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 Features

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.

Impressive Compatibility

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.

Distributed Computing

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.

Fast Calculations

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.

Integration

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.

Machine Learning

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.

Open-Source

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