Both R and Python are open-source programming languages that are frequently used for statistical analysis and data visualisation. They also enjoy the highest popularity among language models when it comes to deep learning. Which one is better and what are the key differences between R and Python?
Key Differences Between R and Python
Applications
R was designed specifically for statistical analysis and data visualisation. Python on the other hand has a much broader range of applications, including web development, machine learning, artificial intelligence, automation as well as data analysis.
Syntax
R’s syntax is specifically optimised for statistical analysis. Python’s syntax is again more general, allowing users to efficiently solve a variety of tasks.
Programming paradigms
R’s specific applications cause it to be more functionally oriented. In contrast, Python is capable of both functional as well as object-oriented programming.
Packages
R is well known for its library of packages specifically developed for statistical analysis and data visualisation. While Python also has packages for these tasks, it has a vast array of packages for different purposes due to its broad applicability.
Performance
Python is faster than R and can handle larger datasets as well as more complex tasks.
Our Take
Our Head of Data Science Dr. Patrick Vetter gave his opinion on the comparison between R vs. Python for data science: “We don’t care about the programming language used – in the end, it’s the data scientist that matters”.
Use Cases
Curious how we used R or Python for data science projects? Get a general idea with these two use cases:
Get in Touch
Unsure, whether you should use R vs Python for Machine and Deep Learning? Still uncertain whether R vs Python is better for data analysis? Get in contact with us and let us help you work with big data: /de/contact/
