R Courses
These R courses teach the tidyverse, data cleaning, and ggplot2 for graphics through beginner-friendly projects. You’ll analyze real-world datasets to run statistical tests and create publication-quality charts.
R Courses
These R courses teach the tidyverse, data cleaning, and ggplot2 for graphics through beginner-friendly projects. You’ll analyze real-world datasets to run statistical tests and create publication-quality charts.
Showing 25 of 25 courses
- 23 courses 18 projects 88 hours 93K+
Data Analyst in R
In this path, you'll learn the fundamentals of R and build upon them with more advanced skills. You'll learn how to use RStudio, applications and tools, tidyverse, DataFrames, tibbles, operators, expressions, and much more - as well as data visualization, graphs, plots, and charts. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner R SQLStart career path → - 4 courses 3 projects 15 hours 52K+
R Basics for Data Analysis
In this path, you'll explore the basics of R and work through the entire data analysis workflow , learn how to use packages and why they are essential in any data analysis process, and how to repeat code efficiently with iterations. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Beginner R Programming FundamentalsStart skill path → - 1 courses 1 projects 4 hours 8.5K+
Data Visualization with R
In this path, you will gain experience in manipulating, comparing, and presenting compelling and actionable data and you'll discover the best methods for visualizing data using line graphs, histograms, bar charts, scatter plots, and more. You'll learn how to use R programming and ggplot2 to create meaningful data visualizations. Ggplot2, which is a part of tidyverse, is an R package for data visualization. It's one of the most versatile and easy-to-use tools for creating elegant graphics using R, and it's the main focus of this path. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Intermediate R Data VisualizationStart skill path → - 5 courses 5 projects 10 hours 6.4K+
Probability and Statistics with R
In this path, you'll learn the foundations of statistics such as sampling, working with variables, and understanding frequency distribution tables and the fundamentals of probability and how to use them for analysis. You'll also learn how to create and test hypotheses with significance testing, and how to make forecasts based on patterns and trends with real-world data. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Intermediate R ProbabilityStart skill path → - 2 courses 2 projects 4 hours 3.1K+
APIs and Web Scraping with R
In this path, you'll learn how to use application program interfaces (APIs) and powerful web scraping tools to create truly unique and targeted datasets. You'll also learn how to automate the process of putting unstructured data into an organized and understandable dataset. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser. You'll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.
Intermediate R APIsStart skill path → - 5 lessons 3 hours 48K+
Introduction to Data Analysis in R
This interactive R course for beginners teaches fundamental data analysis skills and helps you begin your journey to become a successful data professional. In this course, you'll learn to use basic arithmetic; write code using R syntax; and work with different data types, values, and vectors in the data analysis workflow, including data exploration, manipulation, analysis, and visualization with R. Best of all, you'll learn by doing - you'll write code and get feedback directly in the browser.
Beginner R Data ScienceStart course → - 5 lessons 6 hours 15K+
Data Structures in R
In this course, you'll learn the most common basic data structures you'll encounter in the data analysis workflow. You'll learn how to work with vectors, matrices, lists, and DataFrames. You'll be able to index vectors and matrices to extract specific elements and apply functions to vectors and matrices to perform calculations. You'll also learn how to create and index lists to extract objects. Finally, you'll learn how to define a tibble, filter and subset the data in a tibble and employ piping with tibbles. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. The course will wrap up with a guided project that will enable you to perform a data analysis by investigating COVID-19 Virus Trends
Beginner R Data StructuresStart course → - 4 lessons 3 hours 13K+
Control Flow, Iteration, and Functions in R
In this course, you'll begin by learning control flow (including if-else statements) and conditionals. You'll then move into learning about functions and functional programming. You'll also learn about iteration, how to write for loops and while loops, and when and why you might choose to write a for loop instead of employing vectorization in R. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll work on the first part of a guided project that will enable you to apply control flow loops and functions to create a reusable data workflow.
Beginner R Data ScienceStart course → - 5 lessons 4 hours 8.5K+
Introduction to Data Visualization in R
In data science, it's not enough to be able to analyze data. You must also be able to create compelling visualizations to share your insights and help people understand your findings. In this course, you'll learn the ggplot2 package, a powerful data visualization library for R. You'll also learn how to add and work with multiple plots in your code to show different visualizations. By the end of this course, you'll be able to create visualizations such as line charts, bar plots, scatter plots, histograms, and box plots to help others understand your data. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, we'll conclude with a guided data science project that uses real-world data about forest fires in Portugal.
Intermediate R ggplot2Start course → - 4 lessons 3 hours 6.7K+
Specialized Data Processing in R
First, you'll learn techniques like string indexing and concatenation to interpret, process, and analyze text data. Next, you'll leverage the lubridate package to overcome the unique difficulties of working with dates and times in R. Finally, you'll learn how to vectorize a function using the map function. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll work on the second part of a guided project that will enable you to apply control flow loops and functions to create a reusable data workflow.
Beginner R Data ProcessingStart course → - 6 lessons 6 hours 6.2K+
Introduction to Data Cleaning in R
Data cleaning is a necessary skill for anyone who wants to work in a data-related field. You'll start this course by learning how to identify data cleaning needs prior to analysis, how to use functionals for data cleaning, how to practice string manipulation, how to work with relational data, and how to reshape data using tools from the tidyverse. You'll create correlation matrices to identify trends in your data, and then you'll then learn how to deal with missing values in your dataset. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll work on a guided project to analyze parents', students', and teachers' perceptions of NYC schools. You'll learn to work with survey data - specifically how to import, simplify, and reshape the data. You'll also learn about R Notebooks and how you can use them to showcase your work.
Intermediate R Data CleaningStart course → - 7 lessons 5 hours 5.2K+
Introduction to Statistics in R
In this course, you'll learn several techniques for sampling data, such as random sampling and cluster sampling. You'll also learn about discrete variables and random variables in the context of frequency distributions, and the different types of charts and graphs you might use to visualize frequency distributions. As you learn about these concepts and how to use them for more robust data analysis, you'll be working with a dataset about basketball players in the WNBA (Women's National Basketball Association) that contains general information about players, along with their metrics for the 2016-2017 season. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a portfolio project that asks you to investigate Fandango Movie Ratings to determine if Fandango is inflating movie ratings on its site. This is an opportunity to learn to identify and overcome common setbacks in practical data analysis.
Intermediate R StatisticsStart course → - 5 lessons 4 hours 3.7K+
Linear Regression Modeling in Python
Linear regression shows us how we can use data to predict the value of an outcome. This course covers the structure of a linear regression model, how to interpret it, how to determine if a model is appropriate, and how to use the model to predict values of new data. In this course, you'll learn to create single and multiple linear regressions, identify the different types of predictors, and identify a cost function for linear regression. You'll also learn how to interpret regression parameters, how to check linear regression fit, and how to apply linear regression models. You will use tools such as scikit-learn, statsmodels, pandas, NumPy and matplotlib. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll combine your new skills to complete a project to predict insurance costs.
Intermediate Python Linear RegressionStart course → - 6 lessons 2 hours 3.2K+
Linear Regression Modeling in R
In this course, you'll learn how and when to use linear regression models to make predictions. You'll learn how to build linear regression models, how to interpret their output, and how to assess model accuracy. You'll also explore the limitations of linear regression models when data isn't linear. Finally, you'll learn to use programming tools to fit and visualize many linear regression models at once. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a project to practice your skills with a subset of condominium sales data from all five boroughs of New York City.
Intermediate R Linear RegressionStart course → - 4 lessons 6 hours 2.9K+
Advanced Data Cleaning in R
You'll learn about regular expressions (regex), a powerful tool that allows you to match and manipulate text data with precision. You'll also learn to work with JSON data, a common format you'll encounter when pulling data from web APIs. Then you'll dive into map and anonymous functions, two intermediate-to-advanced concepts in R that can speed up your data cleaning. You'll also learn how to resolve missing values in your data, a critical part of almost every data analysis project. Rather than dropping rows or columns, which reduces the amount of data you have to work with, you'll learn statistical techniques to impute missing data, and you'll also learn how to insert data from outside sources. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate R Data CleaningStart course → - 5 lessons 3 hours 2.9K+
Logistic Regression Modeling in Python
Logistic regression and linear regression are very similar, but the two have slightly different objectives. In linear regression, we try to predict losses in insurance claims. In logistic regression, we're trying to predict categorical outcomes, otherwise known as classification. In other terms, logistic regression is the classification-based equivalent of linear regression. In this course, you'll learn the logistic regression method. You'll learn how to interpret regression parameters, how to evaluate logistic regression models, and how to apply them. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll combine your skills to complete a project to classify heart diseases.
Intermediate Python Logistic RegressionStart course → - 4 lessons 2 hours 2.4K+
Introduction to APIs in R
Although there are many datasets available in convenient formats like CSVs, there is also a large amount of data that is accessible only via an API. If you want to analyze streaming data from Twitter, for example, or dig into posting trends on Reddit, you need to get that data from the relevant APIs. In this course, you'll learn the fundamentals of APIs, such as connecting to an open API and interpreting different status codes. You'll also learn how to work with the JSON data format in R (since most data from APIs will be in JSON format). Then, you'll tackle more complex tasks like authenticating with private APIs and submitting more complex requests. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. The course concludes with a guided project that asks you to assemble a full data analysis project using an API to get data on New York's solar energy resources.
Intermediate R APIsStart course → - 6 lessons 1 hours 2.4K+
Introduction to Machine Learning in R
In this course, you'll learn key concepts such as KNN Algorithms (K-Nearest Neighbors), error metrics including the Mean Squared Error and the Root Mean Squared Error and caret, a machine learning library for the R programming language. You'll learn how to optimize machine learning algorithms for better accuracy and performance of trained models using hyperparameter optimization. You'll then dig into performing rigorous model testing using k-fold cross-validation. As you learn these new skills, you'll be working with AirBnB prices data from Washington D.C. to predict the optimal price for generating profit from a Washington D.C. home rental. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a project to predict car prices using the K-Nearest Neighbors algorithm. This project is a chance for you to combine the skills you learned in this course and practice a machine learning workflow.
Advanced R Machine LearningStart course → - 5 lessons 1 hours 2.1K+
Hypothesis Testing in R
In this course, you'll learn about single and multi-category chi-square tests, degrees of freedom, hypothesis testing, and different statistical distributions. To learn about hypothesis testing and statistical significance, you'll work hands-on with multiple datasets on weight loss data - are patients losing weight due to pure luck, or is it a diet pill? You'll run the numbers and find out! Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a guided project that asks you to work with data from the American TV show Jeopardy. You'll analyze text and search for winning strategies. It's a chance for you to combine the skills you learned in this course, and to showcase a fascinating project in your portfolio.
Intermediate R StatisticsStart course → - 6 lessons 2 hours 2.1K+
Intermediate Statistics in R
In this course, you'll learn how to summarize distributions using the mean, the median, and the mode - as well as when to use them. You'll learn which statistic gives you the most information about a distribution so you know not only how to apply them but also why you should. You'll then learn to measure variability using variance or standard deviation, and how to locate and compare values using z-scores. We'll then explore range, mean absolute deviation, variance, and standard deviation. You'll also learn about z-Scores and how to use them to compare values across any distribution. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a guided project in which you'll find the best markets for advertising an e-learning platform that combines your data science programming skills and the statistical skills you've learned in this course.
Intermediate R StatisticsStart course → -
Free
1 lessons 0 hours 2.0K+
Querying Databases with SQL and R
Immerse yourself in the dynamic world of R and SQL in our transformative course. Connect and query from SQLite databases using R, turning raw data into actionable insights. The best part? It's all hands-on. You'll implement your newly acquired skills in real-world scenarios and receive interactive feedback. By the end of this course, you will have a unique skill set that puts you ahead in the rapidly evolving data industry.
Intermediate R SQLStart course → - 5 lessons 1 hours 1.8K+
Introduction to Probability in R
In this course, you'll learn the difference between theoretical and experimental probability, and you'll calculate the probabilities for a variety of different events. You'll also learn the number of permutations and combinations possible in experiment outcomes. You'll apply this knowledge in a guided project to build the logic for a mobile app that estimates lottery odds. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate R ProbabilityStart course → - 4 lessons 2 hours 1.6K+
Introduction to Web Scraping in R
Although there are many datasets available in convenient formats, there's also a lot of data that's more difficult to access, like a table on a web page. To get this data, we'll need to use web scraping. In R, we can do that with the rvest scraping package. In this course, you'll learn about web page structure, including the basics of HTML and CSS. You'll also learn how to get the code from a page into your R workflow for further parsing and cleaning. Then, you'll dig deeper into scraping, learning to use the CSS Selector to get precisely the data you want. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you'll complete a guided project that asks you to use web scraping to analyze movie ratings.
Intermediate R Web ScrapingStart course → - 5 lessons 1 hours 1.5K+
Conditional Probability in R
In this course, we'll build on the fundamentals of probabilities, including the theoretical and empirical probabilities, the probability rules ( the addition rule and the multiplication rule), and the counting techniques (the rule of product, permutations, and combinations). You'll learn to assign probabilities to events based on certain conditions by using conditional probability rules, how to assign probabilities to events based on whether they are in a relationship of statistical independence with other events, and how to assign probabilities to events based on prior knowledge by using Bayes's theorem. You'll also learn to create a spam filter for SMS messages using the multinomial Naive Bayes algorithm. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser.
Intermediate R ProbabilityStart course → - 5 lessons 1 hours 1.5K+
Introduction to Interactive Web Applications in Shiny
While notebooks are great for sharing data work, they're not very user-friendly for viewers without programming skills. With the popular Shiny package, we can showcase our data work in a more attractive and accessible way by building interactive, data-based dashboards and projects for the web! In this course, you'll learn the fundamentals of working with Shiny. Then you'll explore more complex topics such as learning about programming server logic for Shiny and programming the UI for your interactive dashboards. You'll also learn various ways to customize the design of elements you build in Shiny. Finally, you'll learn how to get your apps online and how to share them. Best of all, you'll learn by doing - you'll practice and get feedback directly in the browser. At the end of the course, you will complete a guided project that asks you to build a portfolio website for yourself using Shiny!
Intermediate R ShinyStart course →
Related resources on R
- Article
R vs Python for Data Analysis — An Objective Comparison
R vs Python — Opinions vs Facts There are dozens articles out there that compare R vs. Python from a subjective, opinion-based perspective.
19 min read View article → - Article
Learn R the Right Way in 5 Steps
Starting to learn a new programming language can feel overwhelming. When I first encountered R, I was a complete newcomer to coding with a full-time job.
21 min read View article → - Article
12 Data Analyst Skills That Will Get You Hired in 2026
If you want to land a data analyst role in 2026, you need more than just technical knowledge. Employers are looking for a mix of technical, analytical, and communication skills that can turn raw data into actionable insights.
8 min read View article →
Learn R by building projects
-
Free
Project
Predicting Condominium Sale Prices
For this project, you’ll assume the role of a data analyst to predict condominium sale prices in New York City boroughs based on property size, using linear regression modeling in R.
8 steps Start project → -
Free
Project
NYC Schools Perceptions
For this project, you’ll assume the role of a data analyst exploring survey data on parent, teacher, and student perceptions of NYC school quality. You’ll clean, analyze, and visualize the data using R and showcase your work in an R Notebook.
3 steps Start project → -
Free
Project
Investigative Statistical Analysis – Analyzing Accuracy in Data Presentation
For this project, you’ll be a data journalist analyzing Fandango’s movie ratings to determine if there was any change after a 2015 analysis found evidence of bias. You’ll use R and statistics skills to compare movie ratings data from 2015 and 2016.
8 steps Start project → -
Free
Project
Investigating COVID-19 Virus Trends
For this project, you’ll act as a data scientist analyzing a real-world COVID-19 dataset using R. You’ll leverage packages like dplyr and ggplot2 to identify the most affected countries and quantify testing efforts.
11 steps Start project →
Frequently Asked Questions
How do you choose the right R course for your goals?
Choose an R programming course based on how you plan to use R. If your goal is academic research, statistical analysis, or data science work, prioritize courses that focus on statistical computing and real-world data workflows.
Look for a course that teaches modern R programming using the Tidyverse, core R functions, and reproducible tools like R Markdown. Courses that include real exercises and projects help you build practical R skills instead of just learning syntax.
What is R?
R is a programming language designed for statistical computing, data analysis, and data visualization. It is widely used by statisticians, data analysts, and data scientists to perform analysis and build statistical models.
R includes a large ecosystem of packages, including tools like ggplot2 for creating clear and informative graphics. Dataquest teaches practical R programming skills so you can analyze data and apply R to real projects.
Is R hard to learn?
Learning R is not inherently hard, but its syntax differs from that of many other programming languages, which can feel unfamiliar at first. With guided, hands-on practice, beginners can grasp R quickly. Dataquest’s step-by-step interactive lessons help you learn to use it effectively for data analysis.
What are the best R courses online?
The best R courses get you coding right away while teaching practical data analysis and visualization skills. Look for courses that use real datasets, emphasize hands-on projects, and teach workflows that mirror how data analysts work in the real world. Dataquest stands out by providing a fully configured R environment in your browser, allowing you to practice analysis and visualization immediately without any complex software setup.
Are R skills still in demand?
Yes. R skills are particularly valued in academia, healthcare, finance, and research. While Python is more broadly used, R remains the gold standard for advanced statistical analysis. Dataquest helps you build these skills and prepares you for specialized roles in these fields.
What jobs can you get as an R programmer?
With R skills, you can pursue roles such as:
- Data Analyst
- Statistician
- Data Scientist
- Quantitative Analyst
- Research Scientist
Dataquest helps you build the specific R skills needed for research-heavy data careers.
Which programming language should I learn first: R or Python?
If your focus is machine learning and general coding, learn Python first. If your focus is statistics-heavy work and academic research, learn R first. Dataquest offers paths for both, and many professionals eventually learn both.
Can you learn R without a programming background?
Yes, you can learn R even if you’ve never programmed before. Dataquest’s beginner-friendly R courses teach programming fundamentals alongside statistics and data analysis, so you build practical skills step by step while working with real-world datasets.
What tools are commonly used with R?
Common tools for R include RStudio, the Tidyverse collection of packages (like dplyr and ggplot2), and Shiny for interactive apps. Dataquest teaches these modern tools so you gain the practical, up-to-date skills used in data roles.
What is the best way to learn R fast?
The fastest way to learn R is by working with real data. Dataquest’s project-based curriculum lets you clean, analyze, and visualize interesting datasets from day one, helping you retain skills more effectively than learning theory alone.
Can you learn R in 3 months?
Yes, most students can learn core R concepts and become comfortable with data analysis in about three months with consistent practice.
To reach proficiency and work on real analysis tasks, expect closer to three to six months. Dataquest’s guided paths help students stay focused on key concepts and build practical R skills needed for job readiness.
How much do R courses cost?
Costs vary widely, from free introductory courses to monthly subscriptions on learning platforms to university programs costing thousands.
Dataquest offers an affordable subscription with full access to all data science, analytics, engineering, and AI courses. It also includes free lessons and a 14-day money-back guarantee, so you can start learning risk-free.
Will you get a certificate, and does it help you stand out?
Yes, you earn a certificate for each online course you complete, including R specialization courses.
However, certificates alone rarely help you stand out. A portfolio of R visualizations, data analysis projects, and statistical analysis work shows employers how you apply R skills in real scenarios, which is far more valuable when applying for jobs.
Join 1M+ data learners on Dataquest.
- 1
Create a free account
- 2
Choose a learning path
- 3
Complete exercises and projects
- 4
Advance your career