Since there are so many programming languages available today, it’s sometimes hard to decide which one to choose. As a result, programmers often face the dilemma of too many good choices. It’s enough to stop people in their tracks, paralyzed with indecision!

To combat this potential source of mental gridlock, we present an analysis of the R programming language. This article covers what the R programming language is all about, what it’s suitable for, its basics and advantages, and anything else we can throw in to help you make an informed decision.

Let’s kick off our journey of discovery by answering the question: “What is R?”.

What Is R?

What better place to find a good definition of the language than the R Foundation’s website? According to, R is “… a language and environment for statistical computing and graphics.” It’s an open-source programming language often used as a data analysis and statistical software tool. 

The R environment consists of an integrated suite of software facilities designed for data manipulation, calculation, and graphical display. The environment features:

  • A high-performance data storage and handling facility
  • A suite of operators for array calculations, mainly matrices
  • A vast, easily understandable, integrated assortment of intermediate tools dedicated to data analysis
  • Graphical facilities for data analysis and display that work either for on-screen or hardcopy
  • The well-developed, simple and effective programming language, featuring user-defined recursive functions, loops, conditionals, and input and output facilities. 

The syntax of R consists of three items:

  • Variables, which store data
  • Comments, which are used to improve code readability
  • Keywords, reserved words that have a special meaning for the compiler

R was developed in 1993 by Ross Ihaka and Robert Gentleman and includes linear regression, machine learning algorithms, statistical inference, time series, and more.

R is a universal programming language compatible with the Windows, Macintosh, UNIX, and Linux platforms. It is often referred to as a different implementation of the S language and environment and is considered highly extensible.

What Is R and What Are the Advantages?

The R programming language has a lot going for it. Here is a list of some of its major strong points: 

  • It’s open-source. No fees or licenses are needed, so it’s a low-risk venture if you’re developing a new program.
  • It’s platform-independent. R runs on all operating systems, so developers only need to create one program that can work on competing systems. This independence is yet another reason why R is cost-effective!
  • It has lots of packages. For example, the R language has more than 10,000 packages stored in the CRAN repository, and the number is continuously increasing.
  • It’s great for statistics. Statistics are a big thing today, and R shines in this regard. As a result, programmers prefer it over other languages for statistical tool development.
  • It’s well suited for Machine Learning. R is ideal for machine learning operations such as regression and classification. It even offers many features and packages for artificial neural network development.
  • R lets you perform data wrangling. R offers a host of packages that help data analysts turn unstructured, messy data into a structured format.
  • R is still growing. R keeps evolving and growing, constantly updating and upgrading, thanks to a solid supportive community.

Does R Have Any Drawbacks?

What language doesn’t? When answering the question “What is R?” we should also look at some of R’s not so great aspects:

  • It’s a complicated language. R has a steep learning curve. It’s a language best suited for people who have previous programming experience.
  • It’s not as secure. R doesn’t have basic security measures. Consequently, it’s not a good choice for making web-safe applications. Also, R can’t be embedded in web browsers.
  • It’s slow. R is slower than other programming languages like Python or MATLAB.
  • It takes up a lot of memory. Memory management isn’t one of R’s strong points. R’s data must be stored in physical memory. However, the increasing use of cloud-based memory may eventually make this drawback moot.
  • It doesn’t have consistent documentation/package quality. Docs and packages can be patchy and inconsistent, or incomplete. That’s the price you pay for a language that doesn’t have official, dedicated support and instead is maintained and added to by the community. 

What is R Used For?

Although R is a popular language used by many programmers, it is especially effective when used for

R offers a wide variety of statistics-related libraries and provides a favorable environment for statistical computing and design. In addition, the R programming language gets used by many quantitative analysts as a programming tool since it's useful for data importing and cleaning.

As of August 2021, R is one of the top five programming languages of the year, so it’s a favorite among data analysts and research programmers. It’s also used as a fundamental tool for finance, which relies heavily on statistical data. 

The Popularity of R by Industry

Thanks to its versatility, many different industries use the R programming language. 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

This graph, provided by Stackoverflow, gives you a better idea of R programming language usage in recent history. Given its strength in statistics, it's hardly surprising that R enjoys heavy use in the world of academia, as illustrated on the chart.

If you’re looking for specifics, here are ten significant companies or organizations that use R, presented in no particular order.

  • Airbnb
  • Microsoft
  • Uber
  • Facebook
  • Ford
  • Google
  • Twitter
  • IBM
  • American Express
  • HP

R packages are defined as collections of R functions, sampled data, documentation, and compiled code. These elements are stored in a directory called “library” within the R environment and are installed by default during installation. 

R packages boost R’s power by improving the existing functionalities, collecting sets of R functions into one unit. In addition, the R package is a reusable resource, which makes a programmer's life much easier.

Here’s a chart illustrating the most popular R packages based on questions asked, provided once again courtesy of Stackoverflow.

Despite the massive popularity of Internet articles (ahem!), the printed word isn’t dead. Consequently, there are many excellent books you can find that cover the R programming language exceptionally well. Here is a list of excellent "printed word" resources to help round out your R language skills and understanding of what is R.

  • A First Course in Statistical Programming with R. Braun, W. and Murdoch, D. (2007). Cambridge, MA: Cambridge University Press.
  • R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Wickham, H. (Author), Grolemund, G. (2017). O’Reilly Media.
  • Programming with Data: A Guide to the S Language. Chambers, J. M. (1998). Murray Hill, NJ: Bell Laboratories.
  • Introductory Statistics with R (2nd edition). Dalgaard, P. (2008). New York: Springer.
  • A Handbook of Statistical Analyses Using R. Everitt, B., and Hothorn, T. (2006). Boca Raton, FL: Chapman & Hall/CRC.
  • Learning R: A Step-by-Step Function Guide to Data Analysis. Cotton, R. (2013). O’Reilly Media.
  • R for Everyone: Advanced Analytics and Graphics. Lander, J. (2017). Addison-Wesley Professional; 2nd edition.
  • Linear Models with R. Faraway, J. J. (2005). Boca Raton, FL: Chapman & Hall/CRC.
  • Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Faraway, J. J. (2006). Boca Raton, FL: Chapman & Hall/CRC.
  • An R and S-Plus Companion to Applied Regression. Fox, J. (2002). Thousand Oaks, CA: Sage Publications.
  • R for SAS and SPSS Users. Springer Series in Statistics and Computing. Muenchen, R. A. (2009). New York: Springer.
  • R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics. Long, J.D. and Teetor, P. (2019). O’Reilly Media; 2nd edition.
  • R Graphics. Murrell, P. (2005). Boca Raton, FL: Chapman & Hall/CRC.
  • Mixed Effects Models in S and S-Plus. Pinheiro, J. C. and Bates, D. M. (2004). New York: Springer.
  • Data Manipulation with R. Spector, P. (2000). New York: Springer.
  • Modern Applied Statistics with S. Venables, W. N., and Ripley, B. D. (2002). Fourth Edition. New York: Springer.

What Is R and Which Language Is Better: Python or R?

According to StatisticsTimes, C is the top programming language as of August 2021 (R is in fourth place on the list). But both Python and R are popular and have their share of adherents. But which one is the best?

As you might expect, the answer isn’t so cut and dried. There are factors to consider. So, as you ponder your options and puzzle out the language that is best (for you!), ask yourself these questions:

  • How much programming experience do I have? Python has an easy learning curve and is ideal for beginners. R’s advanced, complex functionalities make it better suited for experienced programmers.
  • What do I want the language to accomplish? Python is best for machine learning and large-scale operations such as data analysis in web applications. R shines in statistical learning.
  • Do charts and graphs matter to me? R applications are great for using eye-catching graphics to render your data, while Python applications are best suited for integration in an engineering environment.
  • What are the people around me using? Python is a production-oriented language best suited for many different engineering, research, and industrial workflows. R is best as a statistical tool, ideal for academics, scientists, and engineers.

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