Resources
Notes, code and additional material developed to supplement my classroom teaching.
Why these resources are here
During coding demonstrations, students can lose the thread of an explanation when a local error draws their attention away from what is being discussed. Publishing the code after class means students can listen first and return to a working reference afterwards.
This library also provides additional explanations, worked examples and useful links for concepts that often need more than one pass. It is intended to supplement classroom teaching, not replace official course material.
All content is provided for educational purposes. I do my best to keep it accurate and up to date, but students should check it against their course guidance and remain responsible for meeting applicable academic-integrity requirements.
Interactive tools
Resource workbench
Purpose-built tools for exploring statistical ideas and improving reproducible work. Calculations, notes and uploaded files remain in your browser.
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Statistical simulation studio
Change sample size, confidence level, effect size and distribution; rerun seeded simulations and export the result.
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Clinical prediction laboratory
Translate diagnostic accuracy into patient counts, predictive values, likelihood ratios and net benefit.
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Statistical interpretation clinic
Practise interpreting coefficients, odds ratios, interactions and p-values with immediate feedback.
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Quarto document workshop
Edit QMD examples, inspect the guided output, run R code and download a starter project.
Offline resource mode
Save the current teaching library and interactive tools for reading without a connection. Executable R chunks may still require a connection the first time their runtime is loaded.
Available resources
My resource notebook-
Interactive maps in R with mapgl
Use real Greater Sydney SA2 polygons, ABS population data, and NSW school locations to build interactive maps for a DATA1X01 Project 2 Quarto report, from your first map to advanced controls.
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Beautiful tables in R: kable, kableExtra, gt, gtsummary and gtExtras
Choose the right R table system, format numbers and labels deliberately, build descriptive and regression tables, and export accessible publication-ready output.
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GAMSAT preparation: resources and approach
A selective guide to preparing for the GAMSAT, including official materials, Section 1 and 3 recommendations, and a detailed approach to both Section 2 tasks.
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Git and GitHub: from your first commit to a reproducible project
Learn the working tree, staging area and commit history; practise core commands safely; connect a local project to GitHub and recover from common mistakes.
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Interactive visualisation with Plotly in R
Build purposeful interactive charts in R with plot_ly() and ggplotly(), customise hover information, compose figures and publish self-contained HTML widgets.
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Linear regression: understanding the model and R output
Build, diagnose and interpret a linear regression model in R, from coefficients and confidence intervals to residuals, assumptions and adjusted predictions.
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t-tests in R: one-sample, independent and paired
Choose, run and interpret the correct t-test, understand Welch and pooled-variance methods, inspect assumptions and report estimates with confidence intervals.
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ggplot2: from basics to publication-ready figures
A practical guide to building, refining and reusing statistical graphics with ggplot2.
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Quarto documents in RStudio: a complete practical guide
Learn how a .qmd file becomes a reproducible report, from YAML and R code cells to figures, callouts, citations, themes and project-wide settings.
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Understanding confidence intervals
How to interpret a confidence interval, avoid common errors and calculate a one-sample interval in R.
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A note for students
These resources are independently prepared and are not official University of Sydney course materials. Use them alongside your course guidance and remain responsible for meeting the academic-integrity requirements that apply to your work.