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10 Best R Programming Courses in 2022

The best online courses to learn R programming, the language used by data analysts and statisticians to clean, analyze, and visualize data.

In this article, I’ve used Class Central’s catalog of over 70K courses to find the best online courses to learn R programming.

If you’d like to know how I chose these courses, you can find my methodology below. But if you’re here for the list, here are my top picks. Click on one to skip to the details:

Course Workload In Brief
1. Data Analysis with R Programming (Google) 37 hours Best comprehensive course for aspiring data analysts
2. R Programming Fundamentals (Stanford) 12—8 hours Covers the fundamentals aspect of R programming
3. Data Science: R Basics (Harvard) 8—16 hours Approaches R from the data science perspective
4. Data Analysis with R (Facebook) 8 weeks Lengthy course covering the entire data analysis workflow with R
5. The Analytics Edge (MIT) 130—195 hours Rigorous course covering analytical methods like machine learning.
6. Introduction to R (DataCamp) 4 hours Short course focusing on R’s basic data structures with free certification
7. swirl: Learn R, in R. (swirlstats.com) Self-paced Learn R directly from hands-on coding in the R console
8. Introduction to Business Analytics with R (Illinois) 16 hours Teaches both R and business analysis
9. Introduction to Probability and Data with R (Duke University) 14 hours Covers statistics and probability with R
10. Data Science and Machine Learning Bootcamp with R (Udemy) 18 hours total Comprehensive project-based learning course

What is R?

R is a programming language for statistical computing and graphics. It was created with statistics in mind and hence, it is the second-most popular programming language for data science behind Python among data analysts, data scientists, statisticians, and the like, especially in finance and academia. According to the TIOBE index, it is currently the 13th most popular programming language in the world.

What makes R so popular is its versatility. R has a convenient library of specialized ready-to-use tools in the form of packages ranging from preprocessing, analysis, and visualization to machine learning, anything under the sun that has to do with data really, thanks to its vibrant open-source community.

Apart from that, R comes with an integrated development environment (IDE) called RStudio that has a ton of unique and specific features for doing data analysis. It is much less of a hassle to do exploratory data analysis and data visualization this way. Also, similar to Jupyter notebooks, R has its own flavor of notebooks in the form of RMarkdown.

If you’re doing anything related to data science, you will certainly come across R. Expect plenty of opportunities in big data and business analytics!

Course Ranking Methodology

I built this ranking following the now tried-and-tested methodology used in previous rankings (you can find them all here). It involves a three-step process:

  1. Research: I started by leveraging Class Central’s database with over 70K+ online courses and 170K reviews. Then, I made a preliminary selection of R Programming courses by rating, reviews, and bookmarks.
  2. Evaluate: I read through reviews on Class Central, Reddit, and course providers to understand what other learners thought about each course and combined it with my own experience as a learner with a computer science background.
  3. Select: Well-made courses were picked if they presented valuable and engaging content and they have to fit in a set of criteria and be ranked accordingly: comprehensive curriculum, price, release date, ratings, and enrollments.

After going through this process — combining Class Central data, experience as a lifelong learner, and a lot of editing — I arrived at our final ranking. So far, I’ve spent more than 10 hours building this ranking, and I intend to continue updating it in the future.

Course Ranking Statistics

Here are some aggregate stats about the ranking:

  • The largest course in this ranking has close to 1.8 million enrollments.
  • All of the courses are free or free-to-audit except for one.
  • Most of the courses are for beginner level, while some are at an intermediate level.
  • edX and Coursera are the most represented course providers in this ranking.
  • Around 7.5K people are following R Programming Courses on Class Central.

Without further ado, let’s go through the top picks.

1. Data Analysis with R Programming (Google)

My #1 pick for the best R programming course is Google’s Data Analysis with R Programming.

This free-to-audit course equips you with the skills needed to apply for entry-level jobs as data analysts through hands-on practice. You’ll learn the ins and outs of the R programming language such that you can clean, organize, analyze, visualize, and report data by making use of the powerful R tools and packages in RStudio.

No prior experience with programming is necessary to take this course.

What You’ll Learn

The course begins with an overview of the R programming language (and programming in general). You’ll write your code in RStudio, a powerful coding environment, and be amazed at how R can perform virtually any task in every phase of the data analysis process.

You’ll start by exploring the fundamental concepts associated with R, most notably data structures like vectors, lists, matrices, and dataframes. R is fundamentally all about manipulating data, and data must be contained in data structures. And to manipulate them, you’ll need to learn the inbuilt functions as well as popular R packages.

Before you start analyzing data, you’ll need to clean the data to ensure that it is mostly error-free. R provides an arsenal of tools in the form of packages, with the most popular package being the Tidyverse package. As part of the Tidyverse, you’ll have access to the functions in tidyr to help you check for missing values and statistical anomalies. Tidyverse also provides ggplot, a powerful and customizable data visualization library with all sorts of features.

R has a number of different options to explore when you are ready to save and present your analysis. R Markdown is a file format for making dynamic documents with R. You’ll end the course by learning how to format and export R Markdown and incorporate R code chunks in your documents.

How You’ll Learn

The course is 5 weeks long, with 37 hours worth of material in the form of video lectures, readings, and assignments. For paying learners, each week ends with a graded quiz and the final week contains a course challenge that will ask you questions about the key skills you’ve practiced throughout the course.

Institution Google
Provider Coursera
Level Beginner
Workload 37 hours total
Enrollments 132K
Rating 4.8 / 5.0 (3.4K)
Certificate Paid

Fun Facts

  • The course has 339 bookmarks on Class Central.
  • This course is the seventh course in the Google Data Analytics Certificate which equips you with the skills needed to apply to entry-level data analyst jobs.

If you’re interested in this course, you can find more information about the course and how to enroll here.

2. R Programming Fundamentals (Stanford University)

My second pick for the best R programming course is R Programming Fundamentals by Stanford University.

This free-to-audit 6 weeks limited access beginner-friendly course is a comprehensive introduction to R. You’ll hear from one of the co-creators of R, Robert Gentleman. By the end of the course, you’ll have a solid foundation of R programming ready to tackle larger projects.

The prerequisites for this course are basic familiarity with computers and productivity software. Experience or background in statistics, a scientific or engineering discipline is helpful but not required.

What You’ll Learn

After a brief history of R, the course begins with the basics of R and RStudio, the coding environment. Next, you’ll study how to create work with R’s data structures — vectors, matrices, lists, and data frames — based on data type, size, and desired outcomes. Then, you’ll learn best practices for importing and exporting data, how to save and retrieve your work, and how to debug when problems occur.

The course moves on to the programming aspect of R. You’ll start simple with writing basic statements and expressions in R. With those under your belt, you’ll find it easier to work with advanced data-manipulating functions in R such as preprocessing, data wrangling, ordering, filtering, and dealing with outliers and missing values.

R is famous for its variety of tools for visualizing data, the most popular tool being ggplot. Beginning with the plotting of simple graphs, you’ll eventually work your way up to extracting subsamples using essential statistical functions and plotting more complex data sets. Finally, the course ends with a tutorial on performing simple tests and regression analysis.

How You’ll Learn

The course is 6 weeks long, with each week taking 2—3 hours to complete. The course provides video lectures and ungraded practice assignments as well as multiple quizzes which are only accessible to paying students. Each of the nine modules comes with one or more graded quizzes to help you retain your knowledge.

Institution Stanford University
Provider edX
Instructor Susan Holmes
Level Beginner
Workload 12—18 hours total
Enrollments 11K
Certificate Paid

Fun Facts

  • The course has 98 bookmarks on Class Central.
  • Susan Holmes is Professor of Statistics at Stanford University
  • Robert Gentlemen, co-creator of R. makes an appearance in this course.

If you’re interested in this course, you can find more information about the course and how to enroll here.

3. Data Science: R Basics (Harvard University)

My third pick for the best data science course is Data Science: R Basics, offered by Harvard University on edX.

What sets this free-to-audit 8 weeks limited access course apart from others is its unique pedagogy. Through a case study focusing on crime in the United States, you’ll analyze and use a dataset to answer questions like ‘What is the smallest state?’,  ‘What is the most dangerous state?’, and ‘What is the average murder rate in the entirety of the US?’ — without googling of course!

Although no programming experience is required, the course assumes you are comfortable with basic math and algebra.

What You’ll Learn

The course is split into four sections: R Basics, Vectors and Sorting, Wrangling and Visualizing Data, and Programming Basics.

You’ll start with an overview of RStudio, R’s preferred programming environment with lots of features designed specifically for data analysis. Then, you’ll move on to the basics of R programming, where you’ll learn how to write simple programming expressions and statements, and the different data types R provides.

Vectors in R are similar to the mathematical vectors you may have learned in school and indeed are the building blocks of many data structures like matrices and data frames. You’ll create vectors and use them to build lists of cities or sequences of numbers which you can then sort or sum. The course guides you on the basics of vector arithmetic, like how to add two vectors together or what it means to scale a vector. Additionally, you’ll use indexing and subsetting methods to find the most dangerous state in America.

Lastly, you’ll be taught the basics of plotting in R to help communicate your insights to a broader audience. You’ll learn four different plots you can use to visualize patterns and trends in the data: line plot, histogram, boxplot, and heatmap.

How You’ll Learn

This course is 8 weeks long, with each week taking 1 to 2 hours to complete. The course contains a series of video lectures and readings.

Regarding assessments, some of the exercises in the course involve writing code and submitting it directly through DataCamp’s browser-based programming environment, although major assessments must be completed using RStudio.

Institution Harvard University
Provider edX
Instructor Rafael Irizarry
Level Beginner
Workload 8—16 hours total
Enrollments 713K
Certificate Paid

Fun Facts

  • Data Science: R Basics has 7.1k bookmarks on Class Central.
  • It is the first course out of nine from Harvard’s intensive Data Science Professional Certificate.
  • The Professional Certificate comes with a companion book written by Rafael Irizarry, the course instructor.

If you’re interested in this course, you can find more information about the course and how to enroll here.

4. Data Analysis with R (Facebook)

Data Analysis with R by Facebook gives a comprehensive (but not basic) introduction to R programming. As stated in the course name, the emphasis in this course is on exploratory data analysis (EDA). EDA explores data through statistical methods and background information to better understand the data you’re working with — is the data promising or a dud?

By the end of the course, you’ll be able to confidently investigate and summarize datasets using R and even create your own analysis to find hidden patterns!

Although the course recommends some background in statistics (like in Intro to Descriptive Statistics from Udacity), it is not required. Also, no programming experience is needed.

What You’ll Learn

Exploratory data analysis (EDA) should be the first step of any data analysis project. But to perform EDA or any other process in the data analytics process really, you’ll need the necessary tools that R and RStudio provides.

You’ll begin by installing RStudio, the R-specific coding environment, and explore its layout and treasure trove of features. You’ll learn the basic R syntax to write R scripts to help you explore and visualize datasets.

Unfortunately, most of the data you’ll receive as a data analyst will be raw and unfiltered. To ensure that you’re not wasting time and effort working on faulty datasets, you’ll need to inspect the dataset thoroughly for any sign of anomalies and outliers in data. The course teaches you the tools to explore one variable with histograms and boxplots, two variables with scatter plots and correlations, and even more variables by adding colors and shapes to your graph.

Finally, the course wraps up by going through a diamond data set. You’ll see how everything you’ve learned in this course is put into practice to help you understand the importance of following the data analytic workflow and knowing your tools well.

How You’ll Learn

This course runs for 8 weeks total and is split into 10 lessons with the final lesson being the final project. You’ll learn from the video lectures supplied in the course as well as the plenty of embedded exercises in Udacity. The final project consists of you creating your own exploratory data analysis on a data set of your choice to help you consolidate what you’ve learned.

Institution Facebook
Provider Udacity
Instructor Moira Burke, Chris Saden, Solomon Messing, and Dean Eckles
Level Beginner
Workload 8 weeks total
Certificate None

Fun Facts

  • The course has 16.2K bookmarks and 18 reviews on Class Central.
  • Data Analysis with R  is part of Udacity’s paid Become a Data Analyst Nanodegree in collaboration with Kaggle. If you enjoy this course and have the money to take yourself further, this Nanodegree is for you!
  • The course recommends Exploratory Data Analysis written by John Tukey as supplementary material if you’re interested.

If you’re interested in this course, you can find more information about the course and how to enroll here.

5. The Analytics Edge (Massachusetts Institute of Technology)

MIT’s The Analytics Edge is for any aspiring data analyst out there who wants to have both a rigorous understanding of analytical methods and R programming.

The contents of this course mirror its corresponding MIT on-campus class, so expect it to be challenging (but rewarding)!

By the end of this course, you’ll have an applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization. Additionally, you’ll have the programming skills to implement all of these methods in R.

To take this course, you’ll need high-school mathematical knowledge of things like mean, standard deviation, and scatter plots. No prior programming experience is required.

Note, this course is archived on edX. This means that you can view most of the course materials, such as lectures and readings for free, but you cannot complete assignments for a grade. There is another version of this course available on MIT OpenCourseWare in case you encounter any problems with edX’s archived course.

What You’ll Learn

The course begins with an introduction to analytics and the R programming language, a statistical modeling language. Data analytics plays a huge role in business and technological affairs, like Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. In fact, you’ll encounter each of these case studies throughout your study of the course.

You’ll start off with a discussion of linear regression and logistic regression. They may be one of the simplest analytical methods around, but don’t underestimate their potential!

Sometimes, an analytical model may be unclear on why it predicts one thing over another. Classification and regression trees aren’t like this, and are very interpretable. You’ll learn how trees can be applied in the Supreme Court and health care cost prediction. Additionally, you’ll cover the field of text analytics, an exciting application of statistical methods where words are analyzed for opinion and intelligence.

You’ll learn about clustering — grouping similar data points together. This may seem simple but such algorithms are used in YouTube and Amazon to recommend personalized content to people.

Then, you’ll unravel the power of visualization for understanding worldwide trends and communicating insights and ideas. You’ll also discuss the idea of predictive policing that uses analytics to stop crime. Finally, you’ll study linear and integer optimization techniques which help airlines offer the best profitable discounts and sports league design their schedules.

Everything I’ve mentioned here will be taught and modeled with R. You’ll see how simple analytics give you and your work a significant edge.

How You’ll Learn

This course is 13 weeks long, 10-15 hours a week. You’ll watch lecture videos and after the videos you’ll answer a quick question to assess your understanding of the material.

If you’re wondering about exercises, there are plenty of them! There’s recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Additionally, each week will have a homework assignment that involves working in R or LibreOffice with various data sets, and after the last week comes a final exam.

Institution Massachusetts Institute of Technology
Provider edX
Instructor Dimitris Bertsimas
Level Beginner
Workload 130—195 hours total
Rating 4.6 / 5.0 (80)
Certificate None

Fun Facts

If you’re interested in this course, you can find more information about the course and how to enroll here.

6. Introduction to R (DataCamp)

DataCamp offers a lot of excellent hands-on coding courses on data science topics, and Introduction to R is one of them.

In this course with free certification, you’ll master the core data structures widely used in R within 4 hours. This may seem a tad boring, but data structures like vectors, matrices, and data frames make up a huge part of R programming — you can’t have data without data structures after all!

No prerequisites are required to take this course.

What You’ll Learn

You’ll begin this course with an introduction to the basics of programming in R. You’ll explore the R console where the magic happens and learn how to write basic expressions and statements in R and the different data types.

Then, you’ll look at the fundamental building-block data structure: vectors. A lot of data structures are based on multiple vectors. You’ll learn how to create, name, select, and compare vectors in R.

Other data structures you’ll be learning about are matrices, data frames, and lists. You’ll learn to work with them, like doing calculations with matrices, holding and selecting data in data frames, and subsetting elements of different data types from a list. Additionally, you’ll learn about categorical data, called factors in R, and see how they are represented in the various data structures.

How You’ll Learn

This course is 4 hours long and made up of six topics. You’ll learn fully from hands-on coding exercises and receive immediate feedback in DataCamp’s specialized learner programming environment.

Institution DataCamp
Instructor Jonathan Cornelissen
Level Beginner
Workload 4 hours total
Enrollments 2.2M
Certificate Free

Fun Facts

  • The course has 1.2K bookmarks and 12 reviews on Class Central.
  • DataCamp offers many data science and business courses, but only six of them are completely free.
  • Jonathan Cornelissen is one of the co-founders of DataCamp and the initial DataCamp CEO. He holds a PhD in financial econometrics, and was the original author of an R package for quantitative finance.

If you’re interested in this course, you can find more information about the course and how to enroll here.

7. swirl: Learn R, in R. (swirlstats.com)

The description in swirl’s website says it best: “swirl teaches you R programming and data science interactively, at your own pace, and right in the R console!

Unlike the traditional video courses in this ranking, swirl is an R package that turns the R console into an interactive learning environment. You’ll progress through the self-paced lessons through 100% hands-on coding and receive immediate feedback!

Although most of the original swirl content was developed with beginners in mind, swirl also covers advanced R programming topics, from regression models to statistical inferences.

What You’ll Learn

swirl consists of multiple courses ranging from beginner to advanced.

Beginners to R should start with R Programming: The basics of programming in R. You’ll familiarize yourself with RStudio, manipulate matrices and data frames with inbuilt functions, and learn to deal with missing values or datetime values and so on. By the end of this course, you’ll be ready to move on to intermediate courses like Regression Models and Getting and Cleaning Data.

Intermediate R programmers would enjoy Getting and Cleaning Data as it touches upon the icky but necessary stuff when it comes to doing data science — cleaning dirty data. You’ll work with three tidyverse libraries, dplyr, tidyr, and lubridate to help you in this process. By the end of the course, you’ll be able to manipulate, group, and chain data with dplyr, tidy data with tidyr, and deal with datetimes with lubridate.

Advanced R programmers might find Statistical Inference useful. Closely mirroring the Johns Hopkins’s Statistical Inference, it introduces the student to basic concepts of statistical inference including probability, hypothesis testing, confidence intervals and p-values. The course concludes with an initiation to topics of particular relevance to big data, issues of multiple testing and resampling.

A few other notable swirl courses you might be interested in are: Regression Models, Advanced R Programming, and Data Analysis.

How You’ll Learn

A key feature of the swirl learning environment is that it is totally self-paced. There are a total of ten swirl courses, with each course containing a number of lessons usually between 10—20 mins long. You’ll learn primarily from hands-on coding and immediate feedback rather than watching video tutorials.

Institution swirl
Level All levels
Workload Self-paced
Certificate None

Fun Facts

If you’re interested in this course, you can find more information about the course and how to enroll here.

8. Introduction to Business Analytics with R (University of Illinois at Urbana-Champaign)

If you’re an aspiring business analyst, this free-to-audit course gets you started with business analytics fundamentals and R programming!

Offered by the University of Illinois at Urbana-Champaign, Introduction to Business Analytics with R recognizes that data influences every business decision from R&D to marketing. Businesses need business analysts capable of transforming data into actionable insights and communicating those insights.

By the end of this course, you’ll have a solid foundation in R and RStudio such that you can    solve business problems with data automation, transform data into actionable insights, and communicate your findings with others.

What You’ll Learn

The course begins with a quick overview of the interplay between data analytics and business principles. Then, you’ll learn how to obtain actionable insights through the FACT framework and get to know the RStudio by reading a dataset into the programming environment.

Next, you’ll get to the building blocks of R — tidy dataframes. Using spreadsheet software and its handy functions, you’ll excavate the wealth of insights embedded in dataframes. You’ll then share your insights with notebooks and dashboards — no copy-pasting needed!

Most raw data you receive will be really untidy. To clean the data, you’ll need the Tidyverse, a collection of popular R packages used to investigate all kinds of data, like currencies, time, or numbers. The course teaches you in-depth how to utilize these tools to your advantage. You’ll code many data preprocessing tasks, like aggregation, handling missing values, stacks, and pivots, to help you prepare your data for analysis and visualization.

How You’ll Learn

This course is 4 weeks long with 16 hours worth of material. The course provides several videos and readings for you to learn from, with practical lessons designed to give you hands-on practice preparing data. Only accessible to paying learners, each module comes with a graded quiz to test your understanding. Additionally, there’ll be a peer-review assignment.

Institution University of Illinois at Urbana-Champaign
Provider Coursera
Instructor Ronald Guymon and Ashish Khandelwal
Level Beginner
Workload 16 hours total
Enrollments 12K
Rating 4.6 / 5.0 (126)
Certificate Paid

Fun Facts

  • The course has 566 bookmarks on Class Central.
  • Introduction to Business Analytics with R is the first part of the Business Analytics Specialization. It focuses on widely used strategies, methods, tools, and applications in business.

If you’re interested in this course, you can find more information about the course and how to enroll here.

9. Introduction to Probability and Data with R (Duke University)

How does a doctor decide that a new drug is more effective than an existing drug? How confident should a politician be in their latest poll numbers? How does Netflix make personalized movie recommendations?

These are the questions Introduction to Probability and Data with R seeks to answer with statistics.

This free-to-audit course by Duke University aims to help you think critically about data and Frequentist and Bayesian statistics with R. Note that the emphasis in this course is on statistics rather than R, so take this course if you want an introduction to statistical methods and probability.

By the end of the course, you’ll have an understanding of R programming in conjunction with statistical and data analytical knowledge like sampling methods, exploratory data analysis techniques, and statistical inference.

What You’ll Learn

The course begins with a cursory introduction to statistics, including types of data (numerical and categorical) and various sampling methods. Choosing the suitable sampling method for your research is essential, as each method has its own biases. Understanding the type of data you’re working with is even more so! Finally, you’ll practice exploring data in R and coming up with your own inference of the data.

The course then moves on to probability. You’ll learn a bit of the math behind probability methods, including conditional probability and the Bayes’ theorem to help you predict more likely events. Lastly, the course ends with a discussion of two probability distributions: the normal and binomial distribution. Knowing how your data is distributed allows you to make more accurate assessments of the nature of the data you’re working with.

How You’ll Learn

This course is 4 weeks long (an additional 4 weeks are programming exercises), with 14 hours worth of material. You’ll learn from the video lectures supplied by the course as well as the plenty of supplementary material to strengthen your knowledge of statistical concepts and R programming. For paying learners, each week ends with a quiz to test your statistical knowledge. Additionally, there are three labs in total to practice your R programming skills.

Institution Duke University
Provider Coursera
Instructor Mine Çetinkaya-Rundel
Level Beginner
Workload 14 hours total
Enrollments 244K
Rating 4.7 / 5.0 (5.1K)
Certificate Paid

Fun Facts

If you’re interested in this course, you can find more information about the course and how to enroll here.

10. Data Science and Machine Learning Bootcamp with R (Udemy)

For those interested in doing data science and machine learning (which, by the way, we have guides on) with R, this paid course teaches you just that!

Made for complete beginners and established developers, Data Science and Machine Learning Bootcamp with R starts you off with the basics of R programming. You’ll slowly build your knowledge of machine learning through hands-on R programming exercises up to the point where you’ll be able to do your own data science project.

What You’ll Learn

The course focuses on three main topics: R programming, machine learning, and data visualization.

In most R programs, you’ll work with data frames based on vectors and matrices. Hence, any good R programmer must be able to wrap their heads around these fundamental building blocks. You’ll learn how to read data into RStudio, the coding environment for R. By going through the basics of programming, diving into the valuable features of R, along with its popular tools and packages, you’ll have enough to move to the next section of the course comfortably.

Data visualization helps data scientists share their findings with the less technically-inclined folks. R comes with a swiss-army knife of data visualization tools and libraries like ggplot2 and Plotly to help you convey your message. In fact, you’ll create interactive visualizations with Plotly to truly bring your insights to the next level.

For machine learning, you’ll cover a variety of machine learning algorithms, from the straightforward (linear regression) to the advanced (random forests and neural networks). Not only that, you’ll work through many practical projects like Twitter data mining and word clouds so that you don’t have a screwdriver without screws (that is, knowledge without application).

How You’ll Learn

The course consists of 35 sections with 18 hours worth of material. It contains a boatload of videos as well as exercises in the form of interactive notebooks (with solutions) for you to practice coding. Additionally, you’ll complete multiple machine learning projects throughout the course.

Provider Udemy
Instructor Jose Portilla
Level Beginner
Workload 18 hours total
Enrollments 80K
Rating 4.7 / 5.0 (15K)
Certificate Paid

Fun Facts

  • Jose Portilla is a popular online instructor with over 2.7 million students and 40+ courses taught on Udemy.

If you’re interested in this course, you can find more information about the course and how to enroll here.

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Elham Nazif

Part-time content writer, full-time computer science student.

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