inst/examples/gg.sp.R

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

\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))
  }
}