Learn R
Structured lessons from the basics up — follow the path below in order, at your own pace.
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1
Level 1 — Beginner Beginner 27 lessons
Start here if you have never written a line of R before. No programming experience is required — work through these lessons in order, since...
Lessons in this module
What Is R? Why Learn R? What Can You Build With R? Installing R and RStudio Setting Up Your Development Environment Your First R Program How R Code Runs R Syntax Basics Variables and Assignment Data Types Numbers and Arithmetic Vectors Working with Vectors Strings Logical Values and Comparisons Missing Values: NA and NULL Type Conversion Conditionals: if and else switch and Multiple Choices Loops Functions Scope and Anonymous Functions Lists Factors Data Frames Packages Comments and Code Style -
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Beginner Projects Beginner 1 lesson
Build these with what Level 1 covered — vectors, strings, conditionals, loops, functions, lists, and data frames. Write each as an R script...
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3
Level 2 — Intermediate Intermediate 20 lessons
These lessons cover importing data, the tidyverse dplyr, tidyr, stringr, lubridate , ggplot2 charts, descriptive statistics, the apply famil...
Lessons in this module
Importing and Exporting Data Tibbles dplyr: filter, select, and arrange dplyr: mutate, summarise, and group_by Joining Data Tidy Data with tidyr Working with Text: stringr Dates and Times: lubridate Data Visualization with ggplot2 Customizing Charts: Facets, Scales, and Themes Descriptive Statistics Probability, Sampling, and Simulation The apply Family Functional Programming with purrr Error Handling and Defensive Code Working with JSON and APIs Files, Paths, and Projects Reproducible Reports with Quarto Testing with testthat Debugging R Code -
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Intermediate Projects Intermediate 1 lesson
Build these using data import, dplyr, tidyr, ggplot2, and descriptive statistics from Level 2. Create each as an RStudio project, and write...
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5
Level 3 — Advanced Advanced 15 lessons
These lessons cover statistical modeling, machine learning with tidymodels, environments and functional programming, object oriented R S3, S...
Lessons in this module
Linear Regression Hypothesis Testing Generalized Linear Models Machine Learning with tidymodels Environments, Scoping, and Closures Programming with dplyr: Tidy Evaluation Object-Oriented R: S3 S4 and R6 Classes Performance and Profiling Working with Databases Interactive Web Apps with Shiny Building APIs with plumber Building R Packages Reproducibility: renv, Git, and Projects R in Production: Scheduling, Docker, and CI -
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Advanced Projects Advanced 1 lesson
Build these using modeling, tidymodels, Shiny, databases, packages, and the reproducibility practices from Level 3. 1. Predictive Model with...
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7
What You Should Know Now — and What's Next 1 lesson
What You Should Know Now Core syntax: assignment, vectors, types, missing values, conditionals, loops, and functions Lists, factors, matrice...