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
|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|
|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.|
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.