A course on exploratory data analysis, course outline for 2020:
- L1 & 2: Getting started
- R installation, basics, workflows, visualizing raw data with ggplot
- L3 & 4: Managing data frames
- dplyr verbs -- filter, arrange, select, mutate, summarise, group_by
- L5 & 6: The EDA checklist
- tabular and graphical univariate data summaries, missing values, outlier detection and treatment, asking the right questions
- L7 & 8: More managing dataframes
- reshaping, tidying, joining together data fr
- L9 & 10: Principles of good graphics (slides)
- L11 & 12: Principles of good graphics (code, practical)
- L13 & 14: Exploring spatial data
- sf, raster, tmap, geom_sf, leaflet
- L15 & 16: R Shiny, dashboards
- L17 & 18: Exploring time series data
- L19 & 20: Interactive graphics and animations
- plotly, ggplotly, gganimate
- L21 & 22: Clustering observations and variables
- cluster analysis, principal component analysis, dimension reduction
- L23 & 24: Version control
- Git, GitHub