Learn R: Best Courses, Books, and Resources for Learning R

If you want to analyze and visualize large data sets, then learning R is a useful skill to have in your tool belt. R is a popular programming language used for a wide range of data science and analysis problems. When you learn R, you gain access to an array of statistical and graphical tools for data analysts.

The R programming language can be used for projects like analyzing small data sets about stock prices to large-scale analyses of data collected on social networks. This flexibility makes R a highly-favored skill among data scientists.

But how do you get started learning how to code in R? This guide will teach you how to learn R and get analyzing in no time. We’ll break down a few top tips to help you start your journey to learning R for free.

What Is R?

R is a programming language and environment for mathematical and statistical computing. The language was developed at Bell Laboratories by John Chambers and a team of colleagues. R is often considered to be a newer implementation of another data science language.

That’s the technical definition of what R is, but in practice the R programming language allows you to perform statistical analysis and data visualization on data sets, such as through using linear modeling, time-series analysis, classification, and more. The language is open source, so the features you need to analyze a data set are always free and easy to access and use.

Why Should You Learn R?

Unlike other languages such as Python and Java, R is known for having a steep learning curve. But don’t let that put you off learning how to code in R. There are still plenty of reasons why learning R is a great investment. The main reason you should learn R is that it is the industry standard for data analysis and data visualization.

R is built for data science

The R programming language, unlike Python, was built specifically to support mathematical calculations and data analysis. Data scientists learn R around the world and with good reason. Almost all the features data scientists need are available with R. According to Statista, R is the second most popular programming language used by data science professionals in the US.

Learning R allows you to run data science calculations without any compiler, which makes the development of code more efficient. Also, R is a statistical language, so it’s easy to convert statistical models into code using R.

R has a supportive developer community

As data has become an increasingly important part of the tech industry, the data science field has grown. Because R is so popular, it’s easy to find other developers to help with the issues you are facing, and because R is an open-source language is very well documented.

There are already thousands of questions with answers and discussion threads about R on sites like StackOverflow. This means that you should have no trouble finding a solution to a problem. Some developers even share custom-built packages, like the extremely popular ggplot2 package for building graphs.

R is used in industry

R is not just a language used by hobbyists for data analysis, it is the tool the big players are using in the industry. R is used for machine learning, deep learning, statistical programming, and business analytics. For instance, Facebook uses R for behavior analysis related to status updates. Google uses R to measure the effectiveness of its advertising campaigns.

Microsoft acquired the Revolution R company and has also used R throughout its organization. Bing, Merck, TechCrunch, and Mozilla also use R, and scientists in many fields use R to analyze data they collect in their research experiments. According to Indeed.com, as of August 17, 2022, there are 6,724 jobs in the United States for the query “R Data Scientist.”

What Is R Used For?

R is used for statistical programming and graphics. It is commonly used in data science to process large amounts of data, identify trends, and visualize data. In addition to making it easy to perform statistical calculations, R provides a range of tools for sharing data. It’s easy to export data from R in graphical forms that explain a dataset.

R programming is used in a range of settings, from healthcare to technology. Academia uses R more than any other industry according to StackOverflow. The same study found that R is increasingly used in the healthcare industry, government, consulting, and insurance. There is no easy answer to the question “what is R used for” because it is used in so many settings.

How Long Does it Take to Learn R?

It takes around one month to learn the basics of R, assuming you spend an hour a day devoted to your studies. If you want to use R in a professional environment, you can expect to spend at least six months developing the knowledge you need.

Like any programming language, it’s hard to estimate how long it will take you to learn it. R is very different from many programming languages. While a technical background will help you, even beginner concepts will be unfamiliar to a lot of programmers.

How long it takes to learn R also depends on whether you already understand the statistical analyses you are trying to do. For example, if you already know about regression analysis it will be much quicker for you to learn how to do regression analysis in R.

What’s more, there are thousands of packages you can use in R. These packages are written to apply R in various specific contexts. You can expect to spend months learning how to use some of the bigger packages.

Is R Hard to Learn?

R is known for being hard to learn. This is in large part because R is so different from many programming languages. The syntax of R, unlike languages like Python, is very difficult to read. Basic operations like selecting, naming, and renaming variables are more confusing in R than they are in other languages.

If you are already familiar with basic data science principles and have some coding experience, you’ll probably encounter a little bit of friction as you start learning R. A survey of professional developers on StackOverflow found that 41.6 percent of professionals love R, while 58 percent of professionals dread it.

R is hard to learn, but over time you’ll become more familiar with the rules of the language. This is the case for all programming skills. At first, you’ve got a long journey ahead. Once you’ve mastered the fundamental concepts, you have the knowledge and mindset you need to explore more difficult concepts.

How to Learn R: Step-by-Step

The question you may have now is: How do you learn R fast? To answer this, we’re going to break our question down into two parts. We’ll discuss the core skills you need to learn R and the resources you can use to build those skills.

Step 1: Establish Your Motivation

Before you even start learning R programming, you should ask yourself: What is your motivation? It can be useful to have a clear idea in your mind of why you want to learn R. This will make it more difficult to give up when you encounter a challenge.

Do you want to analyze some personal data? Or do you want to become a data scientist? Or do you just enjoy working with datasets and want a challenge? Whatever your motivation, try to make it clear and specific.

If you want to become a data scientist, ask yourself what you want to analyze. Do you want to analyze stock market data, healthcare data, or sports data? Once you’ve established your motivation to learn R, you will be ready to start on your journey to learning how to code in R.

Step 2: Learning the Basics

You’ve decided to learn R. Your main focus should be on mastering the basics. This will give you a solid foundation upon which you can build your skills. To help you get started, this is a list of must-learn r basics:

• Syntax and Variables
• Data Frames
• Data Cleaning and Visualization
• Statistics with R
• Testing Hypotheses

Step 3: Attend a Coding Bootcamp

Given the high demand for R skills in the data science field, many coding bootcamps have adopted R as part of their curricula. If you are looking for a more immersive, project-focused experience, then looking into R bootcamps for data science may be worth your time.

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Find Your Bootcamp Match

To help you get started, check out the Career Karma coding bootcamp directory. Our directory features a list of hundreds of coding bootcamps, including bootcamps that specialize in R and data science.

Step 4: Work on R Projects

Taking online courses and following tutorials will only take you so far. Once you’ve mastered the syntax, you’ll be ready to start working on your own R projects. R projects let you build your skills while working on a project that is meaningful to you. When you work on a self-driven project, you get to choose what you work on and how it turns out.

Are you stuck for ideas? If that’s the case, ask yourself: What data do I generate every day? Then, try to think of an R project you could build using that data. Maybe you want to analyze data from your fitness tracker. Or maybe you want to analyze your finances. Or maybe you are interested in weather trends from your local area in spring.

No matter what you end up working on, make sure it is relevant to one of your own interests. This will ensure that, even if you encounter a challenge, you’ll have no problem finding the motivation you need to keep going.

R Project Ideas

The first project you work on should be simple, to ensure that you don’t take on too much too soon. This is a common mistake made even by seasoned programmers who are learning a new skill. When you feel more confident, you can start adding new complexities to your R projects.

If you can’t find a data set to work on, you should check out Kaggle. Kaggle is a site for data analysts and scientists to work together and share project ideas. On Kaggle, there are thousands of open-source data sets that you can use in your projects free of charge. This almost eliminates the problem of “I can’t find the data I want!” when you’re working on a new R project.

Here are a few ideas to help you out:

• Analyze the performance of the stock market
• Sentiment analysis of Amazon reviews
• Movie recommendation engine
• Analyze donations data for the US presidential election
• Analyze voting turnout data for the UK general election

Step 5: Explore More Difficult Topics

Once you’ve built a few projects, you can then go on to explore more difficult projects. This is especially important when you’re learning R because the steeper learning curve can make it easy to stop learning past a certain point. You need to keep going and challenge yourself to reach new heights.

Here are a few questions you should ask yourself to encourage you to think about how you can improve. These questions will help you get thinking about how you can improve even once you’ve learned the basics. Because, after all, when you’re learning to code there is no real end. There’s always something new you can learn.

• Can your project handle a larger data set?
• Can you add predictive features to your project?
• What biases may exist in your analysis? How can you combat those biases?
• How can you improve the performance of your project?
• Could you generate more visualizations from your data?

Step 6: Join an R Developer Community

Learning how to code is hard. And learning the syntax for R is its own challenge. As a result, you may encounter situations where you feel tempted to give up and do something else. One way to help address these situations is to join a developer community for R. Developer communities are communities of programmers who come together to share ideas and collaborate on projects.

Developer communities bring together large numbers of developers, from beginners to experts, into one room to discuss coding. As a result, these R communities are often great places to meet new coders and find solutions to the problems everyone is facing.

R Developer Communities

Joining a developer community is not enough, though. Once you join an R community, you should start contributing as soon as possible. Volunteer to help others if you can, or ask other people useful questions to help keep discussions going. Here are a few great communities for aspiring R developers:

• RStudio Community: The RStudio community is a group of R developers and users of the RStudio package who come together to discuss R programming.
• Dev.to: Dev.to is a community of developers helping each other out. There are a number of great data science threads that can be found on Dev.to, even though their R community is new.
• StackOverflow: StackOverflow is a questions-and-answers site for programmers. Over the years, thousands of people have posted questions related to R. If you need help with something, you should be able to find an answer on StackOverflow.
• R-Help Mailing List: Mailing lists may be an old idea. The R-Help mailing list is a great place for R developers to stay up to date with the latest goings-on in the R community. However, this is not the place to ask beginner-level questions.

Step 7: Practice Your R Skills

Practicing is the best way to become an expert R developer. As the old saying goes, “practice makes perfect,” so you should practice R programming to really master R.

Learning how to code is a skill, and like any skill, practicing is the best way to get ahead. With R, practice is especially important because many of the syntax features you will encounter can be difficult to understand at first. But, as you get more practice programming in R, you’ll adapt to the quirks of the language.

We cannot emphasize the importance of practicing enough. The more you practice, the better you’ll get: it’s that simple. Are you looking for ways to practice your R programming skills? Here are a few suggestions for how you can do so:

• Build a project that interests you. Working on a project requires a big commitment. It is worth it because once you start you’ll not want to give up until you are done.
• Participate in coding challenges. Sites like Coderbyte and Codewars feature coding challenges specific to R that you can use to build your skills.
• Analyze datasets for fun. If you have a spare few hours, don’t hesitate to go onto Kaggle, read over a challenge, and give it a go.

R Programming Basics: Master R Fundamentals

The topics we discuss below cover the main concepts that you need to know to get started and learn R basics. There is a lot more you can do with R but this should give you a nice starting point.

Syntax and Variables

There’s no escaping learning the syntax for R, the rules that govern the language. It is notoriously complex to learn R’s basic syntax. The good news, however, is that once you’ve learned the syntax for R, you will not have to learn it again!

After learning the basic syntax of R, you can go on to explore variables and data types. Here are the main topics you should cover:

• How to perform mathematical calculations
• Comments
• Data types
• Variables and vectors
• Comparison and logical operators
• Conditionals
• Importing functions

Data Frames

Data frames are objects that store data in a table structure with rows and columns. Think about a data frame like a spreadsheet, but one that can be more highly customized. The main topics you should learn about related to data frames are:

• What is a data frame?
• How to work with CSVs in R
• How to save data to a CSV
• How to inspect a data frame
• How to select and exclude columns and rows
• How to arrange rows

Data Cleaning and Visualization

Data cleaning refers to the process of preparing a dataset for analysis. To clean a dataset, you need to remove any data that is not useful for your target use case. This is a very important step to learn in R basics because clean data allows you to do good analysis.

Visualization is a technique used to turn the raw data with which you are working into charts. These charts showcase your data visually. Here are the main topics you should cover in these two fields:

• How to tidy/clean data
• Reshaping data and removing duplicate values
• Splitting a dataset by index and character
• Parsing strings
• How to use ggplot2 to create visualizations
• Aesthetics for data visualization
• Adding labels to a data visualization

Statistics with R

R offers a wide range of features to perform statistical analysis on a data set. So, whether you want to calculate the median of a range of data, or perform a standard deviation calculation, R has you covered. Here are a few of the main statistical concepts you should learn about:

• Mean, median, mode, and range in R
• Variance and standard deviations
• Quartiles and inter-quartile ranges

Testing Hypothesis

Data analysts write a hypothesis and use data to figure out whether their hypothesis does or does not exist. An essential part of working as a data analyst is being able to test hypotheses. Here are the main topics you should cover to master testing hypotheses:

• Formulating a hypothesis
• Designing an experiment to prove a hypothesis
• Significance levels using p-values
• T-tests
• Assumptions associated with hypothesis testing

How to Learn R for Free

R is free, open-source software, so it only makes sense that it is easy to learn R for free. There are a lot of different options available to you, from self-guided reading to instructor-led classes. You can choose a blend of resources that best matches your learning type.

Programming is a practical skill, and you need to step through real-world examples to fully learn r programming. Once you’ve worked through some free R learning resources, try a data analysis project to build your portfolio and see if you are ready to move on to more advanced topics.

• Free online R courses. If you like a highly structured learning environment, an online course is a great way to learn R. In most free versions of online R courses, you aren’t required to do any assignments. However, you should always take the time to go work through any optional assignments because it is a great way to learn. 
• Free R books. R books are comprehensive resources that cover a lot of topics in R. It’s great to use these as references to help you piece together unrelated courses and tutorials. You can download a free eBook or borrow a physical copy from your local library. 
• Free R tutorials. If you like to learn by doing, tutorials are the best choice for you. These guided programs take you through practice problems in the R environment. 
• Free R discussion boards. One of the universal truths of coding is that you will run into error messages that you don’t understand. When this happens to you, search a free R discussion board or post a new question with the error message and your code. Either the answer already exists or someone should be able to point out a mistake that you missed. 
• Free R YouTube videos. Short YouTube videos usually focus on a very specific R topic, like how to import a CSV data set. It’s a good place to go when you know exactly what you need help with, but you won’t be able to learn R just through YouTube videos.

Best Way to Learn R Online

So far, we have discussed why you should learn R and what topics you should cover. But where can you go to actually learn these topics?

That’s a great question! There is no right answer in terms of the “best resource” for learning R. The best resource for you will depend on your learning style. Ask yourself: Do you like reading guides, or do you prefer step-by-step tutorials? Do you like online courses or prefer interactive walkthroughs?

The best way to learn R is to experiment with a few different learning formats to see what sticks. You can focus on one or two sources when you have found something you like.

Online R Courses

Data Science: R Basics

• Cost: Free
• Audience: Beginners

Coming to us from Harvard via edX, this r programming course will teach you how to wrangle, analyze, and visualize data in R in just a few hours a week. The course is free, but you can pay to get graded homework and a certificate of completion.

R Programming Basics For Data Science

• Cost: Free
• Audience: Beginners

IBM’s edX offering will have you doing basic data science in five weeks. You’ll learn how to use RStudio, practice how to write a program, and complete a final data analysis project. This free course also has a paid version which offers more hands-on support and a certificate at the end.

R Programming

• Cost: Free
• Audience: Intermediate

The R programming course is taught by Roger Peng, author of one of the best R books on our list. This program is hosted by Coursera and takes 57 hours to complete. Here you’ll learn how to install R, build simple functions, and debug your R code. 

R Basics – R Programming Language Introduction

• Cost: Free
• Audience: Beginners

If you have no experience in programming at all, consider this Udemy course targeted at absolute beginners. If you’ve struggled with more advanced learn R online courses, use this course to learn the R environment and key R vocabulary to get you back on track.

Learn Data Science With R

• Cost: Free
• Audience: Beginners

This R programming course is a ten-part series that also teaches you about the field of data science. The first part of the series covers 8 hours of recorded lectures. This Udemy offering will teach you the basics of data structures, vectors, and data frames, all key skills for new data scientists!

Online R Books

R For Data Science by Hadley Wickham

It’s hard to go wrong with a nice overview written by an O’Reilly author. In fact, this is one of the best books to learn R. At over 500 pages, this volume will introduce you to the basics of importing data, tidying it up, analyzing it, and visualizing it. With this foundation in place, you’ll be prepared to do high-quality data science with R.

Introduction To Data Science by Rafael Irizarry

While this isn’t an R tutorial per se, it will walk you through the foundational concepts in data science, such as statistical analysis, hypothesis testing, and regression models, all with examples in R.

R Packages by Hadley Wickham

Any language as old and widespread as R is going to have lots and lots of third-party software packages. Getting a handle on this ecosystem is easier with an overview like this one.

Exploratory Data Analysis With R by Robert Peng

One of the most important skills for a new data scientist to learn is ‘exploratory data analysis.’ This consists of simply getting a feel for the data by inspecting them, making simple plots, and performing simple tests. This book shows you how to do that in R.

Advanced R by Hadley Wickham

If you already have some coding language skills, Advanced R will show you the coding syntax needed to translate your experience to R. This isn’t an R book for beginners, but once you are ready for more challenging concepts, Advanced R is an invaluable resource.

Online R Resources

Impatient R

Impatient R is an online tutorial aimed at those who are just starting to learn R. This tutorial covers all the key concepts you need to know, from objects to common R jargon.

Cookbook for R

Cookbook for R is a repository of solutions for common R problems. This online resource covers topics like formulas, the basics, data input and output, and creating graphs. Cookbook for R is an excellent reference guide.

R Bloggers

R Bloggers is a community of tutorials about R. This website features hundreds of tutorials that have been written by a range of contributors. You’ll find guides covering everything from data formats to removing outliers.

swirl

swirl is a downloadable package that teaches you how to program in R from the R console. It has a large library of tutorials that take 10 to 20 minutes each. This resource is for complete beginners and novices who want to start learning R.

StackOverflow R Questions

StackOverflow has a page devoted to questions about R topics. It’s a searchable page, and you will probably find that someone else has already asked the same question you have. Users can tag questions with specific R packages or errors to make their questions and answers easier to find.

Should You Learn R Programming?

R is a programming language that has become increasingly popular over the last few years among data scientists and analysts. Being able to code in R not only can help you unlock career opportunities. Knowing R will also give you a whole new range of skills that you can use to analyze data. You’ll also build the mindset you need to approach problems in your life using data.

There’s no way around it, learning R is a challenge. Its syntax can be confusing even to professional programmers. R is constantly changing, so you may find that over time you need to come back to basic concepts and master them again. But that’s just part of being a programmer because all the best programming languages evolve over time.

Learning R is not only possible but within reach, if you follow the steps we outlined in this article.Following these steps will smooth out your journey to learning R. Then, once you’ve learned R, you can start working on a wide range of exciting projects: the sky’s the limit!

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