R and Python, both are used for data analysis. R can be integrated with many programming languages, such as, C, C++, Java, etc. If the data that you have to analyze has heavy statistical variables, equations, and graphics, R is the language to rely upon.
Python has the syntax and build that is similar to object oriented languages, such as, C, C++, and Java. Python has multipurpose use apart from its capabilities to perform data analysis. You can build websites and many other software development tools. This language provides support in developing apps, or any kind of application software.
You can evaluate and compare these languages on the below mentioned points.
For Beginners and Experts
Python is for beginners in programming language. It is easy to learn and is not as complex as R. Advanced developers can go with R. You don’t need a prior programming language experience to learn python.
There are more monetary benefits to using Python online for umpteen web development purposes. Python supports the commonly used internet protocols, such as, HTML, XML, JSON, FTP, etc. This is highly suitable for text analysis because the code is generated in human language and not system readable language.
R provides many tools that are free for download for text analysis. Data mining is still at a very nascent stage. It is said that 80% of the data that exists in the world is unstructured. R has an extensive library compared to Python that can sort complex data.
Code is easily readable in Python compared to R. R is used in academics and research and Python is used by programmers at the enterprise level.
You can choose either of these languages based on the complexity of the data being analyzed. Python increases productivity and R synthesizes dirty data into a cleaner one. R can be installed independently, where as Python has dependencies when installing their libraries.