Geom for SpatialPolygons objects

Uses the ggplot2::fortify() function to turn the SpatialPolygons objects into a data.frame. Then calls geom_polygon to plot the polygons. Requires the ggplot2 package.

Usage

# S3 method for class 'SpatialPolygons'
gg(data, mapping = NULL, crs = NULL, ...)

Arguments

data

A SpatialPolygons or SpatialPolygonsDataFrame object.

mapping

Aesthetic mappings created by aes used to update the default mapping.

crs

A CRS object defining the coordinate system to project the data to before plotting.

...

Arguments passed on to geom_sf. Unless specified by the user, the argument alpha = 0.2 (alpha level for polygon filling) is added.

Value

A geom_sf object.

Details

Up to version 2.10.0, the ggpolypath package was used to ensure proper plotting, since the ggplot2::geom_polygon function doesn't always handle geometries with holes properly. After 2.10.0, the object is converted to sf format and passed on to gg.sf() instead, as ggplot2 version 3.4.4 deprecated the intenrally used ggplot2::fortify() method for SpatialPolygons/DataFrame objects.

See also

Other geomes for spatial data: gg(), gg.SpatRaster(), gg.SpatialGridDataFrame(), gg.SpatialLines(), gg.SpatialPixels(), gg.SpatialPixelsDataFrame(), gg.SpatialPoints(), gg.sf()

Examples

# \donttest{
  if (require("ggplot2", quietly = TRUE) &&
      requireNamespace("terra", quietly = TRUE) &&
      bru_safe_sp() &&
      require("sp")) {
    # Load Gorilla data

    gorillas <- inlabru::gorillas_sf

    gcov <- gorillas_sf_gcov()
    elev <- terra::as.data.frame(gcov$elevation, xy = TRUE)
    elev <- sf::as_Spatial(sf::st_as_sf(elev, coords = c("x", "y")))

    # Turn elevation covariate into SpatialGridDataFrame
    elev <- sp::SpatialPixelsDataFrame(elev, data = as.data.frame(elev))

    # Plot Gorilla elevation covariate provided as SpatialPixelsDataFrame.
    # The same syntax applies to SpatialGridDataFrame objects.

    ggplot() +
      gg(elev)

    # Add Gorilla survey boundary and nest sightings

    ggplot() +
      gg(elev) +
      gg(gorillas$boundary, alpha = 0.0, col = "red") +
      gg(gorillas$nests)

    # Load pantropical dolphin data

    mexdolphin <- inlabru::mexdolphin_sp()

    # Plot the pantropical survey boundary, ship transects and dolphin sightings

    ggplot() +
      gg(mexdolphin$ppoly) + # survey boundary as SpatialPolygon
      gg(mexdolphin$samplers) + # ship transects as SpatialLines
      gg(mexdolphin$points) # dolphin sightings as SpatialPoints

    # Change color

    ggplot() +
      gg(mexdolphin$ppoly, color = "green") + # survey boundary as SpatialPolygon
      gg(mexdolphin$samplers, color = "red") + # ship transects as SpatialLines
      gg(mexdolphin$points, color = "blue") # dolphin sightings as SpatialPoints


    # Visualize data annotations: line width by segment number

    names(mexdolphin$samplers) # 'seg' holds the segment number
    ggplot() +
      gg(mexdolphin$samplers, aes(color = seg))

    # Visualize data annotations: point size by dolphin group size

    names(mexdolphin$points) # 'size' holds the group size
    ggplot() +
      gg(mexdolphin$points, aes(size = size))
  }

# }