Hire R developers remotely from our vetted global talent

Most developers and digitally active businesses work with general-purpose programming languages — coding scripts you can use to build various types of software. However, when you have a more specific use like statistical analysis in mind, tailored solutions like R are as perfect a fit as it gets. So, let’s talk about R, what it does, and why it may be time for you to hire an R developer.

R is an open-source programming language built for statisticians by statisticians — specifically the pair of Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. It’s an implementation of S, another programming language built for statistics, sharing the same syntax for representing code and data. The programming paradigm is both procedural and object-oriented. However, R’s heritage as an implementation of S means it has more robust and advanced object-oriented programming facilities than any other language.

In addition to being a programming language, R is a coding environment for statistical computing and graphics, complete with an integrated suite of software features for the manipulation and analysis of data as well as graphical display. It also contains a high-capacity data handling and storage facility, a set of array calculation operators mostly for matrices, and a vast array of intermediate analysis tools. And, of course, it contains the programming language itself, with its well-developed, developer-friendly, and effective form and features like I/O facilities, loops, conditionals, and user-defined recursive functions.

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.

R development is a very in-demand career path, especially as data analysis becomes a core feature of many industries. Across these industries, companies hire R developers to take on many roles, paying quite a lot to hire and keep them. The global average paycheck for an R developer is around $64,000; in the US, it's up to $120,000. Given how much it costs to hire R engineers, companies have to optimize workflows to get the best value for their money. Assigning the right roles is one of the most effective ways they do that.

What roles an R developer fills in a company depends on what that company does. So, it's essential to understand what the technology requires in your sector before you assign roles. Still, there are some core things an R developer is responsible for; let’s overview them.

• Developing R-based Programs and Software: The primary responsibility of an R developer is building programs and software for data analysis, statistical computation, and graphical rendering. R's “Shiny” development package allows users to build interactive web apps, so R developers are often tasked with making such web software as well. Using this package, they create both the user interface and the server-side digital wiring.
• Create R-based Statistical and Graphical Models: Creating statistical and graphical models using R is another thing an R developer does. The platform already provides a suitable statistical modeling and design environment, enabling developers to seamlessly select models, estimate parameters, and assess model fit. For example, it offers a suite of features for operating with binomial and Poisson distributions and multiple functions for testing hypotheses, such as chi-squared tests and ANOVA.
  
  Additionally, the R development environment contains powerful linear regression functions. Developers can seamlessly fit models, perform model diagnostics, and make predictions based on data. Finally, it has many tools for rendering graphs and plotting charts. An R developer’s job is to leverage these to build high-performance statistical and data operations models.
• Monitor and Support Platform and Applications: Statistical analysis software and data modeling programs must run smoothly and execute tasks without hitches. So, providing monitoring and technical support for these systems becomes necessary. Data-driven companies working with R often hire R developers to fill this role.
  
  In the monitoring role, your R developer will rely on any number of efficient monitoring and diagnostic tools to keep track of performance. For example, R Markdown provides a good solution for model monitoring, and it's also possible to integrate other excellent third-party tools as well. Technical support will involve troubleshooting and maintenance for models, web apps, and tech systems modeling and computing data.
• Bug Tracking and Debugging: R developers are responsible for tracking and removing bugs in R-based application code during development. After development, they also run periodic diagnostics and debugging.
• Manage Access Control for R-based Data Assets and Projects: Companies working with R language hire dedicated R developers to manage access to data assets. As access control managers, R programmers regulate who can view or interact with the data and models. They control who can modify model parameters and define access control policy, deciding between role-based, attribute-based, or discretionary access control.

Every year, new talent pours into the pool of R developers for hire, and CTOs from some of the biggest companies quickly find and scoop them up. To hire R developers of the best caliber (at least, for your budget) and avoid getting left scrubbing pool-bottom, you need to know and follow what the winning CTOs do. You need to go in with a clear idea of an R developer’s vital skills. These are the most essential ones.

• Extensive Knowledge of and Experience with R Programming Language: An R developer must know R programming language, environment, syntax, and paradigms. In addition, they must have lots of experience using R.
• Familiarity with RStudio: RStudio is an indispensable tool for analyzing, importing, accessing, plotting, and transforming data on R. Companies don't hire R developers who don't who are not familiar with it.
• Experience with Databases: R is a technology built for data analysis and computation. As such, R developers need to have significant experience using database systems, such as AWS and MongoDB.
• Knowledge of Statistical Analysis and Modeling: To function as an R developer, one needs to understand how to apply statistical analysis to datasets and create data models. It's a vital skill companies confirm before they hire dedicated R developers.
• Good Analytical and Problem-solving Skills: An R developer must be able to analyze data in very fine detail. And while the community is always there to help, you want to ensure you hire an R developer who can think independently and quickly develop innovative solutions.