R vs Python for machine learning

Machine learning (ML) is one of the most profitable sectors of software development right now. That's because of how useful machine learning techniques are in the rapidly growing field of data science. Data science, a field of applied mathematics and statistics, gleans useful information by the analysis and modeling of large amounts of data. Machine learning involves developing computer systems that learn and adapt using algorithms and statistical models. Applying ML techniques to data science makes it possible to advance from insights to actionable predictions.

Python is among the most popular and easy-to-learn programming languages today, and it's widely used in data science and machine learning. That said, R is rising in popularity for its statistical computing and graphing capabilities, which are essential in data science. Today we'll compare the benefits and disadvantages of using these two programming languages for machine learning.

We'll cover:

What is machine learning?
What is Python?
What is R?
R vs Python: Which is better for machine learning?
Wrapping up and next steps

What is machine learning?

Artificial Intelligence (AI) is the field of creating intelligent behavior in computers and has applications as wide-ranging as self-driving cars to natural language processing (NLP). Under the AI umbrella, machine learning is the branch of computer science concerned with systems and algorithms that perform data analysis tasks to learn and make intelligent decisions. For instance, ML algorithms help display relevant content to us on social media. They also provide insights and predictions for businesses so they can adapt to their markets faster.

The monumental amount of data in the world today, from clicks on a website to how long you look at a pair of jeans online, is called Big Data. Data scientists and statisticians perform data mining and extract trends from these datasets with machine learning to make informed decisions. The two main programming languages used for ML systems are Python and R. Next, we'll look at both to see which is better for machine learning.

What is Python?

Python was released in 1991 by Guido van Rossum at Centrum Wiskunde & Informatica in the Netherlands. It's a general-purpose, object-oriented programming language with a huge set of open-source data science libraries and frameworks, including Pandas, Numpy, Keras, TensorFlow, Matplotlib, SciPy, Scikit-learn, and Seaborn. For these reasons, Python is often recommended for people who want to pursue machine learning and data science. Furthermore, Python is a multi-purpose language, so you can apply it to use cases like creating web applications, workflow automation, analytics scripting, and more.

Python also has easy-to-read syntax, and this code readability makes it simpler for new users to work on a project.

What is R?

R is a programming language specifically created for statistical analysis and data visualization. It was developed by Robert Gentleman and Ross Ihaka at the University of Auckland in New Zealand. The first official open-source release of R was published in 1995 and generally replaced the S language. It’s another popular programming language, and its capital is rising with the growth of machine learning and data science.

RStudio, the most popular R integrated development environment (IDE), is available on multiple platforms. Furthermore, the rich R ecosystem has plenty of packages suitable for ML systems. For example, caret, ggplot2, nnet, and the set of packages known as the tidyverse are all available in the Comprehensive R Archive Network (CRAN). R is an especially popular choice for statistical methodology and relies heavily on statistical models.

R vs Python: Which is better for machine learning?

Python and R are both open-source programming languages with huge selections of libraries and the support of large communities. But there are key differences between them.

Libraries: R has a larger variety of packages specifically for statistics because of its origins in statistical models.
Syntax: Python has an easy-to-read syntax, while R, on the other hand, is known for having difficult syntax. R programming can have a steeper learning curve.
Graphics and visualization: While visualization libraries are available in Python, R was made to present and visualize data with graphics, which means it's much faster than Python for graphics and statistical analysis. R’s base graphics module lets you create simple charts and plots, and with packages like ggplot2 you can make more advanced displays, such as complex scatter plots with regression lines.
Integrations: R is also challenging to integrate in engineering environments compared to Python, although this is improving. Since R is limited to statistical analysis and visualization, it's not an ideal choice for an ML program that needs to be integrated with a large-scale environment that fulfills a range of operations.

At a glance, Python's versatility makes it seem like a winner for ML. While it's a great choice, R is quite useful for statistical analysis, and so many organizations use both languages. While you might start with just one, it could be worth learning both. For instance, you can do initial data analysis and exploration with R to take advantage of its speed, then switch to Python for shipping data products. (Python supports R functionality with the RPy2 package.)

Wrapping up and next steps

In this article we identified the differences and similarities between Python and R for machine learning, but there is much more. Whether you're just dipping your toes into machine learning or building on your skills, Educative has several learning options available.

For Python, the best place to start if you have some programming background is Python 3: From Beginner to Advanced. However, if you are truly starting with no Python experience, the course Learn Python 3 From Scratch can get you going.

Businesses are increasingly looking for R users. To learn more about R, the free course Learn R From Scratch uses practical examples and assumes no prior knowledge. It also introduces more advanced topics like exception handling.

If you're committed to entering the field of machine learning, the course Become a Machine Learning Engineer, guides you through essential ML techniques with modules in image recognition, natural language processing, deep learning, and preparing for the machine learning interview.

Happy learning!

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