What Is R Programming? Use Cases and FAQ (2024)

Written by Jessica Schulze • Updated on

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 and how to learn it in the following article.

What Is R Programming? Use Cases and FAQ (1)

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.

What Is R Programming? Use Cases and FAQ (2)

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

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 in security features, cannot be embedded in a web application.
Cross-platform capabilitiesCan be slower than comparable languages like Python

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.

  • 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.

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Jessica Schulze

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Jessica is a technical writer who specializes in computer science and information technology. Equipp...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

What Is R Programming? Use Cases and FAQ (2024)

FAQs

What is the use case of R programming? ›

Here is a list of industries/disciplines that use the R programming language:
  • Fintech Companies (financial services)
  • Academic Research.
  • Government (FDA, National Weather Service)
  • Retail.
  • Social Media.
  • Data Journalism.
  • Manufacturing.
  • Healthcare.
Jun 7, 2023

What is R programming and why it is used? ›

R is a programming language for statistics that can be used for statistical computing and to show data. It's numerous abilities can be put into three main groups: Manipulation of data. Analysis of the numbers.

What are two benefits of using R? ›

6 Reasons R Rocks for Scientific Research
  • Free and open-source. Everyone loves a bargain, and many value open sharing of technology. ...
  • Reproducible research. ...
  • Extremely easy data wrangling. ...
  • Advanced visualizations. ...
  • Quick implementation of new theoretical approaches. ...
  • Easily extends to serve your specific needs.

How do you explain R code? ›

R is an open source programming language and software environment for statistical computing and graphics. It is one of the primary languages used by data scientists and statisticians alike. It is supported by the R Foundation for Statistical Computing and a large community of open source developers.

What are use cases in programming? ›

A use case is a concept used in software development, product design, and other fields to describe how a system can be used to achieve specific goals or tasks. It outlines the interactions between users or actors and the system to achieve a specific outcome.

What is the basic use of R? ›

People with basic R knowledge can use the programming language to perform basic data sorting and analysis tasks, such as assigning variables and using the command line to perform calculations. They can also produce visual representations of data, such as pie charts or graphs.

Who benefits from R programming? ›

It is a trendy language among developers due to the various advantages of R programming. Primarily used in data analysis, statistical operations, and machine learning but its use in the industry is not limited to this. Its packages offer many services which are used by many businesses and industries.

What are the 3 applications of R programming in the real world? ›

R is utilized in research and academics as a statistical research tool. It is used for data evaluation, statistical modeling, and representing data. Researchers analyze and build models with the help of functions, including lme4. R is likewise used for machine learning research and deep learning.

Is R hard to learn? ›

R is considered one of the more difficult programming languages to learn due to how different its syntax is from other languages like Python and its extensive set of commands. It takes most learners without prior coding experience roughly four to six weeks to learn R. Of course, this depends on several factors.

What is R most commonly used for? ›

R is widely used in data science by statisticians and data miners for data analysis and the development of statistical software. R is one of the most comprehensive statistical programming languages available, capable of handling everything from data manipulation and visualization to statistical analysis.

What are the pros and cons of R? ›

Pros and cons of R programming
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 in security features, cannot be embedded in a web application.
1 more row
Feb 12, 2024

Is learning R programming useful? ›

R is used across many industries and professions including financial services, academia, healthcare, and data journalism, benefiting those who can analyze and visualize data. Roles benefiting from R programming include Data Analysts, Data Scientists, Data Architects, Geo Statisticians and Quantitative Analysts.

What is R and why is it used? ›

R is a programming language created by statisticians for statistics, specifically for working with data. It is a language for statistical computing and data visualizations used widely by business analysts, data analysts, data scientists, and scientists.

Is R or Python better? ›

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.

What are cases in R code? ›

cases() Function. complete. cases() function in R Programming Language is used to return a logical vector with cases that are complete, i.e., no missing value. This function is especially handy when dealing with datasets that may have missing data.

What is the use of R function? ›

A key feature of R is functions. Functions are “self contained” modules of code that accomplish a specific task. Functions usually take in some sort of data structure (value, vector, dataframe etc.), process it, and return a result.

What are the use cases for R vs Python? ›

Python vs R: a comparison table
RPython
AdvantagesOutstanding for statistical graphs and reports, with a comprehensive package repository ideal for specific analysesOffers greater readability, speed, and functionality, and is versatile in mathematical computation and deployment
10 more rows

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