What Is R Programming? Use Cases and FAQ

R programming is one of the skills employers in the data science industry most frequently request. 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.

If you have a statistical background, picking up the R programming language may be easier. Read on to learn more about the R programming language, its use, and how to start using it. 

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Data Analysis with R Programming

This course is the seventh course in the Google Data Analytics Certificate. In this course, you’ll learn about the programming language known as R. You’ll ...

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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 comprised of many global contributions and managed by the R Core Team. 

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. 

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.

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. 

Is R language easy to learn?

 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. You may 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. 

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. 

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Google Data Analytics

Get on the fast track to a career in Data Analytics. In this certificate program, you’ll learn in-demand skills, and get AI training from Google experts. Learn at your own pace, no degree or experience required.

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Spreadsheet Software, Data Management, Data Analysis, Business Communication, General Statistics, Business Analysis, Data Visualization, SQL, Data Cleansing, Developing a portfolio, Creating case studies, Metadata, Data Ethics, Spreadsheet, Data Collection, Data Calculations, Data Aggregation, Rstudio, R Markdown, R Programming, Presentation, Tableau Software, Sample Size Determination, Data Integrity, Decision-Making, Questioning, Problem Solving

• 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. 

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IBM Data Analytics with Excel and R

Prepare for a career in data analytics. Gain the in-demand skills and hands-on experience to get job-ready in less than 3 months. No prior experience required.

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Data Visualization (DataViz), Data Analysis, Data Science, IBM Cognos Analytics, Spreadsheet, Pivot Table, Data Visualization, Relational Databases (RDBMS), Dashboard, Microsoft Excel, R Programming, Computer Programming, Regression, Programming Principles, Data Structures, Computer Programming Tools, Statistical Programming, Data Analysis Software, Probability & Statistics, General Statistics, Statistical Analysis, Pivot Charts, cognos analytics, Data Structure, Databases, Data Management, Exploratory Data Analysis, Linear Regression, Select (Sql), Spreadsheet Software, Python Programming, SQL

• 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. 

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Statistics with Python

Practical and Modern Statistical Thinking For All. Use Python for statistical visualization, inference, and modeling

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Critical Thinking, Data Analysis, Computer Programming, Mathematics, Statistical inference methods, Regression, Probability Distribution, Probability & Statistics, Bayesian Statistics, General Statistics, Statistical Model, Data Visualization, Python Programming, Statistics, statistical regression, Statistical Inference, Confidence Interval, Statistical Hypothesis Testing

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. 

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

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

Learn how to code in R on Coursera.

R is a specialized language and environment for statistical analysis and data visualization. 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. Additionally, you will be able to gain insights into performing basic formatting with R Markdown and generate visualizations in R.

course

Data Analysis with R Programming

This course is the seventh course in the Google Data Analytics Certificate. In this course, you’ll learn about the programming language known as R. You’ll ...

4.8

(11,163 ratings)

669,476 already enrolled

Beginner level

Learn More

Average time: 31 hour(s)

Learn at your own pace

Skills you'll build:

Data Analysis