This is a companion discussion topic for the original entry at:
Teach the Tidyverse to beginners
https://www.rstudio.com/resources/videos/teach-the-tidyverse-to-beginners/
Spatial data analysis has a long history in R. Tidy approaches to this are rather recent. I will discuss the special properties of spatialdata, the challenges of different tidy approaches, the work done so far, and the work in progress. The simple features for R package (sf, on CRAN) has been developed with support from the R Consortium. It replaces sp, rgdal and rgeos, and provides dplyr compatibility. A follow-up project, spatiotemporal tidy arrays for R (stars), is under development and aims at dense, spatiotemporal arrays such as time series of simple features, raster data, raster time series, climate model prediction data, and remote sensing imagery. Both projects will be presented, with a focus on how they augment the tidyverse.
David Robinson - Data Scientist, Stack Overflow
In May 2015 I received my PhD in Quantitative and Computational Biology from Princeton University, where I worked with Professor John Storey. My interests include statistics, data analysis, genomics, education, and programming in R.