SAS, R, and Python are all popular programming languages used for data analysis, but they have different strengths and weaknesses.
SAS is a proprietary software that is widely used in business and industry for data management and statistical analysis. It has a user-friendly interface and a wide range of statistical procedures, making it easy to use for beginners. SAS also has a large community of users, which means that there is a wealth of resources available for learning and troubleshooting. However, SAS can be expensive, and its proprietary nature means that users do not have access to the source code.
R is an open-source programming language for statistical computing and graphics. It is widely used among statisticians and data scientists for data analysis, visualization, and modeling. R has a large number of packages and libraries available for a wide range of tasks, such as machine learning, data visualization, and text mining. Additionally, the R community is very active, with frequent updates and new packages being released. R Programming Language also has a strong focus on reproducibility, which is important for research and scientific work. However, R can be less user-friendly than SAS and may have a steeper learning curve for beginners.
Python is a general-purpose programming language that is widely used in data science, machine learning, and scientific computing. Python has a large number of libraries and frameworks available, such as NumPy, Pandas, and Scikit-learn, which make it easy to perform data manipulation, analysis, and modeling. Python is also widely used in industry and is supported by many organizations. It has a large community and many resources available for learning and troubleshooting. Python’s simplicity and ease of use make it a good choice for beginners, however, It might be less efficient than R and SAS for specific statistical analysis tasks.
Comparison Factors for Python vs R vs SAS
- Popularity
- Ecosystem
- Syntax
- Speed
- Cost
- Support
- Integration with Big Data
- Scalability
- Machine Learning
- Cloud Compatability
- Graphical User Interface
- Multiprocessing
Popularity
Python is currently considered the most popular language for data science and machine learning, with a large number of libraries and frameworks available such as NumPy, pandas, and scikit-learn. R is also widely used in data science, specifically for statistical analysis and visualization, with packages such as dplyr and ggplot2. SAS is primarily used in the business and financial industries for data management and analysis.
Ecosystem
Python has a large and active community, with a wide variety of libraries and frameworks available for data manipulation, analysis, and visualization. R also has a strong ecosystem, with a wide range of packages for data manipulation and visualization. SAS also has a comprehensive ecosystem with a wide range of tools for data management, statistical analysis, and visualization, but it’s not as broad as python and R.
Syntax
Python has a simple and easy-to-learn syntax, making it a good choice for beginners. R has a more expressive syntax and is more suitable for advanced users, as it allows for more complex programming. SAS has a proprietary and non-standard syntax, which can make it difficult for users to switch to other languages.
Speed
Python and R are generally slower than SAS when it comes to data manipulation and analysis. However, Python and R are more flexible and can be easily integrated with other languages, whereas SAS is a closed system.
Cost
Python and R are open-source and free to use, which makes them accessible to a wider range of users. SAS, on the other hand, is proprietary software and requires a license to use, which can be costly for some organizations.
Support
Python and R have large communities, so finding support and documentation is relatively easy. SAS, on the other hand, is supported by a single company, so users are dependent on the company for support and updates. This can be a concern for organizations that rely heavily on SAS, as they may need to factor in the cost of support and updates into their budget.
Integration with Big Data
Python and R both have libraries that allow them to integrate with big data platforms such as Hadoop and Spark, whereas SAS is not as easily integrated with big data technologies.
Scalability
Python and R are more scalable than SAS, as they can be easily integrated with other languages and systems to handle large amounts of data. SAS is not as easily scaled, as it is a closed system.
Machine Learning
Python has a wide range of machine learning libraries like TensorFlow, Keras, and scikit-learn. R also has a wide range of machine learning libraries like caret and mlr. SAS has its own suite of machine learning tools, which can be more difficult to use for beginners as compared to python and R.
Cloud Compatibility
Both Python and R are compatible with most cloud platforms, whereas SAS is less cloud-compatible, and it may need additional configuration to work on cloud platforms.
Graphical User Interface (GUI)
SAS has a proprietary and user-friendly GUI, which is called SAS Studio, R has RStudio, which is widely used by R users, whereas python doesn’t have any inbuilt GUI but there are libraries like Spyder, Jupyter Notebook, and Pycharm are widely used in python ecosystem.
Multiprocessing
Python and R have libraries that allow for multiprocessing, which can speed up computation time. SAS does not have built-in support for multiprocessing, but it can be implemented with additional configuration.
Comparison table between SAS v/s R v/s Python
Now let’s see the tabular comparisons between the two for better understanding.
|
Parameters |
SAS |
R |
Python |
|---|---|---|---|
| Popularity | Widely used in certain industries, but declining in popularity due to high cost and closed-source licensing. | Increasing in popularity, especially in academia and data science. | Increasing in popularity, especially in data science, machine learning, and artificial intelligence. |
| Ecosystem | SAS/STAT, SAS/GRAPH, SAS/ACCESS, etc. | CRAN, Bioconductor, ggplot2, caret, etc. | NumPy, pandas, SciPy, matplotlib, etc. |
| Syntax | Procedural and structured | Functional and object-oriented | Object-oriented and functional |
| Speed | Optimized for large-scale data processing and computations | Can be slow for large data sets, but can be accelerated with packages | Faster than R for large data sets, optimized for high-performance computing |
| Cost | Proprietary, commercial license | Open-source, free | Open-source, free |
| Support | Formal support with licensing, online community | The large and active online community, and formal support from companies like RStudio. | The large and active online community, and formal support from companies like Anaconda and Microsoft. |
| Integration with Big Data | SAS Grid, Hadoop integration | Packages like dplyr, data.table, sparklyr, Hadoop integration | Packages like Dask, PySpark, Apache Arrow, Hadoop integration |
| Scalability | Suitable for large-scale data processing | Suitable for medium-scale data processing | Suitable for large-scale data processing with the right tools |
| Machine Learning | Limited capabilities without additional SAS/STAT package | Rich capabilities with caret, mlr, TensorFlow, Keras, etc. | Rich capabilities with scikit-learn, TensorFlow, Keras, PyTorch, etc. |
| Cloud Compatibility | SAS Viya, SAS on Demand for Academics | Microsoft Azure, Amazon Web Services, Google Cloud Platform | Google Cloud Platform, Amazon Web Services, Microsoft Azure |
| Graphical User Interface | SAS Enterprise Guide, SAS Studio, etc. | RStudio, R Commander, etc. | Jupyter Notebook, Spyder, PyCharm, etc. |
| Multiprocessing | Supports multiprocessing for large-scale data processing. | Supports multiprocessing, but is limited compared to Python. | Supports multiprocessing for large-scale data processing. |
In summary, SAS is a good choice for beginners who need to quickly perform statistical analysis, R is a good choice for statisticians and data scientists who need a wide range of statistical and visualization tools, and Python is a good choice for data scientists and developers who need a general-purpose programming language for data analysis and machine learning. The choice of which language to use will depend on the specific needs of your project and your own personal preferences.
