| Lecture | Topic |
|---|---|
| Lecture 01 — Introduction | Our question, our data, and a first look at what we will do |
| Lecture 02 — Introduction to R and Positron | R, Positron and Projects |
| Lecture 03 — GGPlot I | Grouped summaries, facets, and the pipe into ggplot |
| Lecture 04 — GGPlot II | New data, safe zooming, distributions, themes, and patchwork |
| Lecture 05 — Wrangling Data | filter, select, mutate, arrange — and chaining them into a pipeline |
| Lecture 06 — Summary Statistics | Mean, median, spread, and describing groups the tidy way |
| Lecture 07 — From Script to Report | One Quarto file: read the data, plot it, describe it, test it — Word, PDF, and slides |
| Lecture 08 — Flex / Catch-up | Open lab: revisit whatever isn’t sticking yet |
| Lecture 09 — T-Tests I: Setting Up the Test | Hypotheses and checking assumptions before we test |
| Lecture 10 — T-Tests II: Two-Sample vs. Paired | Reading the output, the power of pairing, and rank-based tests |
| Lecture 11 — Linear Regression | Using one measurement to predict another (Whitlock & Schluter Ch. 17) |
| Lecture 12 — Real Climate Data in R | How fast is your city warming? (regression on time) |
| Lecture 13 — Your Final Project Starts Now | Finding real data, asking a real question, and building toward a real answer |
| Lecture 15 — Wide, Long, and Wild: Pivoting Real Data | Reshaping Lake Superior ice cover data with pivot_longer() and pivot_wider() |
| Lecture 16 — Joins: Combining Two Tables | Keys, mutating joins, and filtering joins on a fisher reintroduction dataset |
| Lecture — Mapping: Bigfoot, Moose, and Live Maps | Spatial data and sf, real species records from GBIF, and an interactive leaflet map |
| Lecture — Mapping Redux: Ranges That Move | Time on a map, convex hulls, hex grids, and an interactive map with ggplotly |
| Lecture — One-Factor ANOVA: Start to Finish | Seven sites, one question: do these possums differ in size? |
| Lecture — One-Factor ANOVA II: Wētā, and Correcting for Size | The same eight steps, and the moment they give you the wrong answer |
| Lecture — PCA I: Many Measurements, Few Axes | Six numbers per Pokémon, one picture, and what ‘variance explained’ means |
| Lecture — PCA II: Loadings, Scores, and the Biplot | What PC2 means, and finally drawing the picture |
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