A hands-on guide for graduate students in statistics, business analytics, and operations research. From first install to a complete reproducible analysis pipeline.
Install R & RStudio, navigate the IDE, manage packages, and write your first script.
Vectors, matrices, data frames, lists, and factors. Master indexing and inspection.
Transform, reshape, and summarize data with dplyr and tidyr pipes.
Build layered graphics: scatterplots, bar charts, histograms, facets, and themes.
t-tests, ANOVA, chi-squared, correlation, and confidence intervals in R.
Fit and diagnose OLS regressions with lm(), broom, and stargazer.
Logistic and Poisson regression via glm(), odds ratios, and model comparison.
Recipes, workflows, cross-validation, tuning, and evaluation metrics.
R Markdown and Quarto for literate programming, reports, and presentations.
End-to-end analysis: import, clean, visualize, model, and render a polished report.
Hadley Wickham & Garrett Grolemund's comprehensive introduction to data science with R.