What is R Programming?

R is an open-source programming language that’s commonly used as a tool for data analysis and statistical functions. R is an implementation of the S programming language. It is available on many popular platforms, such as Linux, macOS, and Windows. In this overview, you’ll learn more about what R Programming is, what it can do, who uses it, and how to learn it to determine how to add this skill to your professional toolbox.

What Can You Do with R Programming?

R was created to primarily help with graphics and statistical computations. This language can accomplish various tasks, from data storage to data analysis to generating statistical models. Of all the available programming languages, R is considered to be the one with the most tools devoted exclusively to statistics. This language can aid with descriptive statistics tasks, like calculating standard deviations and designing models for mapping linear regression.

One of R’s most useful features is its ability to help users create customized data visualizations and dashboards. Some consider R’s ggplot2 package the best data visualization tool available. This package allows users to draw nearly any plot they can conceive. In addition, those who wish to take their data visualizations in R to the next level can combine ggplot2’s syntax with Plotly’s interactive features to make dashboards that are as engaging as they are interactive. 

R draws from various machine learning tools so that users can make accurate, data-driven predictions. Users can select from an array of machine learning models, which have applications for creating predictive models, such as movie recommendation systems and churn models. In addition, R users can automate reporting by using R Markdown documents. R Markdown offers a straightforward, accessible syntax to generate various reports, such as presentations, books, or other written documents. This helps R users easily communicate data analysis results with others. 

How Do You Download R? How Much Does it Cost?

R is an open-source software environment that can be downloaded and used for free by anyone. Open-source software is publicly accessible, which means it can be modified and shared by others. R was designed to run on various UNIX platforms and Windows and macOS. If you want to download R, it’s available on The R Project for Statistical Computing’s website. Before downloading, you must choose your preferred CRAN mirror. 

RStudio is also available as a free, open-source, integrated development environment specifically for R. Before using RStuido, you must install R. The RStudio interface displays data tables, R code, graphs, and output, all in one place. In addition, this environment provides a platform with which users can import Excel, CVS, SPSS, SAS, and Stata files into R without having to write a single line of code. RStudio Cloud is also available as a free download. 

What Are the Benefits of Learning R Programming?

Learning to program with R has many benefits and applications across data-related industries and professions. This language is free and open source, which makes it widely accessible. Because it’s available under the General Public License, it has no license restrictions; users can modify code as necessary. Since this language can run on various operating systems, it performs seamlessly, whether you’re using a Windows, Mac, or Linux-based system. In addition, R offers an array of built-in functions and more than 10,000 packages, which can help with data manipulation, statistical modeling, machine learning, and data visualization, among others. 

Another perk of working with R is that it has a large community that can assist as necessary with questions and other R-related topics. This means that R users can seek advice from those who have completed projects like the one they are working on or collaborate with others. There are even data science contests available to test users’ R skills. For those working with data visualization, R offers packages like plotly, ggvis, and ggplot2, which are great resources for designing print-quality graphs. R’s package, Shiny, allows users to create their own dashboards and interactive web pages right from the R Console. Shiny web apps can then be hosted on any cloud service, like AWS.

Read more about why you should learn R programming. 

R Programming Careers

Because of the massive volume of data being created each day, those with the skills to analyze and visualize it are in demand in many professions and often receive high salaries for their skills. Since R is a highly versatile programming language, it is used across many industries and professions, such as in financial services, academia, retail, healthcare, manufacturing, and data journalism. It is also a widely used language across data science and analytics fields. 

R Programmers are involved in designing the tools used to streamline the data analytics process. They work with data structures like data frames, vectors, and matrices to facilitate fast and efficient data analysis. Data Analysts work with R to locate, retrieve, and deliver data insights. Data Scientists commonly use R to create and implement the layout of a large dataset, as well as for tasks such as data mining, data modeling, research, and forecasting. 

In addition to Data Scientists and Data Analysts, Data Architects also work with R when they perform data warehousing, data modeling, or system development. Geo Statisticians use R to examine both spatial and spatiotemporal datasets. Those working as Quantitative Analysts sometimes use R to ensure clients’ portfolios are risk-balanced, as well as to apply mathematical models to identify new opportunities and evaluate asset prices. Because of R’s data visualization capabilities, Data Visualization Analysts also use this language when they create and share interactive dashboards that are intended to help policymakers make more informed decisions.

How to Learn R Programming

If you want to study R programming, the good news is that excellent study options are available online and in person. Learning R in the in-person environment is a great way for extroverted individuals to master core programming concepts. Classroom study provides access to a computer lab with the most up-to-date software and offers a way for students to connect with others on the same learning journey and receive guidance from an expert instructor. Live online classwork offers the same components as in-person programs, such as the ability to connect with an instructor in real-time and the added perk of allowing students to complete all coursework remotely from the comfort of their home or office. You can browse nearby in-person R classes to find one close to home or search specifically for live online R classes that you can complete from anywhere.

Another way to study R is to enroll in self-paced R courses. Top educational providers like Codeacademy, Coursera, and edX offer on-demand R lessons and tutorials, which can help students study this programming language at their own pace and from their preferred location. Unlike in-person and live online study, asynchronous R classwork does not entail meeting at a regularly scheduled time to complete lessons. Instead, students work with pre-recorded videos, tutorials, and other learning materials, to gain an overview of core R concepts. Self-paced learning doesn’t allow students to get support when they have questions, so this method is best for beginners interested in studying the basics of programming with R. To master more advanced programming concepts, it may be necessary to enroll in a course that provides real-time access to an instructor.

Noble Desktop also offers an Intro to Data Science seminar for those interested in an overview of the field of data science. This video briefly introduces data science and discusses the different tools Data Scientists use. It also contains information on how professionals can use data science in the business sector. Those interested in only studying R programming can browse nearby R classes to find a learning option most suited to their learning needs.

Read the full guide on how to learn R programming. 

A Brief History of R Programming

In 1993, Statisticians Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand created the R programming language. They created R to handle data mining and statistical analysis tasks. It originated as an open-source implementation of the programming language S, created in 1976. R was first released in 1995. The R Core Team was created in 1997 and was tasked with continuing to develop this programming language. Then, in 2000, R's initial stable beta version was made available. Three years later, the R Foundation formed, a nonprofit organization devoted to securing support for the R project.

Currently, R is the ranked 11th most-popular coding language available, especially for those who perform data mining. An estimated 2 million people around the globe work with this language. The most recent version of R, R 4.1.2, was released in 2021. As of today, more than 18,000 packages are available for R. Among the most popular is ggplot2, which is used for data visualization. Many of R’s packages make this language faster and easier to use.

Comparable Programming Languages

Another popular programming language that’s commonly compared to R is Python. Similar to R, Python is free and open-source. Both R and Python are widely used in fields such as data analytics, data science, and machine learning. Although these languages bear many similarities, they differ in several important ways:

• R is more commonly used for statistical analysis, whereas Python is often used for general data wrangling tasks. 
• Since Python is a multi-purpose language, it provides a largely readable syntax that most people consider easy to learn. On the other hand, R was created by Statisticians and therefore draws from specialized analytics and statistical models. Many Programmers consider R to be a more complex language to learn.
• Python is considered more versatile for tasks such as pulling data from the internet, whereas R is more suited for importing data from CVS and text files, as well as Excel. 
• R has more extensive data visualizations than Python. Although Python provides visualization options in popular libraries such as Seaborn and Matplotlib, R was created to help users visualize the results of statistical analyses. R also offers ggplot2, with which users can create advanced plots like scatter plots incorporating regression lines.
• Data modeling is easier in Python since it has various data modeling libraries, such as SciPy and NumPy. Those who wish to perform modeling analysis in R may need to work with packages outside this language’s core functionality.

Learn R Programming with Hands-on Training at Noble Desktop

Noble Desktop has several excellent learning options for those new to R and courses for more advanced programmers interested in mastering complex R skills. Noble’s Data Analytics with R Bootcamp is an immersive class designed to take participants from the basics of coding to a portfolio showcasing your experience working with R. Those enrolled receive expert instruction and can retake the class for up to one year to brush up on course materials. 

Noble also offers a variety of in-person and live online coding classes that prepare participants to become Data Scientists or Web Developers. Courses are available in popular programming languages such as JavaScript and Python, as well as front end and full stack web development. Certificates are also offered in software engineering and data science. For students seeking instruction about using programming for finance technology, Noble currently has a FinTech bootcamp available as well, which includes one-on-one mentoring for all participants.

In addition to the variety of programming courses Noble teaches, this top educational provider also has several in-person and live online Data Analytics classes. Noble’s Data Analytics Technologies Bootcamp is a beginner-friendly course that prepares students to work with core data analytics tools like SQL, Excel, and Tableau. In addition, a certificate in data analytics is also available for those interested in becoming a Business Analyst or Data Analyst. This rigorous learning opportunity prepares students to perform data analysis, statistical analysis, and data visualization, as well as how to use relational databases. All students receive one-on-one mentoring to help with their learning process.

Key Insights

• R is a free, open-source programming language with various uses for statistics and data analysis.
• R was first released in 1993 in New Zealand and has remained a widely used language.
• A comparable language to R is Python, which is also open-source.
• You can receive comprehensive R programming training through one of Noble Desktop’s in-person or live online courses.