Battle of the languages: Python vs R

Python and R are two of the most popular programming languages for data science. You can find some heated debates online where participants will passionately defend or decry one or the other. Pythonistas new to R will whine about the lack of type hints, decorators and dictionaries, whereas R gurus trying their hand at Python will despondently wail “What is all this ‘.’ notation everywhere?!” into the void. In this article, we let the two competitors face off in the ring and give a definitive answer to the question of which language is superior in the field of data science! (The answer might surprise you.)

Opponent backstories

Python and R are both very popular open-source languages that were developed with distinct purposes in mind. Python was created as a general programming language with an emphasis on versatility and readability. In contrast, R was specifically designed for statistical computing and data visualisation. This focus equips R with powerful packages and functions tailored for complex statistical modelling and graphical representation, whereas Python has a large community constantly developing new packages and the flexibility to perform a wider variety of tasks.

Gloves up!

Round 1: Performance

Python tends to outperform R in benchmarking tests on execution time and memory management. However, R’s optimised packages beat Python for specific tasks. In practice, the performance variations do not play a large role in daily tasks.?

Round 2: Data wrangling

Both Python and R have robust libraries that can effectively handle data wrangling tasks such as cleaning, transforming and exploring large data sets. In this regard, the two competitors are pretty evenly matched.

Round 3: Data visualisation

Again, both of the contenders provide great tools for users to create beautiful and high quality data visualisations. However, R is well-known for its data visualisation capabilities and beats Python with the ease of use and flexibility it provides.

Round 4: Machine learning and AI

When it comes to building machine learning and AI models, Python pulls ahead. R also provides packages that can perform these tasks and has a growing community. However, Python’s extensive libraries and performance in this area makes it the current go-to choice for most applications.??

Round 5: Statistical analysis

Statistical analysis is where R really shines as it was primarily created for this purpose. While Python has capable statistical analysis packages, R remains a gold standard for statistical work.

Round 6: Community support

Both Python and R have communities that are strong and helpful. One of Python’s strengths is its vast and diverse community that covers a wider range of applications, which includes data science. R’s community might be smaller in comparison, but it is also very vibrant and tends to focus more on statistical techniques and methodologies.?

Round 7: Integration and deployment

Python, being a general-purpose language, excels in integrating data science into larger applications and systems, making it an ideal choice for deploying models in production environments. In contrast, R is primarily designed for data analysis and statistics, which limits its adaptability for integration and deployment compared to Python.

And the winner is…

What a matchup! The Python vs R debate may suggest that you should choose either Python or R. However, the truth is that either of these tools does a splendid job with most of the daily tasks required of a data scientist, and most data scientists use both in the course of their careers. They are complementary tools and the “best” one will depend on the specific use case, project requirements and sometimes simple personal preference. The implementation of best development practices will always be far more important than the specific language used.?

If you only have a hammer

Python and R were created for entirely different reasons and, as such, they solve different types of problems in different ways. If you are familiar with only one of them, you will be biased toward it and not be able to properly capitalise on their respective strengths for diverse data analysis tasks. Teams with a versatile set of tools in their toolbox will be able to think more carefully about which problems will benefit the most from which solutions.

Train with the best!

At Fathom Data we believe in using the best tool for the job at hand. Our polyglot team boasts expertise in R, Python, SQL, C, C#, Java, Rust, HTML, JavaScript, and more. We provide technical training for everyone, from complete beginners to advanced users on topics like machine learning and DevOps, in both Python and R, because we are passionate about both! If you're looking to sharpen your skills for your data journey, whether as a team or an individual, we’d love to hear from you — reach out today!