Julia vs. R: What’s the Difference?

Julia and R are two open-source programming languages with many applications in statistical computing, scientific computing, and data science. While R is an older, more classical language for statistical computing, Julia is a newer, faster language aimed at performing similar tasks. Each language has unique use cases, benefits, and potential drawbacks over the other. 

Explore each language further to learn about these differences and some other aspects to consider when choosing between Julia and R. 

What is Julia?

Created in 2012 at MIT by Dr. Viral Shah, Prof. Alan Edelman, Dr. Jeff Bezanson, and Stefan Karpinski, Julia is a high-level, just-in-time (JIT) compiled programming language. What makes Julia unique is that its compiled speed is similar to that of C, making it much faster than other popular languages like Python. This is because of Julia’s modern design and improved memory management compared to older programming languages. As of January 2025, Julia currently ranks 31st on the TIOBE Programming Community index with a .49 percent user usage rating [1]. 

What Julia is used for

While much of Julia’s focus is improving scientific computing and data analysis, it is also a general-purpose programming language that you can use to build applications, web user interfaces (UIs), and native UIs. Julia has a range of uses, including data visualization, data science, machine learning, and parallel computing. Some of the unique scientific ecosystems available in Julia include:

• BioJulia for biology

• JuMP Dev for operations research

• JuliaImages for imaging processing

• QuantumBFS for quantum physics

• JuliaDynamics for nonlinear dynamics

• QuantEcon for economics

• JuliaAstro for astronomy

• EcoJulia for ecology

It also contains many different packages for machine learning, including decision trees, linear models, clustering, some pre-trained models, and deep learning packages. 

Advantages of Julia

Julia’s modern design and thoughtful memory management offer many advantages over older programming languages. Some of the advantages in using Julia include:

• A powerful, dynamic programming language with a compiler as fast as C

• Free, open-source license from MIT

• Created with parallelism and distributed computing in mind

• Access to over 10,000 Julia packages with mathematical libraries, data tools, and more

• The ability to integrate packages from other languages like Python, R, C, C++, and Java

• It contains a readable interface, making it easy for beginners to identify errors in code

• Julia is dynamically typed, giving programmers flexibility when it comes to declaring variables

Disadvantages of Julia

Julia has many advantages as a new language, but that also creates some of its disadvantages as it is young and less established than older languages. Some of the disadvantages of Julia include:

• The language is young, making its adoption in certain fields low

• Julia’s newness and lower adoption mean that its library and package support is much smaller than Python or R

• The interoperability of Julia into other languages and embedded into desktop applications is poor, with little support

• Julia has a smaller user base and thus less community support than other languages

R

R is an open-source, statistical computing language created in 1995 by Ross Ihaka and Robert Gentleman as an alternative to the S language developed by John Chambers at the former Bell Laboratories. It is a fully functional computer language and environment for statistical techniques. In recent years, the scope of R has increased as it moved from a primarily academic language to a tool to perform data analysis. As of January 2025, R currently ranks 18th on the TIOBE Programming Community index with a 1.0 percent user usage rating [1].

What is R used for?

The initial uses of R were for statistical analysis, quality graphics production, and research into statistical techniques. While R is still very much used for these things, it's now also a tool for data analysis, data visualization, and predictive analytics. R is so versatile because of its very large number of packages, ease of package management, and in-depth statistical packages. Some of R’s other uses include:

• Exploratory data analysis (EDA)

• Machine learning

• Risk analysis and detection in finance

• Health care diagnostic research and biostatistics

Advantages of R

As a mature programming language with a large community, R has extensive features for statistical programming and data analysis. Some of the advantages of R include:

• R is free to use and fully open-source with a license as Free Software under the GNU’s General Public License

• Its own integrated development environment (IDE), known as RStudio, simplifies coding in R

• It has great backward compatibility with older versions of R so that old code runs smoothly

• You can link C code and Fortran to run intensive computational tasks directly in R

• Extensive package support for many kinds of statistics, analysis, and machine learning

• In-depth documentation to help new users understand R and advanced users get the most out of the language

Disadvantages of R

With R’s maturity as a language also comes some drawbacks as memory management and code bugs can make it difficult to use for large, modern projects. These disadvantages of R include:

• When it comes to large datasets and big data, R is slow to process as it only uses a singular CPU

• R has poor memory management since it stores data objects in the random access memory (RAM), limiting the amount of data you can process at one time

• The age of R sometimes makes it hard to diagnose bugs in code and difficult to work with the older architecture at times

Julia vs. R: Other things to consider

Julia and R are both capable programming languages from statistical analysis, data analysis, and machine learning. When choosing between each language you will want to consider a few factors that may push you one way or another. Some factors to consider:

• Speed: If you consistently need fast computations, Julia is much faster than R. While you can call C++ in R, Julia does not need to use two languages to compute at that speed. 

• Widespread adoption: R is much more widespread than Julia because of its maturity. If you work with many other programmers or scientists in your field, using Julia might be more difficult as it is not currently as optimized as R for different machines. However, if you mainly work alone, the speed and ease of learning Julia are advantageous. 

• Packages and libraries: While Julia has many effective libraries, it does not have near the number of users of R created throughout the language’s life, giving it many different statistical use cases.

Julia is a growing language, and as it develops you could expect it to gain the functionality of mature languages like R and Python but with significantly better computational speed. 

Get started in data analysis with Coursera

Julia and R are both effective programming languages for statistics, machine learning, and scientific computing. R is more widely used because of its maturity, and Julia is newer and faster. To improve your data analysis skills, try the IBM Data Analytics with Excel and R Professional Certificate or the Google Data Analytics Professional Certificate, both on Coursera. Upon completion of either program, you will gain a shareable certificate to include in your resume, CV, or LinkedIn profile.