R enjoys off-the-charts popularity levels among users in academia, mainly in universities. Along with academic institutions, fintech and big data companies are the enterprises that most frequently hire R developers. This is hardly surprising, given how good it is for data management and processing. However, despite being what you might call a niche programming language, it also enjoys widespread popularity in other sectors and industries. It’s shown remarkable growth
in popularity, sometimes even on par with programming languages like Python in terms of year-over-year percentage.
According to the TIOBE index rankings for 2023
, R has dropped quite a bit in popularity. However, it’s still the 19th most popular programming language, above Rust and just below Swift and Ruby. R’s competitiveness among these three is remarkable since they are all general-purpose languages and, thus, have a significantly broader scope of use cases. So, why is R such an in-demand programming language in these sectors?
- Excellent for Machine Learning: Many AI developers and machine learning experts think R offers the best prototype for working with machine learning models. Not only does it offer great explanatory code and visualization capabilities, but it also offers many excellent tools and packages for machine learning. Tools like DataExplorer, Esquisse, janitor, and Dalex enable R developers to pre-model, model, and post-model their machine-learning tasks excellently.
R is also suitable for performing sentiment analysis and predicting moods across demographics. These operations are elementary to perform within the environment, requiring no more than a handful of lines of code, mostly “dplyr” functions. These excellent machine-learning capabilities are why companies like Facebook and Google use R for many of their own projects.
- Open-Source Environment: R is an open-source programming language and environment; while the R foundation holds the copyright, it is freely available under the GNU general public license. Anyone can use it, modify its source code, or integrate features into the environment without getting a license or paying a subscription. This open-sourcedness has made it a very robust language, as many developers continue contributing to the environment, improving it over time.
- Compilerless Programming: Unlike compiled languages like C, C++, and Python, R is an interpreted language that allows developers to access its functions via its command-line interpreter. Essentially, when you input an expression into the R console, the R interpreter reads and executes the actual code you have entered, requiring no compiler to act in between and convert said code into an object language.
The process involves parsing each expression in your program to turn syntactic sugar into algebraic form, swapping objects for symbols where necessary, and putting out an object.
- Great for Statistical Computation and Data Analysis: We previously mentioned that the organizations that most frequently hire R developers and use the technology are academic, fintech, and big data companies. These organizations and their sectors favor R because its features for computation and analysis surpass those of most popular programming languages and environments. This is hardly surprising since it is the work of statisticians who wanted just such a level of capability.
For instance, consider R’s data visualization, which surpasses that of a programming language like Python, which many like for its excellent data visualization libraries. Unlike Python, R’s design enables it to display statistical analysis results comprehensively, and its basic graphics module greatly simplifies chart and plot building. Moreover, its “ggplot 2” graphics package easily breaks graphs into semantic components like scale and layers. It even allows you to create more sophisticated plots, such as complex violin or scatter plots with regression lines.
- Compatible with Other Programming Languages: Companies hire R developers to take advantage of its seamless pluggability with so many other programming languages, including general-purpose ones. This is quite unique since very few other programming languages, especially general-purpose ones, can integrate with other languages like this. You can integrate R with other programming languages like C, C++, and Fortran and directly manipulate objects with others like Python, Java, and .NET.
- Many Use Cases: R may initially seem like it’s entirely for tenured professors and nerds in the math department, but it is more widely relevant than that. Just about any company that operates on statistics or does any data analysis can benefit magnificently from using this technology. This is why companies like Wipro, Accenture, and Google use it often and have some of the biggest budgets to hire R engineers.
So, suppose your company is into manufacturing, social media, data journalism, or even healthcare. In that case, you need R in your technology stack and one or two R developers on your IT team. R contains all the right tools for working on the boatloads of data these sectors confront you with. For example, R Markdown can help you generate reports of any type — a handy tool for healthcare professionals, manufacturers, and especially journalists.
- Sizable and Active Community: R’s growth in popularity has mirrored the explosion of the data science field. With so many organizations worldwide doing so much with data, an increasing number of data scientists are embracing R. All that incoming experience, talent, and insight feeds into the growing global community both for R developers for hire and attached or independent developers. It’s hardly surprising that many of the latest ideas for improving the technology have emerged from its developer community.
If your R developer hits a snag in the programming environment, they can easily find answers by picking the brains of other colleagues from any number of forums. These forums are inclusive, containing developers with all experience levels and from all life backgrounds. R communities are also generally warm, cordial, easy to navigate, and effortless to get needed guidance.
- Cross-Platform Support: The fact that R can work with many different platforms is another great reason to hire a dedicated R developer to build your data and statistics computation stack. It can make work on development cycles much more manageable, enabling you to work with virtually any operating system, including Windows, Linux, Macintosh, and UNIX. Your developers only need to build a program that can work across multiple OSes, thus saving the business time, staffing, and resources it would have spent building for each OS.