New Year Launchpad: Lift Off Your Data Career with 57% Off Lifetime Plan.
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.
1M+ learners
Hands-on projects
No credit card required
4.8
Recommended Path for Beginners
Start your r journey with these expert-curated learning paths.
Data Analyst (R)
Analyze, clean, and visualize data using R and SQL to perform end-to-end statistical analysis and communicate insights effectively.
23 courses
18 projects 91.8k
Probability and Statistics with R
Apply probability, sampling, and hypothesis testing in R to make confident, data-driven decisions with practice projects.
5 courses
5 projects 6.2k
Our learners work at
Explore All R Courses
Data Analyst (R)
Analyze, clean, and visualize data using R and SQL to perform end-to-end statistical analysis and communicate insights effectively.
23 courses
18 projects
85 hours
91.8k
Probability and Statistics with R
Apply probability, sampling, and hypothesis testing in R to make confident, data-driven decisions with practice projects.
5 courses
5 projects
11 hours
6.2k
R Basics for Data Analysis
Build foundational R programming skills to analyze data, automate workflows, and support exploratory data analysis.
4 courses
3 projects
15 hours
51.9k
Data Visualization with R
Create clear, compelling visual stories in R using ggplot2 while exploring distributions, comparisons, and relationships.
1 course
1 projects
5 hours
8.4k
APIs and Web Scraping with R
Collect data from APIs and websites in R, clean it, and combine sources for analysis projects.
2 courses
2 projects
5 hours
3.1k
Free
Querying Databases with SQL and R
Query SQLite databases from R by executing SQL statements to retrieve, filter, and analyze subsets of data for practical analysis tasks.
1 hours
2k
Linear Regression Modeling in R
Apply linear regression in R to build, interpret, and evaluate predictive models, understanding when linear assumptions hold and fail.
3 hours
3.2k
Introduction to Machine Learning in R
Implement core machine learning workflows in R using k-nearest neighbors, error metrics, and cross-validation to build reliable models.
2 hours
2.4k
Introduction to Interactive Web Applications in Shiny
Transform notebooks into interactive Shiny dashboards that let non-technical users explore data through clean interfaces.
2 hours
1.5k
Hypothesis Testing in R
Use hypothesis testing in R to assess real-world data with chi-square tests, probability distributions, and statistical significance.
1 hours
2.1k
Introduction to Data Cleaning in R
Develop practical data cleaning skills in R by reshaping tables, fixing missing values, and preparing relational data for analysis.
7 hours
6.2k
Advanced Data Cleaning in R
Work with regular expressions in R to precisely match, clean, and transform text data as part of advanced, real-world data cleaning workflows.
6 hours
2.8k
Introduction to APIs in R
Acquire data from external APIs in R, handling JSON responses, authentication, and status codes to support real-world analysis workflows.
3 hours
2.4k
Introduction to Web Scraping in R
Collect structured data from websites by scraping and parsing web pages in R to support downstream analysis and insights.
3 hours
1.5k
Introduction to Statistics in R
Apply core statistical sampling techniques in R—including random, stratified, and cluster sampling—using hands-on analysis scenarios.
5 hours
5.1k
Intermediate Statistics in R
Apply measures of central tendency and variability in R, using means, medians, standard deviation, and z-scores to compare data.
3 hours
2k
Introduction to Probability in R
Compare theoretical and experimental probability in R while calculating event likelihoods using permutations, combinations, and real examples.
2 hours
1.8k
Conditional Probability in R
Apply conditional probability and Bayes’ theorem in R to model dependent events, reason under uncertainty, and build practical Naive Bayes classifiers.
2 hours
1.5k
Learn R Courses by Building Projects
Apply your skills to real-world scenarios with these guided projects
Project
Free
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
Project
Free
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
Project
Free
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
Project
Free
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
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.