I used #rstats to create this map of Uganda that shows distribution of various types of health services. The data are from #HumanitarianDataExchange (HDX) but I struggled with the regions (adm1) and districts (adm3) as they don't quite line up. The pharmacies form intriguing clusters in the NW and SW on the border with the DRC. As always, I wish I had heaps more time to work on this map! 🗺️ #30DayMapChallenge
@clintonsears
Superb! The soluton for boundaries of regions and districts might be: create the upper level (regions) boundaries by unioning the lower level (district) with dplyr::group_by() |> dplyr::summarise().
