Python vs R vs SAS: Which is the best Tool for Statistical Analysis?

While there are many useful and readily available tools in statistical software, why R, Python, SAS are so popular?

There are a lot of statistical software packages available in data science market today. Some of the popular ones include R, Python, SAS, SPSS and more. Out of these, R, Python and SAS are quite commonly used in business sector. In this article, we will explore and find out if there is a best tool for statistical software domain.

Python is an object-oriented programming language. This means it groups data and code into objects which can interact with one another, other languages which fall in the same suit that includes C++, Scala and Java. Meanwhile, R is a procedural language that dissects programming tasks into a series of procedures, and subroutines. It is the best tool for building data models as it is relatively easy to understand how complex operations are carried out in R. In contrast, SAS or statistical analytics tool is used to store, retrieve, and modify data, for exploratory data analysis, data visualization, and building predictive models. Unlike, R and Python, SAS is a closed-source one.

Why Use Python?

Python was originally developed as a programming language for software development. Its ease of use makes the transition from other popular programming languages like Java or C++ to Python is easier than the transition to R. It is suitable for carrying deep learning-based research.

Why Use R?

R is resourceful for programming constructs for data analytics like conditionals, loops, input and output facilities, user-defined recursive functions, etc. It is very easy to automate and integrate. This tool can also be run on a variety of platforms like Windows, Unix, and MacOS. It is best tool for statistical modeling research.

Why Use SAS?

SAS offers a variety of functions along with a great UI. These features help the individuals to learn this software at a rapid speed. Further, since SAS works on PROC SQL which can be easily understood by the individuals who already understand the SQL.

Python vs R vs SAS: Which is a best tool in statistical software?

The above three programs are widely leveraged in market and offer almost the entire spectrum of statistical methods. It is rather tricky to pick one as favorite among them, however, companies can employ either one or all depending on their requirement. For instance, SAS is preferred by financial and marketing companies, because of its high customer service, whereas R is a common choice in telecom sector for carrying the data analysis of unstructured data. Also, SAS is extremely efficient at sequential data access, and database access through SQL is extremely well integrated. However, it is a bit clumsy when it comes to writing complicated code, and not as elegant for parallel code.

In terms of statistical capability, SAS covers virtually the entire techniques and statistical evaluation. However, advanced techniques like GMLET, ADABoost RF can only be accessible with R. At the same time, Python is not suitable for statistical distributions, although it is well suitable to perform statistics function that is widely used in data science and also for machine learning. While R allows the build-in data analysis for summary statistics, and it is supported by summary built-in functions in R, one has to import the stats model packages in Python to use this function.

Python is developed with a strong focus on (business) applications, not from an academic or statistical standpoint – making it very powerful when algorithms are directly used in applications. R has the widest range of algorithms, which makes it strong on the explanatory side and on the predictive side of data analysis. Also, though Python is mostly used in data mining, today, SAS too data mining tools (SAS Enterprise Miner), however, user may need extra licenses. Another key difference is, unlike R and Python, though it is possible to make minor changes to graphs, to fully customize plots and visualizations in SAS, it can be very cumbersome or even impossible.

So, to surmise, there is not one particular statistical software fits all package in industry. Businesses need to evaluate their organizational requirements before implementing one.