19  Making maps in R

Biologists need almost as many maps as geographers, perhaps to show where our field sites are, the range of a species, or the migration path of a bird.

Typically, the data we want to show is added to a base map. These can be added to different types of base map


Small scale vs large scale? The terminology is confusing!

Map scales are given as a ratio. A world map might have a scale of 1:100000000. 1 cm on the map represents 100000000 cm on the ground - a ratio of 1/100000000. A small number hence a small-scale map.

Conversely, a map of a city might have a scale of 1:25000. 1 cm on the map represents 25000 cm on the ground - a ratio of 1/25000. A large number hence a large-scale map.

Large-scale maps are usually high resolution and cover a small spatial extent.

19.1 Vector base maps

19.1.1 rnaturalearth

The rnaturalearth package makes Natural Earth data available. Natural Earth features include coastlines, rivers, bathymetry, political borders, roads and railways at scales 1:10m, 1:50m, and 1:110 million. The rnaturalearth package includes coastline and country data at the small and medium scale. A companion package rnaturalearthhires has the large scale (1:10 million) data. Other datasets from Natural Earth can be downloaded directly from the website or with ne_download()

#install.packages("rnaturalearthhires", repos = "https://ropensci.r-universe.dev")
world <- ne_countries(scale = 110, returnclass = "sf") 
small_scale_map <- ggplot() +
  geom_sf(data = world) +
  coord_sf(xlim = c(-20, 50), ylim = c(33, 80)) +

europe <- ne_countries(scale = 50, returnclass = "sf", continent = "Europe") 
medium_scale_map <- ggplot() +
  geom_sf(data = europe) +
  coord_sf(xlim = c(5, 30), ylim = c(55, 71)) +

norway <- ne_countries(scale = 10, returnclass = "sf", country = "Norway") 

large_scale_map <- ggplot() +
  geom_sf(data = norway) +
  coord_sf(xlim = c(4, 9), ylim = c(59, 62)) +

# combine maps with patchwork
small_scale_map + medium_scale_map + large_scale_map

coord_sf() is used to show only part of the map.

sf and sp packages

sf and sp are both packages for geospatial data. sf is the newer package that supports the “simple features” standard and is what I recommend, but you will find examples using the older sp package online. Choose one, and don’t mix them together.

19.1.2 ggOceanMaps

ggOceanMaps is, as the name suggests, focused on ocean map, with coastlines, bathymetry and also glaciers. ggOceanMaps requires ggOceanMapsData, which needs to be installed separately


Now ggOceanMaps is ready to use.

#limits are given longitude min/max, latitude min/max
basemap(limits = c(-30, 30, 50, 80),
        bathymetry = TRUE,
        glaciers = TRUE)


Make a map of the Nordic Seas using either rnaturalearth or ggOceanMaps.

19.1.3 Other vector files

The maps in rnaturalearth and ggOceanMaps are good and the global and regional scale, but lack resolution for local scale maps, and may lack features we are interested in.

For such maps we need to find alternative resources. These could be a shapefile, GeoJSON or GeoPackage file, all of which can be imported with sf::st_read().


A “shapefile” is not one file but collection of several files in the same directory, only of which has the extension “.shp”.

Good sources of data for Norway include:

This is a map of the fylke of Norway

Linking to GEOS 3.6.2, GDAL 2.2.3, PROJ 4.9.3; sf_use_s2() is TRUE
fylker <- st_read("data/fylker2021.json")
Reading layer `fylker2021' from data source 
  using driver `GeoJSON'
Simple feature collection with 11 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -77298.97 ymin: 6448400 xmax: 1115097 ymax: 7939977
CRS:           25833
ggplot(fylker) + 

Coordinate reference systems

Most geographic data are given with latitude and longitude, but sometimes, especially for local-regional maps, the data are given as Universal Transverse Mercator (UTM) coordinates instead.

UTM coordinates are a projection of the spherical Earth onto one of 60 flat surfaces.

Most modern latitude-longitude data will use the WGS84 geodetic standard. Older data might use other standards.

You can find the coordinate system of a sf class object with sf::st_crs().

Coordinate Reference System:
  User input: 25833 
PROJCS["ETRS89 / UTM zone 33N",
            SPHEROID["GRS 1980",6378137,298.257222101,

This gives a lot of information, the most important is that the coordinate reference systems is UTM zone 33N.

If we need to change a coordinate reference systems, we can do that with sf::st_transform(). You need to know the EPSG code of the target reference system. The code for WGS84 is 4326.

fylker2 <- sf::st_transform(fylker, crs = 4326)

geom_sf() will automatically transform coordinate systems (if they are specified).


Get a sf object from rnaturalearth::ne_coastline() and find out what coordinate reference system it uses.

19.2 Tiled basemaps

Tiled basemaps can be used with either ggspatial or ggmap packages. I would generally recommend using ggspatial as it is consistent with the other mapping tools used here. If you use a tiled map background, you should attribute the source (e.g., “Copyright OpenStreetMap contributors” when using an OpenStreetMap-based tiles).

19.2.1 ggspatial

We can add a tiled-basemap to a plot with annotation_map_tile(). Here, we need to use coord_sf() to set the map extent and coordinate reference system as we have not added any sf layers with geom_sf(). Downloaded tiles will be stored in the maps directory (which you may need to make first).

ggplot() +
    type = "osm", 
    cachedir = "maps/", 
    zoomin = -1) + # sets the zoom level relative to the default
    xlim = c(4, 8), 
    ylim = c(59, 62),
    crs = 4326) # EPSG code for WGS84

Several different types of maps are available (see rosm::osm.types()) and more can be added.

19.2.2 ggmap

The ggmap package lets you use Google Maps and other similar maps as a basemap.


ggmap can use maps and satellite image from Google, but you need to register for an API key. You shouldn’t be charged unless you make a lot of maps (more than 20000 per month).


bergen <- get_map(
  location = c(5.24, 60.37, 5.36, 60.41), #  left/bottom/right/top
  source = "stamen"           

Maps made with ggmap can appear cluttered with unnecessary information.


Make a tiled map that shows your favourite holiday destination.

19.3 Raster basemaps

Rasters can be used to show maps of continuous data, for example, elevation or sea surface temperature.

19.3.1 terra

The terra package can import raster images in several formats, including GeoTIFF.

terra vs raster vs stars packages

The terra package is an update to the widely-used raster package. It should be faster and easier to use.

stars is designed for spatio-temporal arrays. There are some things it cannot do that terra can (and vice versa) but has better integration with sf and ggplot2.

Rasters imported with terra are easy to plot with the base R plot() function, but if we want to use ggplot(), we have to first convert to an sf object.


# import digital elevation model
# data from https://topotools.cr.usgs.gov/gmted_viewer/viewer.htm
norway_dem <- rast("data/50N000E_20101117_gmted_med300.tif")

# crop to vestland and convert to sf object
vestland_extent <- ext(4.5, 9, 59, 62)
vestland_dem <- crop(norway_dem, vestland_extent) |> 
  as.points() |> 

#rename the data layer
names(vestland_dem)[1] <- "Elevation"

# plot

ggplot(vestland_dem) +
  geom_sf(aes(colour = Elevation))

If the raster is in UTM coordinates, it is possible to convert to a data.frame and then plot with geom_raster(). Then ggplot() just treats it as regular plot and does not know that it is a map.

19.4 Adding data to the basemap

After deciding what type of base map to draw, we can add the data we want to show with the map. This can be

  • points, line, and polygons

  • Shaded political units (a cloropleth map)

  • A grid of values (raster)

19.4.1 points/lines/polygons

Points lines and polygons can be added to the base map. If the data are already a sf object they can be plotted with geom_sf().

# aquaculture sites downloaded from Barentswatch.no/fiskinfo
aquaculture <- st_read("data/flate-ihht-akvakulturregisteret20220928.geojson")
Reading layer `flate-ihht-akvakulturregisteret20220928' from data source 
  using driver `GeoJSON'
Simple feature collection with 1330 features and 4 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 4.660283 ymin: 58.01472 xmax: 30.41932 ymax: 71.02117
CRS:           4326
# with rnaturalearth
ggplot() +
  geom_sf(data = europe) +
  geom_sf(data = aquaculture, colour = "red") +
  coord_sf(xlim = c(5, 30), ylim = c(55, 71))  

# with ggOceanMaps

basemap(limits = c(-30, 30, 50, 80)) +
  geom_sf(data = aquaculture, colour = "red")

# with ggmap
vestland <- get_map(
  location = c(4,  59, 8, 62),
  source = "stamen"           

# needs inherit.aes = FALSE
ggmap(vestland) + 
 geom_sf(data = aquaculture, colour = "red", inherit.aes = FALSE)

Alternatively, you can make/import a tibble with the geographic data and add them to the basemap with the relevant spatially aware geom. So geom_spatial_point() rather than geom_point()


random_points <- tibble(
  lat = runif(n = 10, 59, 62), 
  lon = runif(n = 10, 5, 8)) 

# ggOceanMaps

basemap(limits = c(-30, 30, 50, 80)) +
  geom_spatial_point(aes(x = lon, y = lat), 
             data = random_points,
             colour = "red") +
  geom_spatial_path(aes(x = lon, y = lat),
            data = random_points, 
            colour = "red")

# with ggmaps. just use `geom_point()` Don't need aes as it expects lat, lon
ggmap(vestland) + 
  geom_point(data = random_points) +
  geom_path(data = random_points)

geom_spatial_point() is assuming that the data are latitude-longitude coordinates. It they are UTM, you will need to use the crs argument with the correct EPSG code.

geom_path() vs geom_line()

geom_path() draws a line from the first point in the dataset to the second and so on. This is useful for plotting on maps (and ordinations).

geom_line() draws a line from the left-most point to the next left-most point in the dataset. This is useful for plotting timeseries.

Degrees minutes and seconds

For latitude-longitude data, we recommend using decimal degrees (Bergen is at 60.3807°N, 5.3323°E). But archived data can be in all sorts of unhelpful formats, such as degrees minutes and seconds (Bergen is at 60° 22’ 50.52” N 5° 19’ 56.28” E). If you get data like this, you need to convert it to decimal degrees. The parzer package can help (it is like lubridate for latitude-longitude data). For example:

parzer::parse_lat("60° 22' 50.52''N")
[1] 60.3807

Download and import the data for Andvik et al (2021) on contaminants in orca.

Make a map to show the hexabromobenzene (HBB) concentrations in the blubber.

19.4.2 Cloropleth maps

Cloropleth maps are useful for plotting data that have been aggregated to a geographic unit (kommune, fylke, country etc). A sf object is a special type of data frame that we can filter(), mutate() or left_join() to other data frames. We need a tibble with the data that we can join to the sf object with the geographic units. Here, I generate some random data and join it by Fylkesnummer (Fylkesnavn column in fylke is complicated), and plot it by setting fill in the aes.

random_fylke <- tibble(
  Fylkesnummer = c("38", "42", "30", "11", "18", "15", "46", "03", "50", "54", "34"),
  random = runif(11))

fylker |> left_join(random_fylke) |> 
  ggplot() +
  geom_sf(aes(fill = random))


With rnaturalearth data, make a world map that shows the population (column pop_est) of each country.

19.4.3 raster

Raster data, perhaps the output of a model, can also be added to maps, using code similar to that for plotting a raster basemap.

19.5 Scalebars, north pointer etc

Scalebars and north pointers can be added with the ggspatial package or the ggsn package. North points are not very useful if the map has gridlines as these already indicate north. A scalebar can be useful, especially for large scale map. On small scale maps, they can be inaccurate as the scale varies.

# with rnaturalearth
ggplot() +
  geom_sf(data = norway) +
  coord_sf(xlim = c(4, 9), ylim = c(59, 62)) +
  annotation_scale(location = "br") # br = bottom right
Scale on map varies by more than 10%, scale bar may be inaccurate

19.6 Hints for maps

Keep it simple. Remove unnecessary features (do you really need to show the bathymetry?) and use appropriate scale data for the base map (too high resolution takes a long time to plot and can look worse).

Use facets as necessary (different species, different years etc).

If you need multiple colour scales, the ggnewscale package can help. Use inset maps (Chapter 18) to show your location in context.

19.6.1 Projections

The Earth is an oblate sphere and needs projecting to plot in two dimensions. This inevitably leads to distortions, especially for maps with a large extent. Different projections have different properties and may be suitable for different purposes or regions. Map projections can be set using coord_sf().

world <- ne_countries(scale = "medium", returnclass = "sf")

default <- ggplot(world) + geom_sf()

mollweide <- ggplot(world) + 
  geom_sf() + 
  coord_sf(crs = "+proj=moll")

lambert <-  ggplot(world) + 
  geom_sf() + 
  coord_sf(crs = "+proj=laea")

Many of the projections have optional parameters to change, for example, the projection centre.


Change the projection of one of the small-scale maps you made previously.


  • Richard Telford