Gain a comprehensive understanding of R programming, a language created for statistical data analytics, and learn about its applications in various fields like data science, academia, and business analysis. Discover the versatility of R for tasks like data storage, generating statistical models, creating customized data visualizations, and making accurate data-driven predictions

## Key Insights

- R programming is used for data analytics and visualization, with a powerful ggplot2 package for creating any plot you can imagine.
- Although R is considered a complex language due to its many commands and inconsistent analysis ways, enrolling in an in-person or live online Data Science class can help overcome the challenges.
- R is often compared to Python, another data science language. Python is more flexible for tasks like data analysis, while R focuses more on the statistical element.
- Major companies like Amazon, Google, and Meta utilize R for exploratory data analysis, statistical analysis, and machine learning tasks.
- Noble Desktop offers a variety of in-person and live online coding classes including a "Data Analytics with R Bootcamp" for those new to R or more advanced programmers.
- Salaries for positions that utilize R programming can vary widely, but typically are competitive due to the specialized nature of the skills required.

Are you curious about learning R programming but worried it might be too hard? Of course, the difficulty that comes with learning a new skill is somewhat subjective. The challenges of learning R depend on factors like your prior background in programming and statistics and your knowledge of data analytics and data visualization. No matter your current schedule or comfort level with R programming, plenty of tools are available to help make learning more manageable than you might think.

## 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.

Read more about what R programming is and why you should learn it.

### 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.

## What Are the Most Challenging Parts of Learning R Programming?

R is considered by most to be a relatively difficult programming language to learn. One factor contributing to this difficulty is the sheer number of commands R users must learn. Whereas other data science packages include few commands for analysis and many options for controlling output, R functions oppositely, which can be an adjustment for some learners. When performing linear regressions in SPSS or SAS, you generally specify elements in advance, then see the output all at once, including the ANOVA table and equations coefficients. On the other hand, for those who create models in R, one command will yield parameter estimates while another generates the ANOVA table. Even though SAS or SPSS output can include more information than you may find necessary, it is useful for helping you identify which output pieces you want without the need to remember specific commands to retrieve this information. When working with R, you will need to recall more commands, making it much harder to learn and use than other languages.

Another factor contributing to the difficulty of learning R is that this language has inconsistent analysis ways when more than one variable is present. R functions are inconsistent in the objects they accept and the number of objects they accept. For example, many beginners learn the function summary(x) as a starting point when studying R. This can calculate the summary statistics for the variable x. You may then incorrectly assume that if you’d like to analyze two variables, you would simply enter summary(x, y). Many R functions like this one only accept single objects. This means you’d have to place both variables into a single object, like a data frame, to perform this calculation. R users must memorize these inconsistencies to be able to work with R.

A great way to overcome some of the challenges of working with this programming language is to enroll in an in-person or live online Data Science class.

## How Does Learning R Programming Compare to Other Languages?

Learning R is considered one of the more challenging programming languages to master. This is because its syntax is quite different from other coding languages. In addition to R’s syntax being challenging for some to read, standard operations like naming or selecting variables are more demanding in R than in other languages. R users sometimes struggle to learn this language thoroughly due to the number of commands available, misleading parameter or function names, inconsistent syntax, and the lack of beginner-friendly help files.

One of the programming languages that’s often compared to R is Python. Just like R, Python can be downloaded and used for free. Both also have many applications in data science, machine learning, and data analytics. While these languages can create similar end results, they have several significant differences. Whereas R focuses mainly on the statistical element of a given project, Python is a general-purpose language with applications for bringing a project into a production environment and tends to be more flexible when performing tasks like data analysis.

Because Python was created to be used for software development, those with a background in C++ or Java likely will be able to learn Python faster than they would pick up R. On the other hand, it may be easier for individuals who have studied statistics to learn R. Since Python has so many libraries, it can be challenging to become familiar with them. R, however, has fewer libraries, making this language easier to work with. Generally speaking, Python’s syntax is easier to read, and some users find it a more straightforward language to learn than R.

Noble Desktop’s How to Learn Data Science seminar provides a comprehensive overview of this field for those interested in learning more about data science. Information is available about the benefits of learning data science, the career options available for those with a background in this field, and in-person and live online training options for individuals interested in learning more. Interested learners can also learn about coding by reading Noble’s Learn Coding page. This article provides a general overview of what coding is, what can be done with it, the careers that require a background in coding, and how to learn more about it.

## Surprising Tasks You Can Accomplish with R’s Packages

Although R is commonly used for statistical computing and data visualization, this versatile language has many other applications beyond these tasks. For example, R users can design their own interactive web apps in R simply by writing a few lines of code. The following are just a few of the ways you can use R’s extensive packages to help you better handle data:

- Using the R Markdown package, you can create reproducible Microsoft PowerPoint presentations or Word documents by slightly changing one line of code.
- The Keras package helps R users fit deep learning models directly from R.
- Users can convert various R functions into web APIs when working with the plumber R package.
- R makes it easy yot connect to nearly any local or remote database with the help of its dbplyr package.
- It’s easy to create video game interfaces in R using the nessy package.
- R users can analyze data directly from R with the help of the sparklyr package, which was designed to fit large machine learning models on vast datasets.

## Who Uses R?

The R programming language is used across industries and professions. The following are just some of the major companies and organizations who work with R for their programming needs:

- Amazon uses R for exploratory data analysis, statistical analysis, and machine learning tasks.
- Deloitte Counseling works with R to perform machine learning and exploratory data analytics.
- HCL Technologies employs R for various machine learning tasks.
- Novartis leverages R for machine exploratory data analysis, machine learning, and statistical analysis.
- Google employees rely on R for machine learning tasks and exploratory data analysis.
- Meta works with R for both exploratory data analysis and statistical analysis.

## 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.