Three simple steps
How to use this tool?
This free online converter translates Python source code into R in three simple steps.
- Step 1
Add your Python code
Type, paste, or upload the Python code you want to translate.
- Step 2
Convert the code
Click Convert to translate the source code into R.
- Step 3
Review the result
Review, copy, or download the resulting R code from the output editor.
See it in action
Examples of Python to R conversion
These examples show Python source code and the corresponding R translation. Generated results can vary as AI models are updated.
Example 1
Is String Palindrome
Program that checks if a string is a palindrome or not.
Python code
R code
Example 2
Even or Odd
A well commented function to check if a number if odd or even.
Python code
R code
Language comparison
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. |
Common questions
Frequently Asked Questions
How do I convert Python to R?
Paste your Python code into the input box, confirm the languages are set to Python and R, and click Convert. CodeConvert AI analyzes your Python code and generates equivalent R code in seconds, preserving the original logic and structure.
What are the main differences between Python and R?
Python and R differ in syntax, type system, standard libraries, and common idioms, so copying code line for line usually will not compile. The converter maps each Python construct to its closest R equivalent. See the comparison table below for the key differences between Python and R.
Is the converted R code accurate?
The AI produces high-quality R code that preserves the behavior of your original Python code and follows R conventions. It handles common patterns, data structures, and idioms for both Python and R. For large or performance-critical code, review and test the R output before using it in production.
Can I convert an entire Python project to R?
You can convert Python files one at a time by pasting each file's code. For a full migration, convert each file and then review how classes, dependencies, and project structure map from Python to R. Signing in for free raises the input limit to 25,000 characters per conversion for larger files.
Can I convert R back to Python?
Yes. CodeConvert AI converts in both directions, so you can convert R to Python just as easily using our R to Python converter. Try the R to Python Converter
Is the Python to R converter free, and do I need to install anything?
Yes, it is free and runs in your browser with nothing to install and no IDE extension required. You can convert Python to R without an account for up to 2 conversions per day. Sign in for free for higher limits.
What are the benefits of signing in?
Signing in unlocks CodeConvert AI's Pro converter with more powerful AI models, a built-in chat assistant, code execution, saved conversion history, and personal notes. Every free account includes 5 credits and supports up to 25,000 characters of input per conversion, with no credit card required.
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