As an example, IвЂ™ll use the oz_states data to draw the Australian states in different colours, and will overlay this plot with the boundaries of Australian electoral regions. The ggplot2 package supports this by allowing you to add multiple geom_sf() layers to a plot. In some instances you may want to overlay one map on top of another. Given data in this format, we can use geom_sf() and coord_sf() to draw a serviceable map without specifying any parameters or even explicitly declaring any aesthetics: Each element in the geometry column is a multipolygon object which, as the name suggests, contains data specifying the vertices of one or more polygons that demark the border of a region. The most important column is geometry, which specifies the spatial geometry for each of the states and territories. mi_counties data where there is one row per polygon vertex). There are 9 distinct geographical units, so there are 9 rows in this tibble (cf.

One advantage to sf data is immediately apparent, we can easily see the overall structure of the data: Australia is comprised of six states and some territories. This output shows some of the metadata associated with the data (discussed momentarily), and tells us that the data is essentially a tibble with 9 rows and 2 columns. This is illustrated in the right panel below. To turn this scatterplot into a map, we use geom_polygon() instead, which draws each county as a distinct polygon. In this plot, each row in the data frame is plotted as a single point, producing a scatterplot that shows the corners of every county. To get a better sense of what the data contains, we can plot mi_counties using geom_point(), as shown in the left panel below. In this data set we have four variables: lat and long specify the latitude and longitude of a vertex (i.e.В a corner of the polygon), id specifies the name of a region, and group provides a unique identifier for contiguous areas within a region (e.g.В if a region consisted of multiple islands).

Add full border origin graphing