What Is R Programming? Definition, Use Cases and FAQ

Written by Coursera Staff • Updated on

R is a free, open-source programming language tailored for data visualization and statistical analysis. Find out more about the R programming language below.

[Featured Image] A person in a checkered button-down shirt works with R programming at a desk with a laptop, monitor, and keyboard. There are colored Post-Its hanging on the monitor and wall to the right.

Key takeaways

The R programming language is open-source, or free under the Free Software Foundation’s GNU General Public License for Mac OS X, Windows, and Linux.

  • It stands out for its ability to develop graphics and store, manipulate, and process data.

  • You can find several packages within R to help you perform tasks in areas such as statistics, data analysis, and machine learning.

  • You can learn R programming without any prior experience through online courses or independent study.

Read on to learn more about the R programming language, its use cases, and how to start using it. If you're ready to start building R programming skills with an industry leader, consider enrolling in the Microsoft R Programming for Everyone Professional Certificate.

What is R programming?

R is a free, open-source programming language initially written by Ross Ihaka and Robert Gentleman (also known as R&R) of the University of Auckland’s Statistics Department. However, the statistical programming language we know today as R is a collaborative effort comprised of many global contributions and managed by the R Core Team. 

Its primary use is for statistical computing and data science. Although R may not be as versatile as popular general-purpose programming languages such as Python, if you’re looking to pursue a career in a data science or statistics-centric field, R offers the best environment for these specific cases because it contains more statistical functions than all other software programs.

R programming language syntax

R syntax consists of three elements: keywords that have pre-assigned meanings to communicate with the compiler, variables that store information, and comments used to increase code readability.

R programming language features and environment

R is an implementation of the programming language S. Similarly, it allows users to implement new functionalities by defining new functions. It also has integration capabilities with C and C++. Other notable features of R include:

  • Wide range of libraries

  • Large and active community of users

  • Optimal data storage and handling

  • Suite of data analysis tools and operators

  • Packages to develop neural networks for artificial intelligence (AI)

  • Conditionals, loops, input and output facilities, and user-defined iterative functions

  • Platform-independent and capable of running on all operating systems (Windows, Mac, UNIX, Linux)

What is R programming used for?

The R language is most commonly used for data analysis and statistical computing. It’s also an effective tool for machine learning algorithms. R is especially relevant for data science professionals due to its data cleaning, importing, and visualization capabilities. You can use it to create graphics such as charts and statistical techniques like data reduction, classification, and clustering. 

Looking for an R programming download? Visit https://www.r-project.org/. If you get stuck, try the R programming FAQ.

R language examples

One of the most notable features of R is its versatility. The following are a few examples of industries that use it:

  • Social media companies: Social media platforms collect massive amounts of data daily. Analysts track, store, and use many of your online actions to target advertising. Companies often use R to parse, analyze, and visualize these insights. 

  • Health care: Epidemiologists use the R programming language to track and predict the spread of disease. It’s also employed in drug discovery to analyze the data collected during pre-clinical trials. 

  • Financial technology (FinTech): R is one of the most popular programming languages in the financial services industry. Firms use it for credit scoring, risk management, and statistical analyses to predict investments. 

R programming vs. Python: Which is better?

Both R and Python are open-source programming languages. Python’s simple syntax makes it easier to learn than R. R is more suited for data exploration and statistical learning, while Python is ideal for machine learning and data analysis in large-scale applications. Read more: Python or R for Data Analysis: Which Should You Learn?

Pros and cons of R programming

When considering R for your projects, weighing its benefits and drawbacks is essential. Below is an overview of each. 

AdvantagesDisadvantages
Leading language when it comes to comprehensive statistical analysis packagesMemory-intensive since objects are stored in physical memory
Community-developed code enhancements and bug fixesLacking security features, cannot be embedded in a web application
Cross-platform capabilitiesCan be slower than comparable languages like Python

How to learn R programming

 A couple of different ways to learn to program in R exist to make learning easier. You might determine which is right for you by considering your learning preferences, budget, and time constraints. While bootcamps and degrees offer the opportunity to learn the language, you may also benefit from enrolling in an online program if you prefer more hands-on guidance. Below, you can browse recommendations for online courses, certificate programs, Guided Projects, and community resources for independent learning. 

Read more: R Programming Jobs and How to Get Started

Online courses

The following online courses offer foundational training in data analytics and R programming with flexible schedules:

  • Google Data Analytics Professional Certificate: This beginner-level, flexible online program focuses on data cleaning, analysis, and visualization with R, SQL, and Tableau. In just six months, you can have a shareable certificate for your resume and a foundational knowledge of Rstudio, R programming, and R markdown. 

  • IBM Data Analytics with Excel and R Professional Certificate: You can complete this beginner-level program in three months. It’s designed to guide learners through the processes of programming, data analysis, preparation, and predictive modeling using R, RStudio, and Jupyter. By the end, you’ll earn an employer-recognized certificate from IBM. 

  • Data Analysis with R Specialization: This beginner-friendly online course is meant to help learners master data analysis with R. Covered concepts include statistical testing and inference, linear modeling, and the utilization of regression models to analyze relationships between variables. Duke University offers this flexible program, which you can likely complete in four months. 

Self-teaching resources

If you prefer to self-learn, you’ll find value in exploring one or more of the following resources:

  • The R Journal. This peer-reviewed scientific journal published by the R Foundation is open-access. You can read various topics of interest that are relevant to both R developers and users. 

  • R-Ladies. R-Ladies is an organization promoting gender diversity in the R community, from package developers, to conference speakers, to educators. 

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