Statistical programming has become popular with the growing importance of studying and mining data. There are two major languages – Python and R – that support statistical programming with their vast range of packages and tools.
In this article, we’ve briefly explained both the languages, listed their advantages and disadvantages and compared both of them.
Python is an interpreted, interactive, high-level, object-oriented programming language. Initially developed in 1991, it is a user-friendly programming language that is open-source and finds major applications in AI, ML, Big data, Robotics and GUI based desktop apps.
It is designed to be highly readable. Its syntax is simple yet powerful. Its modules allow you to perform many things like object-oriented programming. It is extensible and interfaces with OSI systems. It is customisable with third-party extensions as well from the Python Package Index.
Python has an extensive standard library of useful modules and a set of modules known as the Python “Standard Library”, which come with the language by default. One of Python’s guiding principles is that users must extend and modify existing libraries and add new modules easily, rather than making them difficult to change.
Python is a beginner’s general-purpose coding language. It can be used for back-end development, software development, writing system scripts, data science, and statistical analysis.
Python for your respective OS can be downloaded here.
|Advantages of Python||Disadvantages of Python|
|Ease in reading, writing and understanding.||Isn’t ideal for games and app development.|
|Interpreted and dynamically typed|
|Free and open source|
|Line-wise code execution makes debugging easier.||Slightly slow because it uses an interpreter.|
|Has a broad inbuilt standard library.||–|
|It can be integrated with other programming languages like Java and C/C++.||–|
|It does not depend on the platform; once the code is ready, it can be run on any platform.||–|
|Provides good interfacing with most databases.|
Released in 1993, R is a dynamic, procedural, object-oriented programming language. It is highly recommended for performing statistical operations, working on data science as well as visualisation. It can be used on UNIX platforms, Linux, FreeBSD, Windows and macOS.
R has a widespread set of functions of statistical and graphical methods, including statistical interference, linear regression, machine learning algorithms, time series, linear and non-linear modelling, clustering and many others.
R has almost 12,000 plus libraries that are stored in CRAN, which is free and open-source. It stands for Comprehensive R Archive Network. These libraries can be downloaded directly within the R software from CRAN and used. It is also machine-independent, which makes it accessible on multiple machines.
R for your respective OS can be downloaded here.
To download R software for your system,
- Click on Download R.
- Select the CRAN mirror based on your location.
- Download the software on your OS.
|Excellent statistical computing and analysis||Difficult due to its different syntax|
|Large libraries||Redundant libraries that may hamper the quality of graphs|
|Open-source||Poor memory management and slow speed|
|Cross-platform support||Low security|
|Performs Data Cleansing, Data Wrangling, and Web Scraping||–|
|No compiler required||–|
|Integrates well with other software||–|
Python vs R
While Python and R both have their advantages and disadvantages, based on the end product required, one should choose between Python or R. R is currently more commonly used by Scholars and researchers. In contrast, Python is used more by programmers and developers. Both have exceptional statistical abilities; hence there is no specific winner here.
|Type||Interpreted language||Interpreted language|
|Programming type||A functional, object-oriented, structured and aspect-oriented language||A procedural and object-oriented language|
|IDE||Ipython Notebook, Spyder||RStudio|
|Uses||General-purpose language for development and deployment||Statistical language and graphical interpretation|
|Best suited for||Deep learning||Data visualisation|
|Maintenance||Robust language, easy to maintain||Requires higher maintenance|
|Packages||Only one package for one task||Multiple packages for one task|
|Speed||Faster than R||Slower than Python|
|Ease of use||Easier to develop models from scratch||Easier to use an existing library|
|Database size||Large size||Large size|
Also read: What’s the difference between C and C++?
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