Due to its shorter learning curve, easier syntax, and better readability than R, Python is a great choice for beginners. Moreover, it’s a more versatile language suitable for various tasks. After gaining proficiency in it, you can then proceed to learn R. On the other hand, if you’re already familiar with the basics and aiming for tasks where R excels, choosing R as your primary language might be a better first step.
R vs. Python for machine learning
9 min read
May 16, 2025
content
Key takeaways:
Python and R both have strong community support, extensive libraries, and resources to cater to a wide range of data science and machine learning projects.
Python’s clear syntax and versatile ecosystem make it ideal for beginners and scalable machine learning projects, while R excels in statistical analysis and academic research.
Python’s libraries, like scikit-learn and TensorFlow, are powerful for building machine learning pipelines, while R’s ggplot2 and tidyverse provide advanced tools for data visualization.
Python integrates seamlessly with APIs, web frameworks, and production systems, making it suitable for end-to-end data applications, whereas R is best for exploratory data analysis and statistical modeling.
RStudio is optimized for statistical workflows, while Python’s Jupyter Notebook offers an interactive environment suitable for diverse data science and development tasks.
Are you stuck in a dilemma of choosing between Python and R for machine learning?
Here’s what you need to know.
Python dominates in machine learning, but R isn’t out of the game.
If you’re deciding which to learn or use for your next project, this guide breaks down how the two compare in syntax, libraries, real-world use cases, and more.
A Practical Guide to Machine Learning with Python
A Practical Guide to Machine Learning with Python
This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.
What is machine learning?#
Under the
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, enabling them to make informed decisions. The two main programming languages used for ML systems are Python and R. In this blog, we’ll discuss both to see which is better for learning machine learning.
Machine Learning for Beginners
This course explains machine learning for absolute beginners by building a visual understanding of the underlying concepts. It covers some foundational mathematics behind the machine learning models and then guides you in coding for models to solve real-world machine learning problems. You’ll begin by understanding the limitations of traditional coding techniques in solving machine learning problems. Next, you’ll get familiar with the machine learning process. Then, you’ll build your first machine learning model from scratch—a single perceptron. The course then takes you from a single neuron to a multilayer perceptron to solve a non-linearly separable classification dataset. Finally, the course introduces Python’s library, scikit-learn, where you’ll learn to build models for classification, regression, and unsupervised clustering. This course aims to make you a lifelong learner and serves as a great starting point for a successful career in 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.
Building Blocks of Coding: Learning Python
This course is designed for you to learn Python from scratch, making it ideal for anyone interested in Python programming for beginners. Using Edward the robot to gamify concepts, you'll explore Python programming fundamentals, from built-in functions to user-defined functions and basic data types. You’ll also learn how to write programs using sequential, selective, and iterative structures. By completing hands-on projects, you'll gain the skills needed to kickstart your career as a Python developer and become a lifelong learner in computing.
R is a programming language specifically created for statistical analysis and data visualization. Robert Gentleman and Ross Ihaka developed it 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. R is another popular programming language, and its popularity 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, net, and the tidyverse set of packages 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.
Learn R
In today’s data-driven world, the ability to analyze large datasets is becoming a vital skill across industries. R, one of the most powerful languages for data analysis. This interactive R course is designed for beginners, with no prior knowledge of R programming required. You will begin with fundamental concepts, such as R variables, data types in R, and basic functions like R print and R cat. As you progress, you will dive into more complex topics, including R vectors, lists, arrays, matrices, and data frames in R programming. You’ll also learn how to perform operations using arithmetic operators in R, relational operators in R, and logical operators in R. Further, the course covers advanced features like if statements in R, switch statements in R, loops in R, and recursion in R. Finally, you’ll gain hands-on experience with file handling in R, exception handling with try and except in R, and object-oriented programming using S3 and S4 classes in R.
R vs. Python for machine learning: A complete comparison#
Python and R are open-source programming languages with large library selections and the support of large communities. However, there are some key differences between them.
1. Libraries#
Python has a wide selection of general-purpose machine learning and data science libraries, such as scikit-learn, TensorFlow, and pandas. These libraries cater to various tasks in data manipulation, machine learning, and even web development.
R, rooted in statistics, offers an extensive collection of packages specifically designed for statistical analysis, such as caret, nnet, and ggplot2 for data visualization.
2. Syntax and learning curve#
Python: Known for its clean, readable syntax, Python closely resembles the English language, making it beginner-friendly and easy to learn, even for those with little programming experience. For example:
# Calculate the mean of a list in Pythondata = [1, 2, 3, 4, 5]mean = sum(data) / len(data)print(mean)
An example of Python syntax
R: While powerful for statistical computing, R has a steeper learning curve due to its unique syntax, especially for newcomers without a background in statistics. For example:
# Calculate the mean of a vector in Rdata <- c(1, 2, 3, 4, 5)mean <- mean(data)print(mean)
An example of R syntax
3. Graphics and visualization#
Python: Python offers robust visualization tools like Matplotlib, seaborn, and Plotly, though they require more coding and customization.
R: Built with data visualization in mind, R provides seamless integration with libraries like ggplot2, enabling users to create visually rich, interactive, and complex plots with less effort.
4. Integration and compatibility#
Python: Known for its ability to integrate easily into production environments. Python works well in large-scale engineering systems and with other technologies like web frameworks (Flask, Django), making it ideal for ML models that need deployment.
R: While R excels in data analysis and statistics, it is often considered harder to integrate into non-statistical systems. However, its capabilities are improving with tools like Rpy2 for Python-R integration.
5. Purpose and use cases #
Python: A versatile, general-purpose programming language. Besides machine learning, Python is commonly used for web development, automation, scripting, and software development.
R: Specialized in statistical analysis and visualization. R shines in exploratory data analysis, statistical modeling, and visual representation, particularly in academia and research.
6. Ease of learning #
Python: Due to its simplicity, clear syntax, and broad application, Python is often the preferred language for beginners in data science and machine learning.
R: Although R’s focus is on statistics and its syntax is more complex, it’s an excellent choice for those who want to dive deep into statistical analysis and visualization.
7. Integrated development environments (IDEs)#
Python: PyCharm, Visual Studio Code, and Jupyter Notebook are commonly used for Python. Jupyter is particularly popular for data science projects due to its interactive nature.
R: The most popular IDE for R is RStudio. It is ideal for data analysis and visualization tasks and offers integrated support for scripts, data frames, and plotting.
Comparison of R and Python for machine learning
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 therefore, 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.
When to choose Python vs. R for your ML project#
Choosing between Python and R depends on your project needs, goals, and expertise. Both languages excel in their respective domains, so understanding their strengths can help you make the right choice. Here’s when you should use Python vs. R:
Python is a good choice if#
You are a beginner in programming and prefer an easy-to-learn syntax.
You need a versatile language that can handle a wide range of tasks, including data analysis, machine learning, web development, and automation.
You require robust libraries like TensorFlow, scikit-learn, and pandas for machine learning.
You need to integrate with APIs, databases, or cloud services for large-scale or dynamic applications.
You need seamless integration into production systems and engineering environments.
Real-world example: Imagine a data science team at a startup that needs to quickly build and deploy machine learning models for predictive analytics. Python, with its ease of use and integration capabilities, would allow the team to rapidly prototype and scale their models for production environments.
In short, Python is an excellent choice for production-ready projects, scalable machine learning systems, and projects requiring multi-functional development.
Ready to learn Python and build meaningful applications? Start with the project “Stock Market Data Visualization Using Python”.
R is a good choice if#
Your project is focused on statistical analysis or data visualization.
You need advanced graphics and visualization tools like ggplot2.
You work in academia or research, where R is commonly used due to its statistical focus and widespread adoption.
Real-world example: Consider a research group working on a complex statistical model for clinical trials. In this case, R would be the ideal tool due to its rich ecosystem of statistical packages and superior visualization tools that make it easier to understand and communicate results.
R is an ideal choice for academic research, statistical analysis, and projects that demand advanced data visualization and exploration.
Try out the “Uber Data Analysis Using the R Language” project to get hands-on practice with R.
Wrapping up and next steps#
In this blog, we explored the strengths and limitations of Python and R for machine learning. Whether you’re a beginner or looking to advance your skills, here are some actionable next steps:
-
Master the basics of machine learning concepts like supervised and unsupervised learning, feature engineering, and evaluation metrics.
-
Explore key tools, specifically learn Python libraries like pandas, scikit-learn, and TensorFlow, or R packages like ggplot2 and tidyverse for practical applications.
-
Work on real-world datasets using platforms like Kaggle or the UCI Machine Learning Repository to apply your skills.
-
Start small projects to build hands-on experience, such as predictive modeling or data visualization tasks.
-
Stay updated by following blogs, research papers, or online communities to keep pace with industry trends.
Happy learning!
Frequently Asked Questions
Should I learn R or Python first?
Should I learn R or Python first?
Is machine learning better with R or Python?
Is machine learning better with R or Python?
Can I use both Python and R in a single project?
Can I use both Python and R in a single project?
How do Python and R compare in terms of visualization capabilities?
How do Python and R compare in terms of visualization capabilities?
Written By:
Julia Granstrom
Free AI Mock Interviews
Coding Interview
Coding PatternsFree Interview
Gain insights and practical experience with coding patterns through targeted MCQs and coding problems, designed to match and challenge your expertise level.
System Design
YouTubeFree Interview
Learn to design a video streaming platform like YouTube by tackling functional and non-functional requirements, core components, and high-level to detailed design challenges.
Free Resources
TRENDING TOPICS
