How to find region-level geometry in sf package in R

Question

I want to plot a geospatial map using sf and plotly package. However, I don't have country-level data, only region-level data (Asia, Europe, Latin America, etc.). This is the mock-up of the dataset I will be working on, I'm looking for a way to lookup the longitude, latitude, and geometry. I plan to collect all the data in Excel first, and later move them to R for analysis

I've looked for several packages/datasets e.g., rnaturalearth, maps::map, etc, but I couldn't find the correct latitude/longitude/coordinates or the geometry of the regions. Where can I find them? Thanks in advance

Answer 1

here you have some thoughs about your question:

1. As it is uncertain how you need to define each region (there are many possibilities of regional classification of countries), maybe you should start by mapping each country of the world to the region you considers it belongs to. After, you can just join the corresponding region to each country.
2. To store a geometry in excel is not a good choice (lenght of field, what crs is in use, etc). A better alternative for storing spatial objects is the .gpkg format.

Please find here how to produce a dataset as the one you mention.

I would use here the geographic regions classification of the UN. This is available on the countrycode package so basically I map each ISO-3 country code to the corresponding region. After, I group the countries by region so I can have a single geometry by region. By last, I extract the bounding boxes and append them to the dataset:

library(giscoR)
library(countrycode)
library(sf)

world <- gisco_get_countries()

# Add the subregion

world$region <- countrycode(world$ISO3_CODE,
                                origin = "iso3c",
                                destination = "un.regionsub.name")

#> Warning in countrycode_convert(sourcevar = sourcevar, origin = origin, destination = dest, : Some values were not matched unambiguously: ATA, CPT, XA, XB, XC, XD, XE, XF, XG, XH, XI, XL, XM, XN, XO, XU, XV

unique(world$region)
#>  [1] "Latin America and the Caribbean" "Polynesia"                      
#>  [3] "Western Europe"                  NA                               
#>  [5] "Southern Europe"                 "Western Asia"                   
#>  [7] "Southern Asia"                   "Sub-Saharan Africa"             
#>  [9] "Australia and New Zealand"       "Eastern Europe"                 
#> [11] "Northern America"                "South-eastern Asia"             
#> [13] "Eastern Asia"                    "Micronesia"                     
#> [15] "Northern Europe"                 "Melanesia"                      
#> [17] "Central Asia"                    "Northern Africa"


# Group regions
library(dplyr)
library(ggplot2)


subworld <- world %>% 
  group_by(region) %>%
  # Mock the data field
  summarise(data=n())

ggplot(subworld) +
  geom_sf(aes(fill=region))

# Create final table
bbox <- lapply(st_geometry(subworld), st_bbox) %>%
  bind_rows() %>% 
  mutate(across(where(is.numeric), as.double))


# Final dataset

end <- subworld %>% bind_cols(bbox) %>%
  select(region, data, xmin, xmax, ymin, ymax)

head(end)
#> Simple feature collection with 6 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -180 ymin: -59.51912 xmax: 180 ymax: 81.85799
#> Geodetic CRS:  WGS 84
#> # A tibble: 6 x 7
#>   region               data   xmin   xmax  ymin   ymax                  geometry
#>   <chr>               <int>  <dbl>  <dbl> <dbl>  <dbl>        <MULTIPOLYGON [°]>
#> 1 Australia and New ~     6 -178.  179.   -53.2 -9.21  (((143.5617 -13.77157, 1~
#> 2 Central Asia            5   46.6  87.3   35.2 55.4   (((50.3399 45.05235, 50.~
#> 3 Eastern Asia            7   73.6 146.    18.2 53.5   (((120.8954 22.336, 120.~
#> 4 Eastern Europe         10 -180   180.    41.2 81.9   (((68.7333 76.49168, 68.~
#> 5 Latin America and ~    48 -118.    3.43 -59.5 32.7   (((-64.01772 -54.74885, ~
#> 6 Melanesia               5 -180   180.   -22.7 -0.826 (((178.2331 -17.34448, 1~

^{Created on 2022-03-23 by the reprex package (v2.0.1)}