In annecori/mRIIDSprocessData: Epidemiological forcasting using real-time data

library(dplyr)
library(ggplot2)
library(sf)
library(purrr)

drc_nghbrs <- c("CAF", "RWA", "SSD", "ZMB", "GAB",
                "AGO", "BDI", "COG", "TZA", "UGA")

shp_dirs <- paste0("data/Geography/gadm36_", drc_nghbrs, "_shp")
names(shp_dirs) <- drc_nghbrs
shp_adm0 <- paste0("gadm36_", drc_nghbrs, "_0.shp")
shp_adm1 <- paste0("gadm36_", drc_nghbrs, "_1.shp")
shp_adm2 <- paste0("gadm36_", drc_nghbrs, "_2.shp")

mean_areas <- 
purrr::map_dfr(list(adm0 = shp_adm0, adm1 = shp_adm1, adm2 = shp_adm2),
               function(x) {
                   purrr::map2_dfr(shp_dirs, x, ~ here::here(.x, .y) %>%
                                        st_read %>%
                                        st_area %>%
                                        mean)}, .id = "level")

Re-write it in appropriate format.

mean_areas <- tidyr::gather(mean_areas, ADM0, mean_area, -level) %>%
    tidyr::spread(level, mean_area) %>%
    rename("area_adm0" = "adm0",
           "mean_area_adm1" = "adm1",
           "mean_area_adm2" = "adm2")

readr::write_csv(mean_areas, path = here::here("data/Geography",
                                               "drc_neighbors_areas.csv"))