Wrangling spatio-temporal data with R
This tutorial is designed to get you up and running with multivariate spatio-temporal data analysis and visualisation in R quickly. It will cover importing and combining spatial and temporal data, filtering on space and time, aggregating on space and time, making basic maps and creating interactive plots.
H. Sherry Zhang is a third year PhD student in Econometrics and Business Statistics at Monash University. Her research focuses on multivariate spatiotemporal data wrangling and visualisation tools. She will be assisted by Professor Di Cook, who is well-known for her work in data visualisation and teaching of tutorials.
- Session 1: Importing various spatio-temporal data formats
- csv: spatial coordinates, time index and measured variables
- netcdf: array of large gridded data
- shape files: to provide the map background
- R data structures: sf, tsibble, cubble
- Session 2: Spatial and temporal operations, and basic mapping
- casting temporal and spatial polygon data into a cubble to make a Covid-19 map
- filtering spatio-temporal data, based on spatial coordinates (using weather station data)
- aggregating measured variables over time (using weather station data)
- Session 3: Creating interactive graphics, with linking between plots
- mouse-over tooltips with plotly
- using crosstalk to link spatial plots with time series plots
- sharing your interactive graphic