What is the R programming language used for?

The R programming language was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, in 1993. Today it is developed by the R Core Team.

R is an open-source programming language. It is widely used as statistical software and a data analysis tool, with an extensive catalog of statistical and graphical methods.

It also gives you tools for machine learning, linear regression, time series analysis and statistical inference. Is R even popular? According to IEEE Spectrum’s survey, R ranked seventh among the top programming languages in 2018.

You can write R programs in an IDE like RStudio, Rattle or Tinn-R. Many R users prefer a simple text editor for small programs and scripts. Once you have written a program, save it to a file with the .R extension.

To run the program, go to the command prompt and execute:

Rscript your-file.R

This runs your file.

R has an extensive library written mostly in R, but surprisingly, many components are written in C, C++ and even Fortran. The language is widely used in academia, and companies like Facebook, Google and Uber have built R-based solutions into their systems.

Interestingly, although R is best known for statistics, many of its statistical features come from packages you can add when writing R routines. As mentioned above, R’s biggest users are in academia, followed by the healthcare sector.

Many government departments use R too. Besides being a language, it is also a software environment that lets you perform many statistical tasks from within its interface.

In fact, its creators describe R as an “integrated suite of software facilities for data manipulation, calculation and graphical display.” It runs from a command prompt and also works on Windows.

When R is running, it looks much like a UNIX shell prompt, and you can run most commands and statistical operations directly from it.

R is derived from the S programming language. In fact, it is considered an implementation of S, which was developed at Bell Labs.

How R differs from other programming languages

As mentioned above, R was designed for data analysis. It gives you lots of features for graphics, statistics, creating and manipulating data structures, object manipulation and numerical computation.

It is an interpreted, vector-oriented language (as opposed to object-oriented, like C++). Although it is called a programming language, R is more of a statistical programming environment than a general-purpose language such as Python or Java.

Some prefer to call it a statistical tool rather than a full programming language. Can you write a complete application in R the way you can in PHP or C++? Not really.

Can you imagine WordPress written in R? No. That’s because R isn’t a general-purpose programming language. You’ll often see R compared with Python, because the two are frequently combined for complex statistical and mathematical work.

R has a steep learning curve. You can start learning Python on your own fairly easily, but to get the most out of R you need to know the right functions, the right libraries and the right environment for the result you want.

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A better comparison is SQL (Structured Query Language). You can issue commands in SQL, but they all revolve around databases; you can’t write standalone applications or mobile apps in SQL alone.

The same goes for R. For a comprehensive, low-level program you would use another language like C, Python or PHP, but if you need a few lines of code without many loops, parameters and arrays, R is a good fit.

The main features of the R programming language

Every programming language survives because it offers something others don’t. R has passionate users.

They vouch for it and claim it does things no other language can. Let’s look at the features that make thousands of people around the world use R for complex statistical and mathematical problems.

R is open source

R is an open-source language and software environment. The biggest benefit of open source is that you can write your own features and packages and add them to the overall software.

As a result, thousands of libraries and packages are available for R. You can download, install and start using it immediately.

Strong data visualization

You can generate all sorts of graphs: pie charts, bar charts, density plots, scatterplot matrices, trellis graphs, kernel density plots and many more. R has so many visualization capabilities that you can create almost any kind of chart.

Thousands of additional packages

R has a huge range of libraries and packages. The best-known repository is CRAN, which hosts tens of thousands of packages covering nearly every data field and type of visualization.

From specialized statistical techniques to graphics devices, import/export tools and reporting, you can find almost everything in these packages. Most are written in R, but many, as mentioned above, are written in Java, C, C++ and Fortran.

When you install R, you get a core set of packages, and CRAN (the Comprehensive R Archive Network) offers thousands more. R also contributes to genomics research through the Bioconductor project, which has added some object-oriented capabilities to the language.

Cross-platform

R is interpreted. Its commands run from a shell prompt, and R runs on Windows, Mac and Linux.

This makes R cross-platform. The basic commands are the same on UNIX, Windows and Mac. It’s like Java: once the runtime is installed, your code runs anywhere.

A strong and vibrant online community

Since the rise of the Internet, every language has grown on the shoulders of its community, and R is no exception. Because it is open source, thousands of programmers and users have contributed packages and libraries, and they are still adding more.

There are tens of thousands of packages you can download and use, all developed by the community. There is also plenty of help online through forums and social groups.

Distributed computing

R has packages like ddR and multidplyr that let it use distributed computing to process large datasets. In distributed computing, tasks are spread across multiple nodes (even computers around the world) to reduce processing time and increase efficiency.

No compiler needed

R doesn’t need a compiler. It is an interpreted language, and the R software does all the interpretation.

Works with databases

Since R works with datasets, it naturally works with mainstream databases. R doesn’t need a relational database for analysis, but large amounts of data sometimes need to be stored before they can be processed, and that data lives in a database.

R works with MySQL, ODBC (Open Database Connectivity), Oracle through ROracle, and RMySQL.

Data wrangling

Data wrangling is the process of cleaning and restructuring raw data into the format you need, so it can be processed better and faster. R can turn raw data into a well-structured dataset.

That can be hard in other languages, but R has built-in tools for finding patterns in messy data and giving it structure.

Machine learning

R is a powerful platform for machine learning and is used by top data scientists around the world.

It is state of the art because it is used so heavily in academia, where researchers constantly add new machine learning algorithms as R packages. And what is machine learning, after all?

It is advanced data analysis, which is exactly what R was built for.

Integration with other languages

For heavy tasks, R integrates easily with languages such as Python, C, C++, Fortran, Java and .NET.

As mentioned above, for complex databases you can also integrate it with SQL. This works because, while many packages are written in R, many others are written in Fortran, C++ and C.

Report generation

You can run saved scripts to produce complex reports in R, or generate them on the fly. To create reports as documents, use the “officer” package.

It can generate Word and PowerPoint documents.

Some more features:

• R performs operations directly on vectors, so you often don’t need loops.
• R can pull data from APIs, servers, SPSS files and many other formats.
• Interested in web scraping? R makes it easy.
• It can perform complex mathematical operations with a single command.
• R Markdown lets you build attractive reports combining text and visualizations, and export them to Word and PowerPoint.

Which industries use R the most?

R is used in major industries, especially in data science. Its extensive libraries let you run highly complex statistical functions.

With big data analytics now common, there is strong demand for analysts and programmers proficient in R. The following industries use R, with academia the biggest user, followed by healthcare.

• Academia
• Healthcare
• Government
• Consulting
• Insurance
• Energy
• Finance
• Retail
• Media
• Manufacturing
• Technology
• Electronics

Technology companies like Google, Microsoft, Accenture and Wipro use R for data analysis and even machine learning. Many database and SaaS companies also use R.

Career opportunities in R programming

R is used mostly by data scientists, analysts and programmers who work with data for statistics and mathematical computation. Around 2 million people use R for various projects.

If you want to work at companies like Google or Twitter, as mentioned above, they use R extensively. How?

Twitter has used R for statistical analysis. Google has used it to measure the returns on its advertising campaigns. Even US government agencies use it for forecasting, including flood forecasting.

Ford has used R to analyze feedback on its products. IBM uses it for machine learning. Accenture uses it for HR analytics.

According to the 2019 Stack Overflow developer survey, R developers earned slightly more on average than Python developers. The US and UK are currently the biggest markets.

Strong R skills can push your salary higher in the US, and data scientists who know R are among the best paid. Most R developers also have skills such as data analysis, Microsoft Excel, SQL, Java and statistical analysis.

So with R skills you can work as a software engineer, software developer, IT business analyst, IT team lead, IT consultant or senior business analyst.

Wherever data analysis and statistical computation are needed, R is invaluable. If you want a career as a data scientist, deep knowledge of data wrangling, analysis and visualization is essential.

You also need a solid foundation in a general-purpose language, plus SQL and a statistical language like R. Since big data analysis is a fast-growing field, students and professionals who know R have an edge.

Want to become a data analyst? R gives you a strong mathematical foundation, and if you want to be a business analyst, R knowledge is invaluable.

With it, you can build technical solutions for business analysis for your own company or for clients. Forecasting, market research, consumer behavior: R can analyze all of it.

What are the benefits of using R?

Beyond its features, one of R’s biggest benefits is that it is open source. As with most popular open-source languages, thousands of programmers keep adding new code to its packages.

And because academics use it so much, they keep adding new features. You can start using R right away.

Being open source also means that once you are proficient and can build your own packages, you can add features that make the language richer for yourself and for other developers, analysts and users.

Need more power? You can use lower-level languages to fill in any gaps. R is purpose-built for mathematical and statistical analysis.

It can analyze large amounts of data quickly. It can be slow at some tasks, but that’s a trade-off of how it was designed: to be flexible and user-friendly.

Because it is interpreted, it is platform-independent. As long as the interpreter is running, your commands work on any operating system. Career-wise, the benefits of knowing R are many.

You can become a data scientist, an analyst or a forecaster. Even as a systems analyst, R can be a great asset.

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