R was created by Ross Ihaka and Robert Gentleman (who aptly titled their project with the R in their names) and entered the programming language scene in 1993. They created it as an open-source programming language that's an alternative to S. R's environment was created to be a statistic computing and graphics environment, and because of that, it has been widely accepted and used by those working in statistics and computer science.
But what exactly are R’s features, advantages, and disadvantages? What is an R developer, and what do they do? How much does an R developer make, and what are their main responsibilities and qualifications? And finally, how do you find and hire the best R programmers? Read on to learn more.
What Is R Programming?
While R was originally created for research purposes, it has since been adopted by private businesses to analyze data and develop statistical software. Even well-established companies such as Google, Uber, and Facebook have benefited from the software made by R.
R’s main goal today is data visualization and manipulation. Because of its background in statistics and graphics, it can generate linear and nonlinear models, time-series analyses, clustering, classical statistical testing, and classification. It is one of the primary languages for data science.
R is supported by the R Foundation for Statistical Computing and the R Core Team.
R has many advanced features that programmers are obsessed with, making it one of the most significant programming languages out there. Here are some examples:
The Ability to Handle Data
R is integrated and works with all data storage formats, improving its ability to handle and manage data effectively. Additionally, R provides excellent storage facilities, data modeling, and data operation facilities, and offers SQL and Big Data extensions. Altogether, handling data with R is easy and efficient.
An Active Community
R has been around for a while, so it's had plenty of time to amass a loyal following. Thanks to its growing number of users who contribute to the language daily, R is constantly evolving. Not only does R possess an active community, but its community loves to engage in seminars, boot camps, and other training sessions. Its large user base and the fact that R is open-source keep its community remaining active.
Although R is primarily written in its own language, it's compatible with many other programming languages, including C, C++, and FORTRAN. R even supports Java, Python, .NET, and other languages that can be used to manipulate objects. R is also compatible with various data processing technologies and distributed computing technologies such as Spark and Hadoop. Lastly, R functions across various platforms and can be used on all operating systems, software environments, and hardware configurations without additional workarounds.
No Compiler Necessary
R is an interpreted language, so it doesn't require a compiler to turn code into executable programs. R’s code is interpreted one step at a time and then translated into machine-level calls.
Data Variety and Wrangling
R is efficient at running and handling several data types, both structured and unstructured. It can also provide various data modeling and data operation facilities thanks to its database interactions.
R has many packages that help it to interact and interface with databases, such as Open Database Connectivity Protocol, RmySQL, and others.
R uses a distributed computing method that enables it to split tasks between various processing nodes. This helps decrease the processing time and boost efficiency, especially with packages like multiDplyr and ddR that allows its distributed computing to process bigger data sets.
R allows its programmers to conduct various complication operations, typically on vectors, data frames, arrays, and other data objects from small to big. These operations are conducted at fast speeds and provide several operator suites that focus on various calculations.
Whether you need to perform simple or complicated statistical and mathematical calculations, R can perform both at lightning speed.
Applications and Use in Data Science
On top of being an object-oriented language, R is also what's called a comprehensive programming language, enabling its use for mathematical and statistical modeling and software development. R puts a huge emphasis on data science. It's also used to develop web applications, typically through its strong RShiny package.
R’s compatibility ensures that it can be integrated with several technologies, software packages, frameworks, and programming languages. R’s distributed computing can be used when paired with Hadoop.
While R is typically used for data science, it can also be used for machine learning. When using R for this purpose, it’s best used to explore or build one-off models.
Although earlier versions of R had various packages to help with machine learning algorithms, recent versions come with an MLR package, or Machine Learning in R package, which has risen in popularity since it consists of ways to work with all machine learning algorithms.
MLR is useful no matter which machine learning algorithm you’re invested in, and it also provides other tools to assist in machine learning.
As mentioned previously, R is an open-source language, meaning it doesn’t require a license to use and is generally free. Many developers contribute customized packages and additional features to R, wanting to help it grow and succeed further.
Because R is open-source, it can be adjusted and adapted to a specific user’s requirements. Plus, since R is freely available to the public, anyone can download it, install it, and start using it right away.
Several Graphical Libraries
R puts a huge focus on graphical libraries. As such, R users can produce static graphics with high-quality visualizations. To help with this, R provides many extended libraries with dynamic graphic attributes.
Thanks to R’s ability to produce these graphics, data visualization and representation become easily accomplishable tasks in an R environment.
R can create concise charts, interactive and dynamic flow diagrams, and more, all created within R’s repertoire. Additionally, due to its huge collection of graphical libraries, R can stay ahead of most other data science programming languages.
Various Industry Facilities
Most industries that use data end up using R to facilitate their data adventures. In the past, R was mostly used by academics, but it is now being used by other industries as well. Basically, any industry that requires data insights can benefit from R.
For example, manufacturing industries have used R to optimize their procedures. Social media platforms and companies like Airbnb use R to analyze their users. Even the health industry uses R, primarily to design drugs and analyze genomic strands.
A Versatile Array of Packages
R has become widely popular amongst developers due to the grand variety of packages and libraries it has to offer. R even has a repository called CRAN, or Comprehensive R Archive Network, that can hold more than 10,000 packages. These packages contribute to every functionality imaginable and provide various fields that deal with data.
Additionally, these packages and libraries offer several features to R users. From astronomy to biology, and more, R has something for everyone.
R’s most basic data structure is that of vectors. Due to this strong focus on vectors, R can use vectors and vector arithmetic without needing looping to process large value sets, altogether making R more efficient.
Other Unique Features
In addition to its vectors and other features listed above, R has a few other features that are worth mentioning, including:
- The ability to pull data from servers, SPSS files, APIs, and other formats
- Web scraping actions and data extraction
- Single commands that can conduct several complicated mathematical equations
- R Markdown function, which enables attractive reports to be created, combining code, visualization, and plain text from the results
It's also worth mentioning that new ideas and technologies appear in R’s community earlier than in other places, in part thanks to its active community, researchers, and statisticians.
Further Resource: Alternative to In-house Recruiting For Global Tech Talent
What Is an R Developer?
That's what R is. Now, what about the people who use it?
R programmers, also sometimes referred to as data scientists, are individuals who are experts in the R programming language. Typically, an R programmer will have a deep knowledge and skillset in data analytics and statistical computing, which helps them to optimize their work. R programmers once worked only in academic fields, but now you can find find them in almost every industry as long as data is involved.
R is a complex language and isn't easy to learn. Hence, R programmers are expected to possess strong skills in the R language and should be able to perform tasks like computer modeling, graphical rendering, and more.
What Does an R Programmer Do?
The roles and responsibilities carried out by an R programmer will vary depending on the specific job, industry, and employer. Despite this, there are some commonalities that most R programmers share. These R experts typically perform:
- Coding: As R is a programming language, R experts will be expected to put their expertise to work and engage in coding environments. R code is complicated and typically used for advanced tasks relating to data analytics and modeling. As such, R experts may find themselves working in industries relating to finance, banking, and e-commerce.
- Troubleshooting: Like most programmers, R experts will be expected to troubleshoot and fix any issues that come up with their code. Since even small errors in code can result in errors, R programmers must stay on their toes to ensure that all of their programs run efficiently and effectively.
- Visualization: One of the primary features of R is its ability to visualize data. No matter which industry the R programmer is involved in, data visualization is usually a must, and programmers will often have to visualize and explain important data statistics to their employers.
R Developers Qualifications
To become an R developer, you must have an educational background, such as a bachelor’s, in computer engineering, programming, or a related field. You should also be skilled in a variety of programming languages and have an advanced knowledge of the R language and its environment.
Other qualifications to become an R developer include:
- Years of experience in statistics or modeling
- Teamwork and communication skills
- Experience working in data science, such as designing databases, data engineering, data mining, and data visualization
- Understanding of machine learning and artificial intelligence
- Knowledge of descriptive analytics and statistics, predictive analytics, and prescriptive analytics
- Other relevant experience
R Programmer Salary
According to Hired, the median annual salary of an R developer in the United States is around $168,923. However, the true salary of R experts will depend on many things, including education, experience, the industry and employer, and additional skills. Additionally, senior R developers will typically make more than their intermediate and junior counterparts.
Entry-Level R Programmer Salary
According to Zippia, a junior R programmer will make, on average, $61,448 annually, which translates to $29.54 hourly. However, location will affect how much a junior programmer makes. For example, junior programmers who live in California, Nevada, and Washington will make more than programmers living in other states, such as Ohio or New Jersey.
Junior experts at the bottom of the barrel should make at least $45,000 yearly, while the top 10% can expect to make $82,000 yearly.
The skills expected of a junior R programmer vary, but typically include:
- Skills in C, C++, R, Java, PHP, and others
- R data analysis
- High-performance computing using OpenMPI
- An ability to work with data structures and algorithms
- Knowledge of statistical machine learning
- Audio programming
Senior R Programmer Salary
According to Salary.com, a senior computer programmer makes around $97,712 yearly in the United States. This is the average, and it can range from between $87,531 and $107,557.
The skills required of a senior R programmer include those of a junior programmer, as well as:
- Experience in business analytics
- Fluency in one or several scripting languages, such as Python
- Expert coding skills
How To Find and Hire R Developers
If you’re wondering how to find and hire R developers that are experts, you’re not alone. This is a question that many businesses that use R are faced with. Depending on where you're located, you may have many developers to choose from — alternatively, you might need to seek out remote talent. Regardless of which situation fits you, you must know in advance the best places to look, the best questions to ask, and how to choose the right candidate for your business.
To find your developer, you’ll want to create a clear and concise job ad that clearly states all aspects of the job description, qualifications expected, and benefits offered. Once you’ve done this, you can then start posting your ad and job description. There are a few places you should definitely post your ad:
- Local job fairs: Job fairs are a way for you to start at the heart of the job search. You’ll be coming face-to-face with potential candidates and communicating with them directly. Face-to-face communication will allow you to assess a person’s attributes and decide whether they’d align well with your business.
- Local universities: Colleges and universities typically offer a continuous talent supply. As students graduate yearly, business owners and entrepreneurs can meet and connect with potential employees who can start working right out of university.
- Online job boards: With online job boards, not only can you post job ads and descriptions and have talent come to you, but you can also search through resumes that have been uploaded by potential candidates. Many job boards also provide tools to manage job applications and candidates.
- Social media platforms: Social media platforms are another way to find would-be employees. If you’re looking to hire, post about it, and have potential talent come to you. You can also use LinkedIn to search for candidates, view their experience, and reach out to them to apply to your job listing.
- Talent marketplaces and recruitment firms: If you don’t want to do the searching yourself, you can turn to a talent marketplace or recruitment firm that can handle it for you. Tell them what you want, and then let them do the rest.
Now that you have an understanding of where to look, you should understand what to look for and how to find the best talent to hire. Before considering what you’re looking for, you should determine what your company’s goals and vision are.
When it comes to hiring the best developers for your team, assess them for the following:
- A portfolio showcasing past their work, including intellectual property, data science projects, data analysis, and statistical analysis
- Their education and relevant experience
- Professional references and recommendations
- Relevant and additional skills
Work With Revelo to Hire Developers
Finding and hiring dedicated developers doesn’t have to be hard. If you’re unsure how to proceed on your own, Revelo can help. We’re a talent marketplace that helps businesses and entrepreneurs connect with top-quality talent in Latin America. We handle all the nitty gritty details, from hiring, administering benefits, and processing payroll. All we need you to do is pick your ideal talent from a pre-vetted list and leave the rest to us.
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