By Christof Leitner

Both R and Python are open-source programming languages with a large community and cutting-edge features for data science.

Although both languages help bring the future to life through artificial intelligence, machine learning, and data-propelled revolutions, each one has its unique strengths and flaws.

But when it comes to R vs. Python, the most important thing is finding out which one is optimized for your specific needs. Let's take a closer look at each.

Python Programming Language

Python is one of the most popular, multi-purpose object-oriented programming languages with an emphasis on code readability. There are several Python libraries like Numpy, Pandas, and Matplotlib that support various data analysis tasks.

The Python programming language is well suited for large-scale deployment of machine learning. Additionally, Python libraries employ useful tools like Keras, sci-kit-learn, and TensorFlow that allow data scientists to develop sophisticated data models that can be integrated directly into a production system.

Pros

• Jupyter notebook allows data sharing with colleagues
• Has high code readability
• Has a smooth linear learning curve
• Can handle an enormous database
• More suitable for deployment and production
• Runs fast
• Easy to create new models from scratch

Cons

• Has relatively fewer libraries
• Prone to runtime errors

R Programming Language

The R programming language also has a rich environment with elaborate data models and valuable tools for data reporting. You can also perform interesting tasks like R web scraping.

Researchers and data science scholars prefer R for deep analytics because it has several tools and libraries to support:

• Creation of beautiful graphical visualizations
• Data cleansing and prepping
• Training and evaluating machine learning algorithms

Pros

• Provides an extensive catalog for data analysis
• Makes it easy to obtain primary results
• Provides more appealing graphical analyses
• Has the GitHub interface
• Can handle a huge database
• Runs locally

Cons

• Bears multiple interdependencies between libraries
• Has a steeper learning curve for beginners

R vs. Python: Key Difference

Python and R can be easy to distinguish based on their approach to data science. While Python offers a more general approach to data wrangling, R is mainly used for statistical analysis.

Programmers use Python for data analysis and machine learning in scalable production environments. R is used by data scientists for deep statistical analytics with just a few lines of code.

R vs. Python: Which Is the Best?

Interestingly, you can begin with both R and Python if you already know the algorithm and want to dive into data analysis right away.

But you can imagine Python as a remarkable player in Machine Learning integration and deployment owing to its influential libraries for math, statistics, and AI. You can use it to manipulate matrices and code algorithms.

Beginners also settle their hearts on Python due to its simplicity. But since it's almost always under constant development, it may not be the best option for communication and econometrics.

On the other hand, the R programming language is specially curated to answer problems in statistics, machine learning, and data science. Notably, it was developed by scientists and statisticians themselves for academics, scientists, and engineers.

As such, its powerful tools, packages, and communication libraries make it ideal for time series analysis, panel data, and data mining. If you're going to write a report, make use of beautiful graphs, or create a dashboard, R would do better than Python.

Bottom Line

Generally, the statistical gap between R vs. Python is narrowing, meaning both programming languages can handle some everyday tasks.

In the end, however, it's best to choose the one that's tailored to suit your specific needs. What's more, you can always learn a second language much easier once you fully understand your first one.

Ask yourself what your objectives are. If you're into statistical analysis, you can't go wrong with R. If your goal is deployment and large-scale applications, Python is an excellent choice.

You should also consider the amount of time you're willing to invest, as R has a steeper learning curve. Finally, it's always good practice to look at your company or industry's most used tool.

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ABOUT THE AUTHOR

Christof Leitner

Christoph is a code-loving father of two beautiful children. He is a full-stack developer and a committed team member at Zenscrape.com - a subsidiary of saas.industries. When he isn’t building software, Christoph can be found spending time with his family or training for his next marathon.