Data visualization in geoGraph

This vignette will cover the main functions for visualizing all objects in geoGrapgh. It first gives an overview of the basic plotting functions and then also has a section for more advanced visualization using ggplot2.

Basic visualizing

An essential aspect of spatial analysis lies in visualizing the data. In geoGraph, the spatial grids (gGraph) and spatial data (gData) can be plotted and browsed using a variety of functions.

Plotting `gGraph` objects

Displaying a gGraph object is done through plot and points functions. The first opens a new plotting region, while the second draws in the current plotting region; functions have otherwise similar arguments (see ?plot.gGraph).

By default, plotting a gGraph displays the grid of nodes overlaying a shapefile (by default, the landmasses). Edges can be plotted at the same time (argument edges), or added afterwards using plotEdges. If the gGraph object possesses an adequately formed meta$colors component, the colors of the nodes are chosen according to the node attributes and the color scheme specified in meta$colors. Alternatively, the color of the nodes can be specified via the col argument in plot/points.

Here is an example using worldgraph.10k:

getColors(worldgraph.10k, res.type = "rules")

##            habitat       color
## 1              sea        blue
## 2             land       green
## 3         mountain       brown
## 4       landbridge light green
## 5 oceanic crossing  light blue
## 6  deselected land   lightgray

head(getNodesAttr(worldgraph.10k))

##   habitat
## 1     sea
## 2     sea
## 3     sea
## 4     sea
## 5     sea
## 6     sea

table(getNodesAttr(worldgraph.10k))

## habitat
## deselected land            land             sea 
##             290            2632            7320

plot(worldgraph.10k, reset = TRUE)

## Spherical geometry (s2) switched off

## Spherical geometry (s2) switched on

title("Default plotting of worldgraph.10k")

It may be worth noting that plotting gGraph objects involves plotting a fairly large number of points and edges. On some graphical devices, the resulting plotting can be slow. For instance, one may want to disable cairo under linux: this graphical device yields better graphics than Xlib, but at the expense of increased computational time. To switch to Xlib, type:

X11.options(type = "Xlib")

and to revert to cairo, type:

X11.options(type = "cairo")

Plotting `gData` objects

gData objects are by default plotted overlaying the corresponding gGraph. To show this, we use the cities example from the vignette ‘get started’:

bordeaux <- c(-1, 45)
berlin <- c(13, 52)
baku <- c(44, 40)
timbuktu <- c(-3, 16)

cities.dat <- rbind.data.frame(bordeaux, berlin, baku, timbuktu)
colnames(cities.dat) <- c("lon", "lat")
row.names(cities.dat) <- c("Bordeaux", "Berlin", "Baku", "Timbuktu")
cities.dat$pop <- c(250000, 3500000, 2000000, 50000)
cities.dat

##          lon lat     pop
## Bordeaux  -1  45  250000
## Berlin    13  52 3500000
## Baku      44  40 2000000
## Timbuktu  -3  16   50000

cities <- new("gData", coords = cities.dat[, 1:2], data = cities.dat[, 3, drop = FALSE], gGraph.name = "worldgraph.10k")
plot(cities, type = "both", reset = TRUE)

## Spherical geometry (s2) switched off

## Spherical geometry (s2) switched on

text(getCoords(cities), rownames(getData(cities)))

Note the argument reset=TRUE, which tells the plotting function to adapt the plotting area to the geographic extent of the dataset.

To plot additional information, it can be useful to extract the spatial coordinates from the data. This is achieved by getCoords. This method takes an extra argument original, which is TRUE if original spatial coordinates are sought, or FALSE for coordinates of the nodes on the grid. We can use this to represent, for instance, the population sizes for the different cities:

transp <- function(col, alpha = .5) {
  res <- apply(
    col2rgb(col), 2,
    function(c) rgb(c[1] / 255, c[2] / 255, c[3] / 255, alpha)
  )
  return(res)
}

plot(cities, reset = TRUE)

## Spherical geometry (s2) switched off

## Spherical geometry (s2) switched on

par(xpd = TRUE)
text(getCoords(cities) + -.5, rownames(getData(cities)))
symbols(getCoords(cities)[, 1], getCoords(cities)[, 2],
  circ = sqrt(unlist(getData(cities))), inch = .2,
  bg = transp("red"), add = TRUE
)

Autoplotting and advanced visualization

Now if we want to have more advanced visualization and publication ready plots, we can use the autoplot functions that are based on ggplot2. Currently there are autoplot methods for gGraph and gData objects that provide a quick way to visualize the data. For even more customization, we can directly use the underlying geom_ggraph, geom_gdata and geom_gpath functions that allow for more control over the plotting. Under the hood these functions convert the gGraph, gData and gPath objects into sf objects that can be plotted using ggplot2.

Autoplotting `gGraph` and `gData` objects

For gGraph objects the autoplot function will by default return a ggplot object with the nodes colors based on the first node attribute and the edges (if specified as edges = TRUE) drawn in grey.

autoplot(worldgraph.40k)

Because the ggplot layers convert the graph’s coordinates to sf geometry internally, we have full access to the projections supported by sf. By default autoplot uses an equirectangular projection (longitude and latitude plotted directly). To use a different projection, add coord_sf() with the desired CRS. For example, the Robinson projection often used for world maps:

autoplot(worldgraph.40k) + coord_sf(crs = "+proj=robin")

## Coordinate system already present.
## ℹ Adding new coordinate system, which will replace the existing one.

Similarly when we want to plot a gData object, we can use the autoplot function. By default, the linked gGraph will be plotted as well. If we want to plot only the gData object, we can set the show.gGraph argument to FALSE. Again, we can use the coord_sf function to change the projection of the plot.

autoplot(hgdp) + coord_sf(crs = "+proj=eck4")

## Coordinate system already present.
## ℹ Adding new coordinate system, which will replace the existing one.

More advanced plots using `geom_ggraph`, `geom_gdata` and `geom_gpath`

Finally if we want to have more control over the plotting, we can use the geom_ggraph, geom_gdata and geom_gpath functions. These functions allow us to customize the plots using the full power of ggplot2. Lets say for example we want to plot the example from the Get Started vignette, we can use the geom_gpath function to plot the paths and the geom_gdata function to plot the HGDP populations. Finally we can use the coord_sf function to change the projection of the plot to orthographic and set custom colors for the land, sea and coast.

addis <- list(lon = 38.74, lat = 9.03)
addisNode <- closestNode(worldgraph.40k, addis)
myPath <- dijkstraFrom(hgdp, addisNode)

ggplot() +
  geom_ggraph(
    data = worldgraph.40k, aes(color = habitat),
    edges = FALSE, size = 1, show.legend = FALSE
  ) +
  scale_color_manual(values = c(
    land = "grey70", sea = "lightblue",
    coast = "grey70"
  )) +
  geom_gpath(data = myPath, color = "firebrick", linewidth = 0.4) +
  geom_gdata(
    data = hgdp, aes(fill = Genetic.Div),
    shape = 21, color = "white", size = 2.5, stroke = 0.3
  ) +
  scale_fill_viridis_c(name = "Genetic diversity") +
  coord_sf(crs = "+proj=ortho +lat_0=40 +lon_0=30") +
  theme_void()

Data visualization in geoGraph

2026-09-07