23  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

• vector map showing the coastline, and perhaps political borders, rivers and other features

• A map made of downloaded tiles (similar to how Google maps works)

Terminology

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.

23.1 Vector base maps

23.1.1 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 low-resolution (1:110 million) coastline and country data, suitable for world maps, are included in the rnaturalearth package. The rnaturalearthdata package, also on CRAN, contains medium-resolution data. High-resolution (1:10 million) data are in the rnaturalearthhires package, instructions for installing this are below.

library(rnaturalearth)

world <- ne_countries(scale = 110)
small_scale_map <- ggplot() +
  geom_sf(data = world) +
  coord_sf(xlim = c(-20, 50), ylim = c(33, 80)) +
  ggtitle("Europe")

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

# Need extra package for high resolution data
# install.packages("rnaturalearthhires", repos = "https://ropensci.r-universe.dev")

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

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

# combine maps with patchwork
library(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 strongly recommend.

Rivers and lakes and other Natural Earth data

Sometimes the coastline and national borders are sufficient. Sometimes they aren’t very informative and you want to add more features, such as rivers, lakes and cities. This can be done with data from Natural Earth (or other sources). Natural Earth datasets can be downloaded directly from the website or with ne_download()

# download if needed
if (!file.exists("maps/ne_10m_rivers_europe.shp")) {
  ne_download(
    scale = 10, type = "rivers_lake_centerlines", category = "physical",
    destdir = "maps/", load = FALSE
  ) # major rivers
  ne_download(
    scale = 10, type = "lakes", category = "physical",
    destdir = "maps/", load = FALSE
  ) # major lakes
}

rivers <- ne_load(scale = 10, type = "rivers_lake_centerlines", destdir = "maps")
lakes <- ne_load(scale = 10, type = "lakes", destdir = "maps")

ggplot() +
  geom_sf(data = europe) +
  geom_sf(data = rivers, colour = "blue", linewidth = 0.2) +
  geom_sf(data = lakes, fill = "lightblue") +
  coord_sf(xlim = c(5, 30), ylim = c(55, 71))

Figure 23.1: Lakes and rivers

Extra rivers and lakes for Europe and N. America are available from Natural Earth, but for a large-scale map, other data sets may be better.

23.1.2 ggOceanMaps

ggOceanMaps is, as the name suggests, focused on ocean map, with coastlines, bathymetry and also glaciers. The ggOceanMaps package includes some data, but high resolution data is downloaded when needed. To avoid repeatedly downloading the same data, open the .Rprofile file by running

usethis::edit_r_profile()

and add

.ggOceanMapsenv <- new.env()
.ggOceanMapsenv$datapath <- "~/ggOceanMapsLargeData"
# you can use a different directory if you want.
# This one should work on any computer

Save the file and restart R (Session > Restart R).

Now ggOceanMaps is ready to use.

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

Exercise

Make a map of Svalbard using either rnaturalearth or ggOceanMaps.

23.1.3 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().

Shapefiles

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

This is a map of the fylke of Norway.

library(sf)

# https://kartkatalog.geonorge.no/metadata/norske-fylker-og-kommuner-illustrasjonsdata-2024-klippet-etter-kyst/a9c64d66-f484-4a8f-a7b4-723fdaa578d3

fylker <- st_read("data/Fylker.geojson")

ggplot(fylker) +
  geom_sf()

Vector data resources

• Kommune and fylke boundaries (with coastline) - geonorge (maps with previous flyke boundaries also available from geonorge)

• Norwegian rivers and lakes - Noregs vassdrags- og energidirektorat.

• Norwegian protected areas - Miljødirektoratet

• Svalbard maps - Norsk Polarinstitutt

• Species occurrence data - GBIF - download with the rgbif package

Tell be about data sources you find useful.

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().

sf::st_crs(fylker)

Coordinate Reference System:
  User input: ETRS89 / UTM zone 33N 
  wkt:
PROJCRS["ETRS89 / UTM zone 33N",
    BASEGEOGCRS["ETRS89",
        ENSEMBLE["European Terrestrial Reference System 1989 ensemble",
            MEMBER["European Terrestrial Reference Frame 1989"],
            MEMBER["European Terrestrial Reference Frame 1990"],
            MEMBER["European Terrestrial Reference Frame 1991"],
            MEMBER["European Terrestrial Reference Frame 1992"],
            MEMBER["European Terrestrial Reference Frame 1993"],
            MEMBER["European Terrestrial Reference Frame 1994"],
            MEMBER["European Terrestrial Reference Frame 1996"],
            MEMBER["European Terrestrial Reference Frame 1997"],
            MEMBER["European Terrestrial Reference Frame 2000"],
            MEMBER["European Terrestrial Reference Frame 2005"],
            MEMBER["European Terrestrial Reference Frame 2014"],
            ELLIPSOID["GRS 1980",6378137,298.257222101,
                LENGTHUNIT["metre",1]],
            ENSEMBLEACCURACY[0.1]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4258]],
    CONVERSION["UTM zone 33N",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",15,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.9996,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["Europe between 12°E and 18°E: Austria; Denmark - offshore and offshore; Germany - onshore and offshore; Norway including Svalbard - onshore and offshore."],
        BBOX[46.4,12,84.42,18]],
    ID["EPSG",25833]]

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, or the wkt. The EPSG code for WGS84 is 4326.

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

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

23.2 Tiled basemaps

Tiled basemaps can be used with either ggspatial or ggmap packages. I recommend using ggspatial as it is consistent with the other mapping tools used here.

Maps made with a tiled background can appear cluttered with unnecessary information such as roads and city names. They are probably best for small areas.

Copyright

If you use a tiled map background, you should attribute the source (e.g., “Copyright OpenStreetMap contributors” when using an OpenStreetMap-based tiles).

23.2.1 ggspatial

We can add a tiled-basemap to a plot with annotation_map_tile() (you may need to install some extra packages at this stage - R will tell you). 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).

library(ggspatial)
ggplot() +
  annotation_map_tile(
    type = "osm",
    cachedir = "maps/",
    zoomin = -1
  ) + # sets the zoom level relative to the default
  coord_sf(
    xlim = c(4.5, 6),
    ylim = c(60.5, 61),
    crs = 4326
  ) # EPSG code for WGS84

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

23.2.2 ggmap

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

Using ggmap

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 (thousands per month).

Then you can run this line in the console

# the write argument will save the key for you.
register_google(key = "[your key]", write = TRUE)

Your key is private. Do not share it with anyone. Do not include it your script or quarto document. It could be expensive if someone else uses it.

Now you can use ggmap

library(ggmap)

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

This map made by ggmap is a ggplot object to which other geoms can be added.

Exercise

Make a tiled map that shows your favourite holiday destination.

23.3 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)

23.3.1 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")

# 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")

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

library(ggspatial)

# GPS tracking data of osprey. Data from
# https://datadryad.org/stash/dataset/doi:10.5061%2Fdryad.w6m905qt2
osprey <- read_delim("maps/osprey/06982.txt",
  locale = locale(decimal_mark = ",")
) |>
  janitor::clean_names()

# ggOceanMaps

basemap(data = osprey) +
  geom_spatial_path(aes(x = longitude_e, y = latitude_n, colour = speed),
    data = osprey, linewidth = 1
  ) +
  labs(colour = expression(Speed ~ km ~ h^{
    -1
  }))

Figure 23.2: Flight speed of a migrating osprey

geom_spatial_point() assumes 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 with geom_spatial_path().

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

Exercise

Otters (Lutra lutra) have re-established in Vestland. File ExcelExport_7972978_Page_1a.xlsx, (from https://artsobservasjoner.no, edited to remove an invalid unicode character) shows observations of otters in Vestland. Make a map of the observations. Relevant columns are “East Coord”, “North Coord”. The coordinates are UTM zone 33N, EPSG code 25833.

23.3.2 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 use data on 2021 phosphorous discharge in municipal wastewater from SSB and join it by Fylkesnummer, and plot it by setting fill in the aes.

p_discharge <- readxl::read_excel(
  path = "data/05280_20230421-012359.xlsx",
  skip = 4, n_max = 11
) |>
  janitor::clean_names() |>
  separate_wider_regex(
    cols = x1, # split fist column
    patterns = c(Fylkesnummer = "\\d{2}", " ", Fylkesnavn = ".*")
  ) # using regular expressions
# \\d{2} = 2 numbers
# " " = space
# ".*" = any number of any character, ie everything else

# join to fylker
fylker_2021 <- st_read("data/fylker2021.json") # older flyker map to match data
fylker_discharge <- fylker_2021 |>
  left_join(p_discharge, by = "Fylkesnummer")

ggplot(fylker_discharge) +
  geom_sf(aes(fill = total_discharge)) +
  labs(fill = "Wastewater\nP discharge\n(tonnes)")

Figure 23.3: Total phosphate discharge in municipal wastewater by fylke.

Cartograms

Sometimes cloropleth maps can be misleading. An alternative is to warp space so that the area of each region is proportional to the value. This is a cartogram, which can be made with the cartogram package.

library(cartogram)

# simplify sf object for speed
fylker_discharge_sim <- sf::st_simplify(fylker_discharge, dTolerance = 1000)

# make cartogram
kart <- cartogram_cont(fylker_discharge_sim, weight = "total_discharge", itermax = 5)

# plot cartogram
ggplot(kart) +
  geom_sf(aes(fill = total_discharge)) +
  labs(fill = "Wastewater\nP discharge\ntonnes") +
  theme(
    legend.position.inside = c(.99, .01),
    legend.justification = c(1, 0)
  )

Figure 23.4: Cartogram showing total phosphate discharge in municipal wastewater by fylke.

Exercise

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

23.3.3 Rasters

Rasters can be used to show maps of continuous data, for example, elevation or sea surface temperature, or model predictions. Depending on the extent, you might not need a separate basemap.

23.3.4 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. terra can integrate with ggplot2 with tidyterra.

stars is designed for spatio-temporal arrays. There are some things it cannot do that terra can (and vice versa). It has good 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(), it is easiest to use a geom from the tidyterra package.

library(terra)
library(tidyterra)

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

# make coastline mask
coast_vector <- fylker |>
  # transfrom to crs of raster
  st_transform(crs = terra::crs(norway_dem)) |>
  # convert to spatVector
  vect()

# crop to vestland and rename the data layer
vestland_extent <- ext(4.5, 9, 59, 62)
vestland_dem <- crop(norway_dem, vestland_extent) |>
  mask(coast_vector) |>
  rename(Elevation = `50N000E_20101117_gmted_med300`)

# plot
ggplot() +
  geom_spatraster(data = vestland_dem) +
  scale_fill_viridis_c(na.value = "grey90") +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0)) +
  labs(fill = "Elevation, m")

Raster map resources

• Low-resolution bathymetry - GEBCO - some data also available through ggOceanMaps

• High-resolution bathymetry (50 m grid) (good for fjords - even higher resolution data is available for some regions) - from mareano at geonorge

• 30 arc seconds (~1 km) Digital elevation model - GMTED2010

• High-resolution (1-50m LiDAR) digital elevation model - Høydedata

• Climate normal data - WorldClim

• Oceanographic conditions - World Ocean Atlas

• Landsat - NASA

23.4 Scalebars, north pointer etc

Scalebars and north pointers can be added with the ggspatial package or the ggsn package. North points are not always necessary 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
  annotation_north_arrow(style = north_arrow_minimal)

23.5 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 22) to show your location in context.

23.6 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. ggOceanMaps can automatically select a projection based on the location, otherwise the map projection can be set using coord_sf().

The projection wizard can help choose a projection for a given area (copy the PROJ string).

world <- ne_countries(scale = "medium")

default <- ggplot(world) +
  geom_sf()

mollweide <- ggplot(world) + # Equal-area world map projection
  geom_sf() +
  coord_sf(crs = "+proj=moll") # projection specified with a 'proj' string

sf_use_s2(FALSE) # might need to turn spherical geometry off for some projections
polar_lambert <- world |>
  # !important - don't crop to tightly or crop lines will show in plot
  st_crop(y = c(xmin = -180, ymin = 50, xmax = 180, ymax = 90)) |>
  ggplot() + # Transverse cylindrical equal-area
  geom_sf() +
  # projection specified with a proj string
  coord_sf(
    crs = "+proj=laea +lon_0=14.4140625 +lat_0=90 +datum=WGS84 +units=m +no_defs",
    ylim = c(100000, -3500000), # xlim/ylim in units of projection
    xlim = c(-2000000, 2000000)
  ) # here in metres

default / mollweide / polar_lambert

Exercise

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

23.7 Interactive maps

Interactive maps can be very useful on webpages and shiny apps, but less useful in theses and papers.

The leaflet or mapgl packages can be used for fully interactive maps.

library(leaflet) # NB uses pipes |> not +

leaflet() |> # initialise map
  setView(lng = 5.3, lat = 60.4, zoom = 9) |> # set initial map area
  addTiles() |> # add background map
  addPolygons(data = aquaculture, popup = aquaculture$name) # add fish farms

Figure 23.5: leaflet map showing fish farms. You can zoom and pan the map, or click to get the fish farm name.

It is possible to change the background map to one that is less cluttered or a satellite view.

Further reading

• Spatial Data Science
• Visualizing geospatial data

Contributors

• Richard Telford