Python vs. R: Which is better for data science?

If you’re trying to choose between Python and R, the project you have planned will be a deciding factor. While R is better for statistics and visualizing results, Python has a wide variety of features and solutions.

What are Python and R?

If you want to learn programming and are looking for a language that’s good for research work with analyses and statistics, you’re sure to come across Python and R sooner or later. The two programming languages are frequently used in data science, predictive analytics and data visualization, and both have large communities of users. At first glance, they have a lot in common, but we’ll also get into their differences below.

What are the pros and cons of R?

R gets its name from its developers, Ross Ihaka and Robert Gentleman. These two statisticians at the University of Auckland developed and released the language in the early 1990s. Their aim was a language that could carry out and display complex statistical analyses. The original target group was people with extensive knowledge of statistics and programming. R is based on the programming language S and is a free implementation.

R can be compiled and runs on UNIX platforms, Linux, Windows and Mac. It’s mostly used for developing statistics software and performing deep data analysis. Thanks to its numerous libraries, R can also be used for graphic displays of data. The language is open source and part of the GNU project. Although in the past R was primarily used in academic contexts, it now boasts integration with a number of other languages and programs and is used by many companies.

Pros of R

  • Open source: R is a language for everyone, at least in terms of cost and availability. It’s completely free and open source. That means it’s possible to use or build on it as your project requires.
  • Scope: The fact that R is open source also means that there are a number of user adaptations that have been made freely available. The chances that there’s already a solution to your problem are relatively high. Developers have already created around 20,000 packages based on R, which can often provide tailor-made solutions in specialized subject areas.
  • Compatibility: R works on a number of different platforms and has interfaces with various other languages and databases. So you can easily use R for a part of your project and embed it into a larger context.
  • User interface: A graphic interface was developed to increase the user friendliness of the language. The interface, called Rstudio, makes it significantly easier to work with R code, meaning projects can be implemented faster. Packages like Plotly also make it easier to create visualizations in the form of graphics and diagrams.
  • Community: R has an enthusiastic community behind it. Many R users are experts in their field and can provide valuable tips for solving your problems. The wide community also means there’s abundant documentation and the extra packages and libraries we mentioned above.

Cons of R

  • Performance: R isn’t a slow or weak language but you might experience delays when it comes to larger data sets. One reason for this is its single thread processing, which can only use one CPU at a time.
  • Learning curve: Since R is usually offered without a graphic interface, it can come with a hefty learning curve. It can take a while to get a handle on the various notation rules, restrictions and idiosyncrasies of the language. Knowledge of statistics are also a key prerequisite for working with R. Take a look at our R tutorial for beginners to get a first impression of the language.

What are the pros and cons of Python?

Python is significantly more well known than R and used by millions of people worldwide. The language was developed in 1991 by Guido van Rossum and has always had the goal of providing the simplest code possible. Many terms in the language are taken directly from English, making it easier to understand. Python code is also very clear and easy to read. It’s platform independent and object oriented. Thanks to its large community and open-source approach, it has numerous packages in the areas of deep learning, AI and data science. Check out our Python tutorial to get a closer look at the language.

Pros of Python

  • Versatility: Python is a versatile language in every sense. It can be used in a number of areas and thus makes it possible to take a holistic approach to projects. It’s also platform independent, meaning it can be used on a number of systems. And it has numerous interfaces with other programs, languages and databases.
  • Open source: Like R, Python is also open source and freely available. Continued development of Python is coordinated by the Python Software Foundation, but every user can adapt the language for their own projects.
  • Scope: Python users have developed a wide variety of packages. There are over 300,000 solutions available for download. That makes working on most projects significantly easier.
  • Learning curve: Python is one of the simplest programming languages out there. Despite its impressive scope, it can be learned and used in a relatively short amount of time. The code is also relatively clear, which makes it easier to work in teams and implement small projects on your own.
  • Community: Python has a large community that’s constantly creating documentation and libraries. It’s known for being helpful and supportive, so if you have questions or problems you’re likely to find someone to help you.

Cons of Python

  • Performance: As a dynamic language, Python could certainly be faster. That’s especially true when it comes to large data sets, leading many programmers to look for alternatives in that case.
  • Errors: Python isn’t a particularly error-prone language but if you’ve made a mistake in the code, you won’t find out until runtime. Regular and extensive testing are therefore very important when working with Python.
  • Visualization: Python is also lacking when it comes to visualizing statistical values and results. There are only a few tools that can deliver truly satisfying results.
  • Mobile devices: Python isn’t optimal for use on mobile devices. While there are a few solutions for this, most app developers opt for an alternative language with native compatibility for Android and iOS.

What’s the difference between Python and R?

Now that we’ve looked at the two languages on their own, we’ll consider some of the differences between Python and R.


The differences between the syntaxes of the two languages can be spotted immediately. R looks like this:

$ R
> myString <- "Hello! You’re using R."
> print (myString)

Python is a bit more concise:

>>> print("Hello! You’re using Python.")

Other differences between Python and R

In addition to syntax, there are a few other important differences between Python and R.

  • Uses: The two languages have very different approaches. R is primarily intended to be used for statistical analyses and visualizations and is very good at this. Python has a far more comprehensive approach and is also suitable for programming software and deep learning.
  • Scope and popularity: More and more people are using R outside of academia, but the language does still have its roots in science. Python is used by significantly more developers. That means that Python has far more packages than R.
  • Performance: Neither R nor Python is the fastest language out there. Python is, however, slightly faster and more powerful than R.
  • Formats: While Python can work with a variety of data formats, R is more limited. CSV, Excel and text files are the only formats it supports without additional tools.

Python vs. R: Which language should you learn?

So which language comes out ahead, Python or R? They’re both very powerful languages, so the answer has a lot to do with what you intend to do. If you’re primarily looking to create and visualize statistical models, R will be the better choice. If your project goes beyond statistics, Python will offer you far more possibilities.


In our Digital Guide you’ll find a number of articles surrounding Python. If you want to know how Python measures up to other languages, check out our articles Python vs. C++, Python vs. Java, Python vs. Matlab and Python vs. PHP.

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