Finding data
In rnaturalearth: World Map Data from Natural Earth

library(rnaturalearth)
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.5, PROJ 9.4.0; sf_use_s2() is TRUE

Available data

There are a lot of data that can be downloaded from Natural Earth with ne_download(). These data are divided into two main categories: physical and cultural vector data. The df_layers_physical and df_layers_cultural data frames included in the rnaturalearth packages show what layer of data can be downloaded.

Physical vector data

data(df_layers_physical)

knitr::kable(
  df_layers_physical,
  caption = "physical vector data available via ne_download()"
)

Table: physical vector data available via ne_download()

|layer | scale10| scale50| scale110| |:----------------------------------|-------:|-------:|--------:| |antarctic_ice_shelves_lines | 1| 1| 0| |antarctic_ice_shelves_polys | 1| 1| 0| |coastline | 1| 1| 1| |geographic_lines | 1| 1| 1| |geography_marine_polys | 1| 1| 1| |geography_regions_elevation_points | 1| 1| 1| |geography_regions_points | 1| 1| 1| |geography_regions_polys | 1| 1| 1| |glaciated_areas | 1| 1| 1| |lakes | 1| 1| 1| |lakes_europe | 1| 0| 0| |lakes_historic | 1| 1| 0| |lakes_north_america | 1| 0| 0| |lakes_pluvial | 1| 0| 0| |land | 1| 1| 1| |land_ocean_label_points | 1| 0| 0| |land_ocean_seams | 1| 0| 0| |land_scale_rank | 1| 0| 0| |minor_islands | 1| 0| 0| |minor_islands_coastline | 1| 0| 0| |minor_islands_label_points | 1| 0| 0| |ocean | 1| 1| 1| |ocean_scale_rank | 1| 0| 0| |playas | 1| 1| 0| |reefs | 1| 0| 0| |rivers_europe | 1| 0| 0| |rivers_lake_centerlines | 1| 1| 1| |rivers_lake_centerlines_scale_rank | 1| 1| 0| |rivers_north_america | 1| 0| 0|

Based on the previous table, we know that we can download the ocean vector at small scale (110). Note that scales are defined as one of 110, 50, 10 or small, medium, large.

plot(
  ne_download(type = "ocean", category = "physical", scale = "small")[
    "geometry"
  ],
  col = "lightblue"
)
#> Reading 'ne_110m_ocean.zip' from naturalearth...

Cultural vector data

data(df_layers_cultural)

knitr::kable(
  df_layers_cultural,
  caption = "cultural vector data available via ne_download()"
)

Table: cultural vector data available via ne_download()

|layer | scale10| scale50| scale110| |:-----------------------------------------------|-------:|-------:|--------:| |admin_0_antarctic_claim_limit_lines | 1| 0| 0| |admin_0_antarctic_claims | 1| 0| 0| |admin_0_boundary_lines_disputed_areas | 1| 1| 0| |admin_0_boundary_lines_land | 1| 1| 1| |admin_0_boundary_lines_map_units | 1| 0| 0| |admin_0_boundary_lines_maritime_indicator | 1| 1| 0| |admin_0_boundary_map_units | 0| 1| 0| |admin_0_breakaway_disputed_areas | 0| 1| 0| |admin_0_countries | 1| 1| 1| |admin_0_countries_lakes | 1| 1| 1| |admin_0_disputed_areas | 1| 0| 0| |admin_0_disputed_areas_scale_rank_minor_islands | 1| 0| 0| |admin_0_label_points | 1| 0| 0| |admin_0_map_subunits | 1| 1| 0| |admin_0_map_units | 1| 1| 1| |admin_0_pacific_groupings | 1| 1| 1| |admin_0_scale_rank | 1| 1| 1| |admin_0_scale_rank_minor_islands | 1| 0| 0| |admin_0_seams | 1| 0| 0| |admin_0_sovereignty | 1| 1| 1| |admin_0_tiny_countries | 0| 1| 1| |admin_0_tiny_countries_scale_rank | 0| 1| 0| |admin_1_label_points | 1| 0| 0| |admin_1_seams | 1| 0| 0| |admin_1_states_provinces | 1| 1| 1| |admin_1_states_provinces_lakes | 1| 1| 1| |admin_1_states_provinces_lines | 1| 1| 1| |admin_1_states_provinces_scale_rank | 1| 1| 1| |airports | 1| 1| 0| |parks_and_protected_lands_area | 1| 0| 0| |parks_and_protected_lands_line | 1| 0| 0| |parks_and_protected_lands_point | 1| 0| 0| |parks_and_protected_lands_scale_rank | 1| 0| 0| |populated_places | 1| 1| 1| |populated_places_simple | 1| 1| 1| |ports | 1| 1| 0| |railroads | 1| 0| 0| |railroads_north_america | 1| 0| 0| |roads | 1| 0| 0| |roads_north_america | 1| 0| 0| |time_zones | 1| 0| 0| |urban_areas | 1| 1| 0| |urban_areas_landscan | 1| 0| 0|

plot(
  ne_download(
    type = "airports",
    category = "cultural",
    scale = 10L
  )["geometry"],
  pch = 21L,
  bg = "grey"
)
#> Reading 'ne_10m_airports.zip' from naturalearth...

Searching for countries and continents

In this article, we explore how we can search for data available to download within rnaturalearth. Let's begin by loading country data using the read_sf() function from the sf package. In the following code snippet, we read the Natural Earth dataset, which contains information about the sovereignty of countries.

df <- read_sf(
  "/vsizip/vsicurl/https://naciscdn.org/naturalearth/10m/cultural/ne_10m_admin_0_sovereignty.zip"
)

head(df)
#> Simple feature collection with 6 features and 168 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -109.4537 ymin: -55.9185 xmax: 140.9776 ymax: 7.35578
#> Geodetic CRS:  WGS 84
#> # A tibble: 6 × 169
#>   featurecla     scalerank LABELRANK SOVEREIGNT SOV_A3 ADM0_DIF LEVEL TYPE  TLC   ADMIN ADM0_A3
#>   <chr>              <int>     <int> <chr>      <chr>     <int> <int> <chr> <chr> <chr> <chr>  
#> 1 Admin-0 sover…         5         2 Indonesia  IDN           0     2 Sove… 1     Indo… IDN    
#> 2 Admin-0 sover…         5         3 Malaysia   MYS           0     2 Sove… 1     Mala… MYS    
#> 3 Admin-0 sover…         0         2 Chile      CHL           0     2 Sove… 1     Chile CHL    
#> 4 Admin-0 sover…         0         3 Bolivia    BOL           0     2 Sove… 1     Boli… BOL    
#> 5 Admin-0 sover…         0         2 Peru       PER           0     2 Sove… 1     Peru  PER    
#> 6 Admin-0 sover…         0         2 Argentina  ARG           0     2 Sove… 1     Arge… ARG    
#> # ℹ 158 more variables: GEOU_DIF <int>, GEOUNIT <chr>, GU_A3 <chr>, SU_DIF <int>,
#> #   SUBUNIT <chr>, SU_A3 <chr>, BRK_DIFF <int>, NAME <chr>, NAME_LONG <chr>, BRK_A3 <chr>,
#> #   BRK_NAME <chr>, BRK_GROUP <chr>, ABBREV <chr>, POSTAL <chr>, FORMAL_EN <chr>,
#> #   FORMAL_FR <chr>, NAME_CIAWF <chr>, NOTE_ADM0 <chr>, NOTE_BRK <chr>, NAME_SORT <chr>,
#> #   NAME_ALT <chr>, MAPCOLOR7 <int>, MAPCOLOR8 <int>, MAPCOLOR9 <int>, MAPCOLOR13 <int>,
#> #   POP_EST <dbl>, POP_RANK <int>, POP_YEAR <int>, GDP_MD <int>, GDP_YEAR <int>,
#> #   ECONOMY <chr>, INCOME_GRP <chr>, FIPS_10 <chr>, ISO_A2 <chr>, ISO_A2_EH <chr>,
#> #   ISO_A3 <chr>, ISO_A3_EH <chr>, ISO_N3 <chr>, ISO_N3_EH <chr>, UN_A3 <chr>, WB_A2 <chr>,
#> #   WB_A3 <chr>, WOE_ID <int>, WOE_ID_EH <int>, WOE_NOTE <chr>, ADM0_ISO <chr>,
#> #   ADM0_DIFF <chr>, ADM0_TLC <chr>, ADM0_A3_US <chr>, ADM0_A3_FR <chr>, ADM0_A3_RU <chr>,
#> #   ADM0_A3_ES <chr>, ADM0_A3_CN <chr>, ADM0_A3_TW <chr>, ADM0_A3_IN <chr>, ADM0_A3_NP <chr>,
#> #   ADM0_A3_PK <chr>, ADM0_A3_DE <chr>, ADM0_A3_GB <chr>, ADM0_A3_BR <chr>, ADM0_A3_IL <chr>,
#> #   ADM0_A3_PS <chr>, ADM0_A3_SA <chr>, ADM0_A3_EG <chr>, ADM0_A3_MA <chr>, ADM0_A3_PT <chr>,
#> #   ADM0_A3_AR <chr>, ADM0_A3_JP <chr>, ADM0_A3_KO <chr>, ADM0_A3_VN <chr>, ADM0_A3_TR <chr>,
#> #   ADM0_A3_ID <chr>, ADM0_A3_PL <chr>, ADM0_A3_GR <chr>, ADM0_A3_IT <chr>, ADM0_A3_NL <chr>,
#> #   ADM0_A3_SE <chr>, ADM0_A3_BD <chr>, ADM0_A3_UA <chr>, ADM0_A3_UN <int>, ADM0_A3_WB <int>,
#> #   CONTINENT <chr>, REGION_UN <chr>, SUBREGION <chr>, REGION_WB <chr>, NAME_LEN <int>,
#> #   LONG_LEN <int>, ABBREV_LEN <int>, TINY <int>, HOMEPART <int>, MIN_ZOOM <dbl>,
#> #   MIN_LABEL <dbl>, MAX_LABEL <dbl>, LABEL_X <dbl>, LABEL_Y <dbl>, NE_ID <dbl>,
#> #   WIKIDATAID <chr>, NAME_AR <chr>, NAME_BN <chr>, NAME_DE <chr>, …

Finding countries

One way to search for countries is to search within the ADMIN vector. Let's start by plotting some of the first countries.

lapply(
  df$ADMIN[1L:6L],
  \(x) plot(ne_countries(country = x)["geometry"], main = x)
)

Suppose that we want to search the polygons for the US, how should we spell it?

ne_countries(country = "USA")
ne_countries(country = "United States")
ne_countries(country = "United States Of America")
ne_countries(country = "United States of America")

One possibility consists to search within the ADMIN vector using a regular expression to find all occurrences of the word states.

grep("states", df$ADMIN, ignore.case = TRUE, value = TRUE)
#> [1] "United States of America"       "Federated States of Micronesia"

We can now get the data.

plot(ne_countries(country = "United States of America")["geometry"])

Continents

Finally, let's create plots for each continent using the ne_countries function with the continent parameter.

unique(df$CONTINENT)
#> [1] "Asia"                    "South America"           "Europe"                 
#> [4] "Africa"                  "North America"           "Oceania"                
#> [7] "Antarctica"              "Seven seas (open ocean)"

lapply(
  unique(df$CONTINENT),
  \(x)
    plot(
      ne_countries(
        continent = x,
        scale = "medium"
      )["geometry"],
      main = x
    )
)