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R vs. Python for data science: Explainer and learning tips


Python and R are considered essential data science programming languages. Ideally, you’d master both for a well-rounded programming foundation, but if you’re new to data science, where’s the best place to start?

Read on to learn more about how each programming language is used in data science along with tips for choosing which to start learning first.

What’s the difference between Python and R?

While the R language is more specialized, Python is a general-purpose programming language designed for a variety of use cases.

If this is your first foray into computer programming, you may find Python code easier to learn and more broadly applicable. However, if you already have some understanding of programming languages or have specific career goals centered on data analysis, R language may be more tailored to your needs.

There are also plenty of similarities between Python and R languages, so a background in one can inform the other. For example, both Python and R are popular open-source programming languages backed by thriving communities. Both can be also practiced in the language-agnostic environment, Jupyter Notebooks, along with other programming languages such as Julia, Scala, Java, and dozens more.

Python: The all-purpose programming language

Python is known for its simplicity and readability, making it ideal for beginners and experts alike. Its extensive libraries and community support facilitate efficient development in web programming, data analysis, artificial intelligence, and scientific computing. Python's versatility and ease of integration with other languages and tools make it useful for a wide range of programming tasks and projects.

Python is an object-oriented programming language, like Javascript or C++, providing stability and modularity to projects, no matter the size. It offers a flexible approach to web development and data science that feels intuitive even if you’ve never learned a programming language before.

Picking up Python gives programmers the skills necessary to work in business, digital products, open-source projects, and various web applications outside of data science. The language is a small part of the Python ecosystem; popular libraries include:

  • NumPy (numerical analysis)

  • SciKit-learn (predictive analysis)

  • Keras (deep learning and artificial intelligence)

  • SciPy (scientific computing)

  • Seaborn (statistical data visualization)

  • Folium (geospatial data visualization)

  • Pandas (data analysis)

  • Matplotlib (object-oriented API for embedding plots)

  • PyCharm (integrated development environment [IDE] for Python)

"The hardest part of anything is starting it and Python is the first big step to data science. People are astonished at how easy Python is."

“The hardest part of anything is starting it and Python is the first big step to data science,” says Joseph Santarcangelo, PhD, IBM data scientist, and instructor for several edX data science courses and programs, from Python basics to deep learning. “People are astonished how easy Python is. When you look at programming, it seems like a pretty abstract concept. It's pretty difficult. If you make a little mistake everything is wrong. So people usually get pretty scared. And then people are like oh wow that’s it?”

3 Reasons to learn Python for data science

1. Python is beginner-friendly: Python uses a logical and approachable syntax that makes it easier to identify the purpose for strings of code and relies less on the formal approach of past languages. This focus on code readability reduces the learning curve and smoothes some of the challenges of learning programming languages for the first time.

2. Python is multipurpose: Python isn’t limited to work within the data science community. Developers use Python to build all kinds of applications, so it’s a helpful language to use if you plan to focus on a variety of tasks within the computer science field. Python also works well with web-based applications and supports many kinds of data structures, including those with SQL. Plus, it’s easy to find different datasets for whatever project you’re working on or create your own using products within the Python ecosystem.

3. Python is scalable: Python operates faster than R, allowing it to grow and scale alongside projects. For those working in production, building pipelines, or executing large-scale production, it offers the efficient workflows necessary to get those off the ground. This speed is the foundation for Python’s production readiness. It allows you to build full-scale machine learning pipelines for insights that keep up with the speed of business. Plus, the modularity of the language ensures that you can build something flexible.

R: The data analysis powerhouse

R programming is a domain-specific language used for data analysis and statistics. It uses specific syntax employed by statisticians and is a vital part of the research and academic data science world.

R follows a procedural model for development. Instead of grouping data and code into groups like object-oriented programming, it breaks down programming tasks into a series of steps and subroutines. These procedures make it more simple to visualize how complex operations will happen.

Like Python, R has a robust community, but with a specialized focus on analysis. R doesn’t offer general-purpose software development like Python, but it handles these specialized data science projects better because that’s the only focus. The R ecosystem includes:

  • RStudio (an R-based IDE)

  • CRAN (the Comprehensive R Archive Network)

  • Tidyverse, a popular collection of R packages

  • dplyr (a set of functions enabling data frame manipulation)

  • R packages, reproducible R codes, and functions

  • Ggplot2, an open source data visualization package

In short, R offers specialization for analyzing big data, but you won’t be able to use it for general purpose web development.

“As with any vibrant open source software community, R is fast moving. This can be disorientating because it means that you can never finish learning R. On the other hand, it makes R a fascinating subject: there is always more to learn."

“As with any vibrant open source software community, R is fast moving. This can be disorientating because it means that you can never finish learning R. On the other hand, it makes R a fascinating subject: there is always more to learn. Even experienced R users keep finding new functionality that helps solve problems quicker and more elegantly,” said Radha, a data analyst in India and edX learner who used the Data Science: R Basics course from HarvardX, part of HarvardX’s Data Science Professional Certificate program, to brush up on the constantly evolving programming language.

3 Reasons to learn R programming for data science

R isn’t a general purpose language, but depending on where or how you plan to work, it could offer a lot of perks that aren’t available with a general purpose language.

1. R is built for statistics: Heavy statistical analysis is possible with Python, but you won’t get the syntax-specific libraries and functions as you do with R. The language makes it much more intuitive to build and communicate results from these specific types of programs. Statisticians and data analysts use R to manage large datasets more easily using standard machine learning models and data mining.

2. R is academic: R is almost a default for working in academia. R is well suited for a subfield of machine learning known as statistical learning. Anyone with a formal statistics background should recognize the syntax and construction of R.

3. R is intuitive for analysis: R may not work with a wide variety of projects, but it is the best choice for analysis and inference work. If you plan to work in a specialized field, you’ll want a specialized programming language. R also offers a powerful environment ideally suited to the types of data visualizations data scientists employ.

Which programming language should I learn: Python or R?

If your goal is to pick up computer programming more broadly, Python is the way to go. If your goal is to focus purely on statistics and data applications, R might have the edge. To decide whether to start learning Python or R first, ask yourself a few questions:

  • What are your career goals? Deciding between business and academia, for instance, can help make it clear which will serve you better in the beginning. Thinking about how much you’d like to keep your options open or which projects are most important to you can help, too.

  • Where do you envision you’ll spend most of your energy? If you plan to stick with the statistical analysis inside most research projects, R could edge out Python. However, if you want to build production-ready systems, you might need more flexibility.

  • How do you plan to communicate your findings? Looking at the different ways Python and R can aid in data visualization can also help narrow down your first step.



Explore other popular programming languages for data science, from MATLAB to JavaScript.

Is Python or R easier?

Python is much more straightforward, using syntax closer to written English to execute commands. However, R makes it easier to visualize and manipulate data if you have other languages under your belt. It’s statistics-based, so the syntax here is more straightforward for analysis.

R may require more work upfront than Python does. However, once you’ve gotten the hang of the syntax, R can make certain types of tasks much easier. The more experience you have with programming languages, the easier it is to pick up another.

“My advice either way is don’t give up — if you're not that great with one language try another one,” says Ben Tasker, Technical Program Facilitator of Data Science and Data Analytics at Southern New Hampshire University and instructor for edX MicroBachelors programs in data management and business analytics. “I was pretty horrible at coding in Python when I started my data science career. So I switched over to R for some reason even though a lot of people state that R is harder to learn. I learned it much more quickly and then I switched back over to Python and became more comfortable with it, and now I just use Python, I don't use R at all.”

At a glance: Tips for choosing between Python and R

People who choose Python:

• Work in business-oriented data science.

• Create machine learning algorithms.

• Work in a variety of industries.

• Require a flexible language.

• Plan to create projects that scale.

People who choose R:

• Work in analytics or statistics heavy data science areas.

• Work in academia.

• Need the language-specific syntax of statistical processes.

• Perform statistical analysis or specialized analytics work.

• Need dynamic output for communicating results.

It’s best to choose Python if:

• You don’t have any programming experience.

• The primary goal is production or deployment.

• You want to build new models from scratch.

• The code for projects should be readable.

It’s best to choose R if:

• You plan to work in research or academia.

• The work is heavy on statistics and analysis.

• You want to make use of extensive libraries for existing solutions.

• The syntax-specific features are important.

• Communication of complex results is key.

Bottom line: Python for beginners, R for research

Ultimately, learning Python and R will help you gain a competitive edge in data science. Explore data analytics boot camps, courses, and programs in a variety of data science and analytics topics to help you take your next step.

Last updated: January 2024