The differences between Python vs R

About Python

Python is an interpreted high-level general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation. Its language constructs as well as its object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.

Python is dynamically-typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library.

About R

R is a programming language and free software environment for statistical computing and graphics. It is supported by the R Core Team and the R Foundation for Statistical Computing.[7] It is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, data mining surveys, and studies of scholarly literature databases show that R is highly popular;[8] since August 2021, R ranks 14th in the TIOBE index, a measure of programming language popularity.

Key Differences between R and Python

Although R vs Python is popular for a similar purpose, i.e. data analysis and machine learning, both languages have different features. Moreover, each language offers different advantages and disadvantages. Nevertheless, both R Programming vs Python are popular choices in the market; let us discuss the Top key Differences Between R Programming vs Python to know which is the best:

R was created by Ross Ihaka and Robert Gentleman in 1995, whereas Guido Van Rossum created Python in 1991. In addition, r is focused on coding language built solely for statistics and data analysis, whereas Python has flexibility with packages to tailor the data.

R is great when it comes to complex visuals with easy customization, whereas Python is not as good for press-ready visualization. In addition, r is hard to integrate with the production workflow. Mostly a statistical analysis and graphics tool, whereas Python integrates easily in a production workflow and can become an actual part of the product.

Let’s have a look at some more key differences.

• Speed and Performance: Although both languages are used for big data analytics. But performance-wise, Python is a better option for building critical yet fast applications. R is a bit slower than Python but still fast enough to handle big data operations.
• Graphics and Visualization: Data can be understood easily if it can be visualized. R provides various packages for the graphical interpretation of data. Ggplot2 gives customized graphs. Python also has libraries for visualization, but it is a bit complex than R. R has a pretty-printed library which helps in building publication-quality graphs.
• Deep Learning: Both r vs python languages have got their popularity with the rising popularity of data science and machine learning. While python offers a lot of finely tuned libraries, R got KerasR, an interface of Python’s deep learning package. Thus, both languages now have a very good collection of packages for deep learning. But python stands out in the case of deep learning and AI.
• Statistical Correctness: Since R is developed for data statistics, it provides better support and library libraries. Python is best used for application development and deployment. But R and its libraries implement a wide variety of statistical and graphical techniques for data analysis.
• Unstructured Data: 80% of the world’s data is unstructured. Data generated from social media is mostly unstructured. Python offers packages like NLTK, scikit-image, PyPI to analyze unstructured data. R also offers libraries for analyzing unstructured data, but the support is not as good as Python. Yet, both languages can be used for unstructured data analysis.
• Community Support: Both R vs Python has good community support. Both languages have a user mailing list, StackOverflow groups, user-contributed documents, and codes. So here is a tie between both languages. But both languages do not have customer service support. This means users have just online communities and developer’s documents for help.

Head to Head Comparison between R and Python

R:

• R codes need more maintenance.
• R is more of a statistical language and, also used for graphical techniques.
• R is better used for data visualization.
• R has hundreds of packages or ways to accomplish the same task. It has multiple packages for one task.
• R is easy to start with. It has simpler libraries and plots.
• R supports only procedural programming for some functions and object-oriented programming for other functions.
• R is a command line interpreted language.
• R is developed for data analysis; hence it has more powerful statistical packages.
• R is slower than python but not much.
• R makes it easy to use complicated mathematical calculations and statistical tests.
• R is less popular, but still, it has many users.

Python

• Python codes are more robust and easier to maintain.
• Python is used as a general-purpose language for development and deployment.
• Python is better for deep learning.
• Python is designed on the philosophy that “there should be one and preferably only one obvious way to do it”. Hence it has few main packages to accomplish the task.
• Learning python libraries can be a bit complex.
• Python is a multi-paradigm language. It means python supports multiple paradigms like object-oriented, structured, functional, aspect-oriented programming.
• Python strives for simple syntax. It has a similarity to the English language.
• Python’s statistical packages are less powerful.
• Python is faster.
• Python is good for building something new from scratch. It is used for application development as well.
• Python is more popular than R

Conclusion

Both R versus python dialects have their advantages and disadvantages; it's an extreme battle between the two. Python is by all accounts somewhat more famous among information researchers, yet R is likewise not a total disappointment. R is produced for factual examination and is generally excellent at that. While Python is a broadly useful language for application advancement. The two dialects give a wide scope of libraries and bundles; cross-library support is likewise accessible at times. Thus it thoroughly relies upon the client's prerequisites which one to pick.