Grow your data science skills at posit::conf(2024)

August 12th-14th in Seattle

Package sf (simple feature) and ggplot2::geom_sf have caused a fast uptake of tidy spatial data analysis by data scientists. Important spatial data science challenges are not handled by them, including raster and vector data cubes (e.g. socio-economic time series, satellite imagery, weather forecast or climate predictions data), and out-of-memory datasets. Powerful methods to analyse such datasets have been developed in packages stars (spatiotemporal tidy arrays) and tidync (tidy analysis of NetCDF files). This talk discusses how the simple feature and tidy data frameworks are extended to handle these challenging data types, and shows how R can be used for out-of-memory spatial and spatiotemporal datasets using tidy concepts.

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