Intro to Raster Data

  • The GeoTIFF file format includes metadata about the raster data.
  • To plot raster data with the ggplot2 package, we need to convert it to a dataframe.
  • R stores CRS information in the Proj4 format.
  • Be careful when dealing with missing or bad data values.

Plot Raster Data

  • Continuous data ranges can be grouped into categories using mutate() and cut() or with a binned color scale in ggplot2.
  • Use built-in color palettes with scale_fill_viridis_b or scale_fill_fermenter() or set your preferred color scheme manually.
  • Interactive plotting with plet() and the leaflet library can lead to even better insights as you zoom in and out.

Open and Plot Vector Layers

  • Metadata for vector layers include geometry type, CRS, and extent.
  • Load spatial objects into R with the st_read() function.
  • Spatial objects can be plotted directly with ggplot using the geom_sf() function. No need to convert to a dataframe.

Explore and Plot by Vector Layer Attributes

  • Spatial objects in sf are similar to standard data frames and can be manipulated using the same functions.
  • Almost any feature of a plot can be customized using the various functions and options in the ggplot2 package.

Plot Multiple Vector Layers

  • Use the + operator to add multiple layers to a ggplot.
  • Use st_crop() to put spatially subset a vector data set.
  • Multi-layered plots can combine multiple vector data sets.
  • Use leaflet or zooming in ggplot2 to see small spatial features.

Handling Spatial Projection & CRS

  • Multi-layered plots can combine vector and raster data sets.
  • ggplot2 automatically converts all objects in a plot to the same CRS.
  • Still be aware of the CRS and extent for each object.
  • project() and st_transform() will reproject raster and vector data.
  • crop() and st_crop() will crop raster and vector data.

Convert from .csv to a Vector Layer

Sometimes, we want to crop to a larger area than just the data set. For that, we can create a box from the extent of the new vector object using st_bbox(). This, though, is really just a vector, so we need to turn it into a polygon using st_sfc() (sfc objects are just a raw shape, while sf contains data).

To make this box bigger, we can use st_buffer() which will create a buffer area using a distance specified in meters. So, 1e4 would be 10km.

This technique can be a nice way to put a new vector file in context, as follows.

R 

#Make a bounding box of the Casco Bay area from the data
casco_bbox <- st_bbox(casco_dmr_sf) |>
  st_as_sfc()

# Enlarge it by 10 km
casco_bbox_big <- st_buffer(casco_bbox, 
                            dist = 1e4)

# Crop to the new area
casco <- st_crop(maine |> st_make_valid(), 
                 casco_bbox_big)

# Plot!
ggplot() +
  geom_sf(data = casco, fill = "darkgrey") +
  geom_sf(data = casco_dmr_sf, color = "red")
  • Know the projection (if any) of your point data prior to converting to a spatial object.
  • Convert a data frame to an sf object using the st_as_sf() function.
  • Export an sf object as text using the st_write() function.

Extracting Data from Rasters using Vectors

  • Use the crop() function to crop a raster object.
  • Use the extract() function to extract pixels from a raster object that fall within a particular extent boundary.
  • Use the ext() function to define an extent.

Work with Multi-Band Rasters

  • A single raster file can contain multiple bands or layers.
  • Use the rast() function to load all bands in a multi-layer raster file into R.
  • Individual bands within a SpatRaster can be accessed, analyzed, and visualized using the same functions no matter how many bands it holds.

Raster Calculations

  • Rasters can be computed on using mathematical functions.
  • The lapp() and app() function provides an efficient way to do raster math.
  • The writeRaster() function can be used to write raster data to a file.