Course Overview

What this R course covers, how to use it, prerequisites.

A self-contained, hands-on course covering the R language and its ecosystem: data types and structures, functions, data import and wrangling (base R and the tidyverse), visualization with ggplot2, statistics and modeling, machine learning, functional and object-oriented programming (S3, S4, R6, S7), metaprogramming and tidy evaluation, performance (data.table, Rcpp, parallelism), big data (Arrow, DuckDB, Spark), package development, reproducible reports (Quarto), Shiny apps and APIs. Every chapter has runnable examples and exercises.

Course Map

Part I — Foundations Introduction to R Installation & Tooling (R, RStudio, Positron) The Console, Scripts, Help & Packages

Variables, Assignment & Basic Data Types Missing & Special Values: NA, NULL, NaN, Inf

Part II — Data Structures, Data Handling & the Tidyverse Matrices & Arrays Data Frames & Tibbles The apply Family & Vectorized Thinking

Strings & Regular Expressions (base R & stringr) Dates & Times (base R & lubridate) Importing & Exporting Data Data Wrangling with dplyr Tidy Data with tidyr Functional Programming with purrr Base R Graphics Data Visualization with ggplot2 Environments, Scoping & Closures

Conditions: Errors, Warnings & Debugging Part III — Statistics, Modeling & Advanced Programming

Descriptive Statistics & Exploratory Data Analysis Probability Distributions & Simulation

Statistical Inference & Hypothesis Testing Linear Models & Regression

Generalized Linear, Mixed & Nonlinear Models Machine Learning with tidymodels Time Series Analysis & Forecasting

Object-Oriented Programming: S3, S4, R5, R6, S7 Metaprogramming: Expressions, NSE & Tidy Evaluation

Performance: Profiling, Vectorization, Memory Rcpp & Interfacing with C/C++ Parallel & Asynchronous Computing

Big Data: Databases (DBI), Arrow, DuckDB, Spark Part IV — Ecosystem & Real-World R

Reproducible Reports: R Markdown & Quarto Interactive Web Apps with Shiny Web APIs, Web Scraping & JSON Text Mining & NLP Spatial Data & Maps

Reproducibility & Workflow: Projects, renv, targets, Git Interoperability: Python (reticulate), SQL, Julia, Stan

Domain Ecosystems: Bioconductor, Finance, Bayesian, Survey, Psychometrics Best Practices & Style

Appendices: Cheat Sheet, Functions, Operators, Resources, Study Plan