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?
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?
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.
