Which is more versatile as a Data Analysis Tool: Python Or R

If you conduct online search, you are likely to come across various resources that will assist you in figuring the strengths & pitfalls in Python and R. These articles, often, delves, into extensive detailing that makes the discussion entirely technical and becomes hard for the common men to interpret. Paragraphs underneath shall try to compare between these data analysis tools in simple terms that will enable the mass to take a decisive call.

Guido Von Rossum is credited for innovating Python. R came up in the market in the year 1993 and the most advanced version of the tool was released to the market in the year 2015. Presently, the core development team of R handles the development of this data analysis tool.

Nature of Languages

Python finds massive application for generic purposes and it is rated as an advanced programming language. The philosophy of its design emphasizes on writing precise codes that the programmers can read easily. Primarily, the environment of R was written in C, R, and FORTAN and it focuses on interpreting statistical data, data analysis, as well as a superlative visualization tool.

Language Functionality

Python features an easily readable Syxtax and hence, it becomes easier for the programmers to code and debug it. Once the bugs are identified, it takes the minimal effort and time to eliminate it. For the statistical problems, R is the most suitable application as these models can be expressed just within a few lines. The display options for R is robust and you can develop ready-to-print graphs, just within a few lines.

Environment of the Language

Python comes with various integrated Development environments that users can choose from, of which, IPYTHON and SPYDER are the most popular ones. For R, the most popular IDE is RStudio. In today’s time, R is slightly ahead of Python for analyzing data, though experts believe that Python as all the capabilities to match this slight difference.

Miscellaneous Attributes

IPYTHON notebooks can function easily with data and Python and significantly reduces the workload, related to coding, output, as well annotation files that allows more time for dealing with the actual programming job. R features a rich ecosystem for cutting-edge packages of interface to interact with the languages that are open-source.

Language Users

Python is considered a multi-purpose language for the reason that this is ready to work and easy to understand, practiced by the majority of the programmers, across the world. On the other hand, R is best described as a programming language that has been conceived by the statisticians for their fellows.

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Both these languages are open-source applications and you can download them, free of cost. This is the most prominent point of difference with the commercial applications like SPSS, MATLIB, and SAS. Several new statistical innovations first come up as open-source packages of R, to some extent, prior to Python, before it enters the commercial domain.

The discussion made above, ends in tie between Python and R, as the better and more versatile data analysis tool. Hence, you need to pick one, as per the suitability to the purpose that you would love to accomplish.

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