R Vs Python Programming: Top 20 differences between R and Python

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There are too many programming languages that are used on a regular basis by developers. But these days the popularity of R programming and Python has increased up to a great extent. It helps a lot in artificial intelligence in machine learning as well as in data science. Many students these days are using R and Python for their projects as well. In this blog, we will discuss 20 differences between R and Python.

What is R programming?

What is R programming
What is R programming?

R is a programming language. It is a free software environment for doing statistical computing and graphics. The R language is mainly used among statisticians and data miners for developing statistical software and data analysis.

What is Python programming?

What is python programming
What is Python programming?

Python is an open-source programming language. It is an interpreted, high-level, general-purpose programming language.

Why R and Python?

R and Python are both open-source programming languages with a large community. On a regular basis new libraries or tools are added continuously to their respective catalog. R is mainly used for statistical analysis while Python provides a more general approach to data science.

Differences between R and Python Programming 

Features R Python
1.    Definition R is a programming language that is mainly used for statistical computing and graphical calculation. Python is an interpreter based and high level and programming language that consists of dynamic semantics.
2.    Scope Mainly used for statistical Programming Used for web development applications and analysis of the data
3.    Integration It is integrated with locally run programs It is an integrated web development apps.
4.    IDE RStudio, RGUI Spyder, Ipython
5.    Package distribution

 

CRAN PyPi
6.    Suitability People with no experience can learn this New learners to experienced ones
7.    Packages It has 100 of packages It has a few packages
8.    Approach Complex data analysis Streamlined approach
9.    Analytic Modelling High level is achieved here Low to medium only
10. Tool advancements Low advancement in R Medium level advancement in Python
11.  Description It acts as a golden child of data science It acts as a king of data science programs
12. Ranking R is at 6th place Python is at first rank
13. Job Opportunity R provides less job opportunities Python develops more job opportunities to its developers
14. Users Number of users are less Numbers of users are more
15. Learning curve It is difficult to learn at the beginning It is easier and smooth to learn
16. Salary Salary of R programmer is less than Python developers Salary package is comparatively high
17. Disadvantages It has a slow learning curve It has fewer libraries as compared to R
18. Libraries It has more libraries It has fewer libraries
19. Primary Users Scholar and R&D Programmers and developers
20. Good for It is good for analyses and exploring data sets It is better for data manipulation and repeated tasks.

Conclusion

Many Startups and MNCs are dependent on these technologies in order to expand and leave a mark in the industry. Many students are learning these technologies to become more skillful. Those who want to enhance their career in machine learning and data science must learn R and Python. It offers a number of libraries to work on. Both languages are highly demand. In this blog, we have discussed 20 differences between R and Python. If you are having any doubt, feel free to ask me in the comment box.

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