R Tutorial

Master R from scratch with our comprehensive, interactive curriculum — 55 lessons from Beginner to Advanced, focused on real statistics and data analysis.

Learn R from scratch — vectors, data frames, dplyr, tidyr, ggplot2, statistics and linear models — free interactive lessons for data science.

👋 New to R? No experience needed.

Start with Lesson 1 and build real data-analysis skills step by step — at your own pace.

Continue where you left off

Lessons in this course

• Introduction to R — What R is, why it dominates statistics, and running your first line of code
• RStudio & Running Code — Set up RStudio, use the console and scripts, and run code interactively
• Vectors — R's core data structure — create, index, and do math on vectors
• Operators — Arithmetic, comparison, and logical operators with vectorized behaviour
• Factors — Represent categorical data with levels — essential for stats and plots
• Functions — Write reusable functions with arguments, defaults, and return values
• Control Flow — Make decisions and repeat work with if/else, for, and while
• Lists — Hold mixed types and nested data in R's flexible list structure
• Matrices — Two-dimensional numeric data and matrix operations
• Data Frames — R's spreadsheet-like table — the workhorse of data analysis
• Reading Data (CSV) — Load real datasets from CSV files into data frames
• The apply Family — Apply functions over rows, columns, and lists without loops
• dplyr Basics — Filter, select, mutate, and arrange data the tidyverse way
• tidyr & Pipes — Reshape messy data into tidy form and chain steps with the pipe
• Strings & Dates — Clean text and work with real dates and times
• ggplot2 Fundamentals — Build publication-quality charts with the grammar of graphics
• group_by & summarise — Split-apply-combine: compute grouped statistics with dplyr
• Joining Data — Combine multiple tables with inner, left, and other joins
• Statistical Functions — Means, distributions, correlation, and hypothesis tests in base R
• Data Types & Classes — numeric, character, logical and complex — plus class(), typeof() and coercion
• Subsetting & Indexing ([ ], [[ ]], $) — Positional, logical and name-based indexing for vectors and lists
• Missing Data (NA, na.rm) — Detect, drop and handle NA with is.na, na.rm and complete.cases
• Vectorization vs Loops — Write fast idiomatic R with vectorized operations, recycling and ifelse
• Regular Expressions (grepl, gsub) — Match and replace text with grepl, gsub, and regmatches
• Frequency Tables (table, prop.table) — Count and cross-tabulate categories with table and prop.table
• Error Handling (tryCatch) — Catch errors and warnings safely with tryCatch, try, and stopifnot
• Environments & Scoping — Lexical scoping, the global environment, and the <<- super-assignment
• Checkpoint: R Basics — Combine subsetting, NA handling, regex and tables in a build — then a quiz
• Tibbles (Modern Data Frames) — The tidyverse data frame — nicer printing and no surprising conversions
• Reshaping: pivot_longer & pivot_wider — Move between wide and long (tidy) data with tidyr
• Functional Programming with purrr — Iterate cleanly with map, map_dbl, the ~.x shorthand, and reduce
• Dates with lubridate — Parse, extract from and do arithmetic with dates the easy way
• ggplot2: Facets, Themes & Scales — Small multiples with facets, custom themes, and scale control
• Probability Distributions — The d/p/q/r functions, set.seed reproducibility, and sampling
• Hypothesis Testing (t.test, chisq.test) — Run and interpret t-tests, chi-squared tests, and p-values
• Generalized Linear Models (glm) — Fit logistic regression and other GLMs and interpret the coefficients
• Checkpoint: Data Analysis — Combine tibbles, pivots and a tidyverse pipeline in a build — then a quiz
• Linear Models (lm) — Fit, interpret, and predict from linear regression models
• Writing Functions & Packages — Package reusable code, document it, and share it with others
• Matrix Algebra (%*%, solve, t) — Multiply, transpose, invert, and solve linear systems with matrices
• Text Wrangling with stringr — Detect, extract, and replace text the tidyverse way with str_* functions
• ANOVA & Model Comparison — Compare group means and nested models with aov and anova()
• Time Series (ts & forecasting basics) — Build ts objects, decompose trend and seasonality, and forecast
• Fast Data with data.table — Filter, aggregate, and update huge datasets fast with dt[i, j, by]
• Interactive Apps with Shiny — Build reactive web apps with a ui, a server, and inputs/outputs
• Reports with R Markdown — Mix prose, code chunks, and results into reproducible HTML/PDF reports
• Closures & Function Factories — Return functions that remember their environment for reusable behavior
• Debugging R (browser, traceback) — Find and fix bugs with traceback, browser, debug, and tryCatch
• Checkpoint: Statistical Modeling — Fit and interpret a model on a small dataset — then a quiz
• Capstone: A Data Analysis Report — Tie it all together: import, clean, analyse, visualise, and report
• Machine Learning with caret — Train, tune and evaluate models with caret's unified train() interface
• Bayesian Statistics in R — Priors, posteriors and credible intervals with brms and rstanarm
• Cross-Validation & ROC — k-fold CV, overfitting, and the ROC curve / AUC for honest model evaluation
• Interactive Plots with plotly — Add hover, zoom and interactivity with plot_ly() and ggplotly()
• Parallel Computing in R — Use every core with parallel, foreach + doParallel, and the future framework

More for this course

• R cheat sheet — Quick syntax reference you can scan while you code