What Is R Programming? Use Cases and FAQ

R programming is one of the most requested skills by employers in the data science industry. If you’re considering learning the R language, you’ll be happy to know that it’s available to the public for free under the Free Software Foundation’s GNU General Public License.

Some consider R to be more challenging to learn than other programming languages, but those with a statistical background may find it to be easier. You can learn more about the R programming language, what it’s used for, and how to learn it in the following article. 

R programming language overview

R is a free, open-source programming language, meaning anyone can use, modify, and distribute it. It was 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 made up of many global contributions and managed by the R Core Team. 

R syntax

R syntax is made up 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?

Most commonly, the R language is 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 use statistical techniques like data reduction, classification, and clustering. 

R programming examples

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

• Social media companies. Social media platforms collect massive amounts of data every day. Many of the actions you take online are tracked, stored, and used by analysts to target advertising. R is often used by companies to parse, analyze, and visualize these insights. 

• Health care. The R programming language is used in epidemiology 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. It’s used for credit scoring, risk management, and statistical analyses for investment predictions. 

Pros and cons of R programming

What is the best way to learn R programming?

There are a couple different ways to learn to program in R. You might determine which one is right for you by considering your learning preferences, budget, and time constraints. Those who prefer more hands-on guidance may benefit from enrolling in an online program, whereas learners who enjoy independent study may not require as much instruction. Below, you can browse recommendations for online courses, certificate programs, Guided Projects, and community resources for independent learning. 

Online courses

• 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 This beginner-level program can be completed in just 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 utilization of regression models to analyze relationships between variables. It’s flexible, offered by Duke University, and can be completed in four months. 

Self-teaching resources

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

• Rstudio books. You can browse a list of free online books about R here. 

• satRdays. These not-for-profit global conferences are held on Saturdays and orchestrated by local communities. They are free or cheap, and focus on R and its applications. 

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

Keep learning about R programming with Coursera 

You can start programming in R today by enrolling in a beginner-focused online course like Data Analysis with R Programming. Led by Google, you’ll explore fundamental concepts like functions, variables, and data types. Upon completion, you’ll be able to perform basic formatting with R Markdown and generate visualizations in R.