Many of the computer programming languages you learn about are general-purpose. You can use them to build all types of applications. However, other programming languages are designed for a specific task and become well-known because they do that task more efficiently or make programming the solution easier. R is one of these specialized programming languages.

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. Let’s look at more details of the R language to see what makes it different.

What makes R unique?

R is unique in that it is not general-purpose. It does not compromise by trying to do a lot of things. It does a few things very well, mainly statistical analysis and data visualization. While you can find data analysis and machine learning libraries for languages like Python, R has many statistical functionalities built into its core. No third-party libraries are needed for much of the core data analysis you can do with the language.

Here’s an example of R syntax for a basic program that calculates the mean in a set of numbers:

# Create a numeric vector
numbers <- c(2, 4, 6, 8, 10)

# Calculate the mean
mean_value <- mean(numbers)

# Print the result
cat("The mean is:", mean_value, "\n")

But even with this specific use case, it is used in every industry you can think of because a modern business runs on data. Using past data, data scientists and data analysts can determine the health of a business and give business leaders actionable insights into the future of their company.

What is R used for?

Just because R is specifically used for statistical analysis and data visualization doesn’t mean its use is limited. It’s actually quite popular, ranking 12th in the TIOBE index of the most popular programming languages.

Academics, scientists, and researchers use R to analyze the results of experiments. In addition, businesses of all sizes and in every industry use it to extract insights from the increasing amount of daily data they generate.


Fintech companies are companies that deal with financial services. R is used at many of these types of companies because money and statistics go hand-in-hand. Banks use the R language to create credit risk models and conduct other types of risk analysis. It is also used for fraud detection, mortgage modeling, volatility modeling, client assessment, and loan stress test simulations.


The R programming language is widely used in academics and research. For instance, Cornell University teaches R in courses that require statistical computing. The University of California teaches students statistics and data analysis by introducing them to R, and many other universities do as well.


In retail and e-commerce, R is used for risk assessment and to create marketing strategies. For example, R’s machine learning capabilities are used to improve cross-selling and suggest better-related products at checkout to increase profits and sales. R is also used for sales modeling and targeted advertising in retail. Both Amazon and Flipkart use the R programming language for data analytics.

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The National Weather Service uses the R programming language to predict disasters and to forecast the weather. They also use the visualization features of R to create weather forecast images. In addition, the FDA uses R to evaluate drugs, perform pre-clinical trials, and predict possible reactions caused by the food products they review.

Data journalism

Data journalists use data to tell a story. They are journalists and data scientists who pull insights about our world and how we live from public data. This can be information from local government and police sources to tell a story about crime, financial data to show the state of a country’s economy or any other type of data that reveals an interesting pattern in how our world works. R is a popular language for data journalists because it gives them the ability to find these insights and generate stunning graphics that tell the story.

Social media

Social media has always been a data-heavy industry. We are tracked everywhere we go online. Every single action is stored in some database, waiting for an analyst to pull insights from it. Most social media sites’ only source of profit is the data they have on their users and targeted advertising. The R programming language is used for social media analytics, segmenting potential customers, and targeting ads.


R is heavily used in genetics, bioinformatics, drug discovery, and epidemiology. For example, in drug discovery, R is used to crunch the data gathered in pre-clinical trials and determine how safe a drug is. In epidemiology, it is used to predict how a disease will spread in a pandemic.


Many companies use the R programming language to analyze customer feedback to help them improve the products they create. The Ford Motor Company uses R to analyze consumer sentiment about its vehicles and improve their design. John Deere uses R to determine how many spare parts and products they need to produce based on crop yield and other data.

R packages

R is an open-source language that’s supported by a large community of developers. As a result, there are tons of packages that extend R’s base functionality. Some of the most popular R packages include:

  • tidyverse: a package that expands R’s utility in data science, allowing you to transform and visualize data and streamline your workflow.
  • ggplot2: this package enhances R’s data visualization capabilities.
  • TensorFlow: a package that extends R’s utility into machine learning.

R advantages and disadvantages

Along with its utility in data analytics and visualization, R holds several other advantages that can make it a great addition to your tech stack.

  • Open source: R is an open-source language, so anyone is free to use it or contribute to its development.
  • Third-party libraries and packages: The massive community behind R is constantly releasing new packages that improve and extend the language’s functionality.
  • Statistics: R was designed specifically for statistical computing and analysis, and it’s the most popular programming language used in the field.
  • Interpreted: As an interpreted language, you can run R code without using a compiler.
  • Platform independent: R is a cross-platform programming language, so it can run on most operating systems.

But before diving into R, you’ll also want to know about disadvantages like:

  • Complex syntax: R has a steep learning curve, and it’s not well-suited to new developers.
  • Memory usage: R provides few memory management features and stores data in physical memory, which can pose an issue when working with larger data sets.
  • Security: R doesn’t have many security features, leaving it vulnerable to exploitation.

Learn more about R programming

Ready to jump into the exciting world of data? Start with Learn R to learn how this powerful programming language works and become a data expert. You can then take your education further by learning how to analyze data with R or one of the other courses available in our R programming language course catalog.

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