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In Development Last updated: June 23, 2023
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When it comes to data science and machine learning development, two programming languages stand out as being very popular – R and Python.

Using either language will help data science teams explore, analyze, visualize, learn from, and work with data easily. However, you must know the key differences between the languages if you want to choose which language to use. This article explores and explains the differences.

R: An Overview


R is a programming language for statistical computing and creating graphs. It was first released in 1995. Robert Gentleman and Ross Unaka developed it at the University of Auckland in New Zealand. R was developed as a practical implementation of the S programming language.

While R is a programming language, it is distributed as so much more. It includes a programming environment – R Studio. This environment enables you to create visualizations, manage data and write R code with syntax highlighting and debugging support.

R is open-source and runs on all major operating systems, including Windows, MacOS, and Linux. To demonstrate what a sample program written in the R programming language looks like, here’s an example program that generates a set of random values that follow a normal distribution.

# Set the seed for reproducibility

# Generate 10,000 random numbers following the normal distribution
random_numbers <- rnorm(10000)

Python: An Overview

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Python is one of the most popular languages in the world. It is a versatile, general-purpose programming language that supports multiple programming paradigms. It is used widely for machine learning, web development, and writing short scripts for automation. Python is easy to learn and used as a first language by most people learning to code.

Guido van Rossum developed Python, which was first released in 1991. Despite its age, it has been updated and modernized over the years; most notably, the release of Python Version 3 was a huge leap from Python Version 2.

Like R, Python is open-source and has a translator for every major operating system. In addition, there are different browser-based environments for running code, such as Jupyter Notebooks. Python is general-purpose but has packages and libraries for data science and machine learning.

Here’s an example program that uses the NumPy package to create 10000 random numbers that follow the Normal distribution.

import numpy as np

# Set the seed for reproducibility

# Generate 10,000 random numbers following the normal distribution
random_numbers = np.random.randn(10000)

Head-to-Head: R vs. Python

As you can see, the languages are similar but carry some fundamental differences. When deciding which language to learn or use for your next project, here are some key differences to consider.


R is a programming language created to provide an easy way to analyze data and create visualizations. Its use is mainly limited to statistics, data science, and machine learning.

On the other hand, Python is a general-purpose language designed to be elegant and simple. Therefore, it is widely used in Artificial Intelligence and Web Development fields.


R is designed to be used by statisticians. As a result, it offers a simple interface with functions you would need for statistics.

Python is used widely by different developers of all experience levels. Because of its versatility, it is not used by any single profession – but by different professions, such as software engineers, analysts, and scientists.

Data Analysis and Visualisation

One of Python’s most popular applications is data science – which involves analyzing and visualizing data. However, Python does not support this application natively and instead relies on libraries and packages such as Pandas and Matplotlib.

Unlike Python, which is general-purpose, R is made to be used for Data Science. As a result, R has functions for data analysis and plotting graphs out of the box. In addition to the built-in functions, there are packages that you can use for further analysis.


R’s simplicity and abstractions come at the cost of performance. It is not very fast. In programming, being slow means running a few milliseconds slower than in other languages. Therefore R’s slowness might be barely noticeable to users.

Like R, Python also provides high-level abstractions and simplifications, making it more flexible but slower. To improve the performance of Python programs, libraries such as NumPy and PyTorch use C++ or C under the hood and provide bindings in Python. This enables Python to run faster.

Usage in Machine Learning

In addition to visualization and analysis, R can be used to build machine learning models using regression, classification, and clustering algorithms. It has packages such as Caret, lattice, DataExplorer, and Dalex.

Python is the most popular language used in Machine Learning. It has a rich library ecosystem, such as Tensorflow, PyTorch, and Jax. It is also the most widely supported by Machine Learning Infrastructure platforms.

Learning Curve

R was designed to be simple. However, its syntax can take some time to get used to and is slightly more difficult than Python, in my experience.

Python is one of the most beginner-friendly programming languages designed to be elegant and simple. It has a gentler learning curve compared to R.


R has a large community – albeit smaller than Python’s. It is still significant as it has more than 10,000 packages for you to use. It also has many educational resources to learn from as the language is still incredibly popular.

This is another area where Python shines. It probably has the largest community of any programming language in the world. It has open-source packages for almost anything, including machine learning and data handling. There are many free and paid educational resources to learn Python and its many tools created by the community.


Python is one of the most popular programming languages in the world, rivaled by the likes of JavaScript and Java. It is also one of the most loved languages because of its simplicity and versatility.

R is definitely not as popular as Python, but it is still popular enough not to be an obscure programming language used by no one. On the contrary, it is used by big tech companies, and a significant number of job openings require expertise in R.


R is primarily meant for statistical computations. Therefore it is not used for anything beyond this use case.

On the other hand, Python is versatile. It is widely used for different purposes, and data science is just one of the many.


Python is distributed solely as an interpreter. The IDE is up to the user. The two most popular IDEs are VsCode and PyCharm – which are compared in this post. In addition, you can also use Jupyter Notebooks to run your Python interactively in cells.

The primary IDE used for R is R Studio which is available for MacOS, Windows, and Linux.

Common Applications

R, a statistical language, is commonly used for Machine Learning, Statistics and Analytics, and Data visualization.

While Python, a general-purpose language, has a wide range of uses, including Machine Learning, Web development, Data analysis, and Numerical computation.

Both R and Python have big tech users. These include Google, Meta, Accenture, and Microsoft for R. Python users include Tesla, Google, Meta, and Spotify for Python.

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Final Words

Python and R are strong languages that will likely be used in the foreseeable future. Learning either one is a good investment as it equips you with the skills to wrangle data and draw insights in this increasingly data-driven world.

Next, check out data analytics courses to grow your career.

  • Anesu Kafesu
    Full stack web developer and technical writer. Currently learning AI.
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