CodeConvert AI - Convert code with a click of a button

CodeConvert

How to use this tool?

This free online converter lets you convert code from Python to R in a click of a button. To use this converter, take the following steps -

Type or paste your Python code in the input box.
Click the convert button.
The resulting R code from the conversion will be displayed in the output box.

Key differences between Python and R

Characteristic	Python	R
Syntax	Python has a simple and easy-to-learn syntax that emphasizes readability and reduces the cost of program maintenance. It uses indentation to create blocks, and its syntax allows programmers to express concepts in fewer lines of code than languages like Java or C++.	R has a syntax that is similar to the English language, making it easy to learn and read. However, its syntax can be more verbose than Python, and it requires more typing to accomplish the same tasks.
Paradigm	Python is a multi-paradigm language that supports object-oriented, functional, and procedural programming styles.	R is primarily a functional programming language, but it also supports object-oriented and procedural programming styles.
Typing	Python is a dynamically typed language, which means that variable types are determined at runtime. This makes it easier to write and read code, but it can also lead to errors that are not caught until runtime.	R is also a dynamically typed language, which means that variable types are determined at runtime. However, R has a more strict type system than Python, which can help catch errors earlier in the development process.
Performance	Python is generally slower than compiled languages like C++ or Java, but it is faster than languages like R or MATLAB. However, Python has a number of libraries and frameworks that can help improve its performance for specific tasks.	R is generally slower than Python or compiled languages like C++ or Java, but it is faster than languages like MATLAB. However, R has a number of libraries and frameworks that can help improve its performance for specific tasks.
Libraries and frameworks	Python has a large and active community that has developed a wide range of libraries and frameworks for tasks like data analysis, machine learning, web development, and more. Some popular libraries include NumPy, Pandas, and TensorFlow.	R also has a large and active community that has developed a wide range of libraries and frameworks for tasks like data analysis, machine learning, and more. Some popular libraries include ggplot2, dplyr, and caret.
Community and support	Python has a large and active community that provides support through online forums, user groups, and conferences. It also has extensive documentation and a large number of tutorials and resources available online.	R also has a large and active community that provides support through online forums, user groups, and conferences. It also has extensive documentation and a large number of tutorials and resources available online.
Learning curve	Python has a relatively low learning curve, especially for beginners. Its simple syntax and large community make it easy to find resources and get help when needed.	R has a steeper learning curve than Python, especially for beginners. Its syntax can be more verbose, and it can be more difficult to find resources and get help when needed.

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