contributors worldwide support R and Python. If any development or up-gradation is available to languages, then it is made available to the customers at ease.
SAS product is accessible only by the SAS Institute Incorporated, and only the SAS developers can produce new features. This takes a lot of time. And before you update the SAS features with new algorithms, you can complete your project using any other tool.
13. Tutorials and Guide
It does not offer any step-by-step guide to its customers. If you are starting with a new topic or want to learn something new in SAS, you should definitely seek a SAS consultant who is again from SAS Institute Incorporated.
R and Python, on the other hand, provide you with detailed examples. It also offers a tutorial on the Internet. Python contains reproducible notebooks called iPython. R exercises and iPython Notebooks are widely available on sites like GitHub.
Here is a table view to easily compare all three tools based on a few criteria
|Advancements in tool||High||Low||Medium|
It can satisfy all your data science needs but is unsuitable for the long run. Companies are moving fast towards open-source programming languages that are easy to access and use.
This being a restrictive and closed tool, it is not preferred much these days.
R and Python are open-source tools that will help you increase your data science knowledge and learn new technologies and algorithms. Knowing about R and Python automatically makes you eligible for data science jobs.
The bottom line is there is no obvious winner among the three. All three tools have their own advantages and disadvantages. Their strengths make them survive in the market in the long run.
It is ultimately the data scientist who has to decide between the languages. As a data scientist, deciding which language fits your need best is up to you. You can ask yourself a few questions and decide about that.
- What type of problems do you want to solve?
- How much are you ready to spend to learn a language?
- What are the common tools used in your field?
- What other similar tools are available in the market, and how does it relate to the commonly used tools?
The answers to these questions can help you choose the best tool and advance in your career.
Learn and become a master of the language.
This has been a guide to SAS vs R vs Python. Here we have discussed Head to Head Comparison, description, and reason for comparisons. You may also have a look at the following articles to learn more –