R is an open-source programming language that is free to download and use. This scripted language provides users with a variety of analytical and mathematical methods, as well as statistical graphing capabilities. R is considered unique because it’s not a general-purpose language. Instead, it is used for specific tasks. R includes a range of built-in features for statistical computing and doesn’t require third-party libraries to perform most data analysis. This article will take a closer look at R’s uses and the benefits of studying this language. 

What is R Programming?

R is a programming language that statisticians created for statistical data analytics. This popular language has a range of applications for performing statistical computing and creating data visualizations. It is often used by Data Scientists, Business Analysts, Data Analysts, and those working in academia or science for tasks specifically involving statistical analysis. R is currently available for free and can run on Windows and Mac OS, as well as a variety of UNIX platforms and related systems.

R provides users with a range of graphical and statistical techniques, such as time-series analysis, clustering, classification, and linear and nonlinear modeling. One of the benefits of working with R is that it simplifies the process of creating publication-quality plots, especially those that incorporate formulas or mathematical symbols. This versatile language includes a fully integrated suite of software tools, such as a data storage and handling facility, operations for performing calculations on arrays, an extensive, integrated set of data analytics tools, and graphical tools designed to analyze and visualize data.

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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. 

In-Depth Review of the Cost

R’s software environment, which was created for graphics and statistical computing tasks, is available as a free download. Because R is free, it’s a largely accessible language for those working in programming, statistics, and the data sciences. Currently, more than 2 million people worldwide work with this programming language. 

Because it’s open-source, R has many advantages over other languages:

Why Learn 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.

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Free Introductory R Programming Course Online 

Are you looking instead for an overview of R programming before diving into an entire course? If this sounds like you, you can begin learning about R programming and other core data science concepts for free online. In this free Intro to Data Science seminar offered by Noble Desktop, you’ll receive an 80-minute overview of how Python is used in data science. This webinar covers the advantages of working with Python in data science and an introduction to the field of data science. This seminar is a great way to learn about data science before enrolling in a class or certificate program.

Other top educational providers also provide free beginner-friendly R materials. Coursera’s R Programming is a fully online, intermediate-level R class that spans 57 hours. Participants learn how R is used for data analysis and different practical issues that can occur in statistical computing, such as using R loop functions and debugging tools. Udemy’s R Basics: R Programming Language Introduction is a free, beginner-friendly class in which participants study how to navigate the RStudio interface, create basic graphs, and execute common commands using R. Udemy also offers a business-specific R course: learn R for Business Analytics. Those enrolled receive instruction on how to leverage R for business analytics. Topics covered include leading packages into R, importing data into R, and running various macros with this programming language.

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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.