Week 1: A Brief History of Data Visualisation and R Basics.
Week 2: Principle of Statistical Graphs and ggplot Basics.
Week 3: Univariate Plots. Graphs for categorical data and numerical data.
Week 4: Bivariate Plots. Stacked bar charts, cluster bar charts, Mosaic plots, line plots, and violin plots.
Week 5: Multivariate Plots. Multivariate visualisation with grouping, faceting and clustering.
Week 6: Maps. Dot density maps, Choropleth maps, and map-making tools in R: ggplot with sf (simple feature).
Week 7: Statistical Model (Part 1). Scatterplot matrices, correlation plots, linear model and visualisation of regression and diagnostic plots.
Week 8: Statistical Model (Part 2). Residual plots, spline, generalised linear models, and tidy model outputs with broom package.
Week 9: Time-dependent Graphs. Dumbbell graphs, slope graphs, stacked area charts, and visualising time series with dygraphs package.
Week 10: More Graphs. Bubble plots, Waffle charts, flow diagrams word clouds.
Week 11: Interactive Graphs. Basic interactive plot with plotly, interactive maps with leaflet, diagrams with networkD3.
Week 12: Best Practices. Labels, titles, the principles of choosing colours with RColorBrewer, and control the overall appearance of plots.