plot,corridor,ANY-method {grainscape} | R Documentation |
Plot quick visualizations of grainscape
objects
Description
Plot quick visualizations of mpg
, grain
, and corridor
objects.
This function is intended to get a quick look at the state of a grainscape
object by rendering what are likely to be the most universally useful
visualizations of the spatial data within these objects.
Much more control is available using ggGS()
with ggplot()
enabling the layering of different different analytical products, and the
visualization of node and link attributes.
For high-resolution visualization and the greatest level of control use
export()
to export spatial objects for cartographic representation
in a geographic information system (GIS).
Usage
## S4 method for signature 'corridor,ANY'
plot(x, y, quick = NULL, print = TRUE, theme = TRUE, ...)
## S4 method for signature 'grain,ANY'
plot(x, y, quick = NULL, print = TRUE, theme = TRUE, ...)
## S4 method for signature 'mpg,ANY'
plot(x, y, quick = NULL, print = TRUE, theme = TRUE, ...)
Arguments
x |
A |
y |
Ignored. |
quick |
If |
print |
Render the |
theme |
Apply grainscape theme and scale aesthetics. Default is |
... |
Additional arguments (not used). |
Value
Invisibly, a `ggplot2` object to which additional `ggplot` geoms and adjustments can be applied. Has the side effect of rendering the plot, unless `print = FALSE`.
Types of visualization available with the quick
parameter
"mpgPerimPlot"
gives a a vector rendering of the minimum planar
graph with vector links connecting the perimeters of the patches. This
doesn't accurately represent the sinuosity of paths of the links between patches
but offers a good approximation that renders better at large extents.
Default for mpg
objects. Not available for other objects.
"mpgPlot"
gives a raster-only rendering of the minimum planar graph
where patchId
are positive integers, and linkId
are negative
integers showing the shortest paths between patches Only available for
mpg
objects.
"network"
gives a vector rendering of the minimum planar graph or
the grains of connectivity network with nodes and links plotted at the
patch or polygon centroid locations. Available for mpg
and grain
objects. Default for grain
objects.
"grainPlot"
gives a raster and vector rendering of the grains of
connectivity network with nodes and links plotted at polygon centroid locations,
superimposed over the boundaries of the Voronoi polygons.
Can be time consuming on large rasters due to the Voronoi boundary extraction.
Only available for grain
objects.
"corridorPlot"
renders the output of a corridor()
analysis.
It is the only option available with corridor
objects and the default.
Author(s)
Alex Chubaty and Paul Galpern
See Also
ggGS()
,
export()
,
corridor,
grain,
mpg
Examples
## Load raster landscape
tiny <- raster::raster(system.file("extdata/tiny.asc", package = "grainscape"))
## Create a resistance surface from a raster using an is-becomes reclassification
tinyCost <- raster::reclassify(tiny, rcl = cbind(c(1, 2, 3, 4), c(1, 5, 10, 12)))
## Produce a patch-based MPG where patches are resistance features=1
tinyPatchMPG <- MPG(cost = tinyCost, patch = tinyCost == 1)
## Extract a representative subset of 5 grains of connectivity
tinyPatchGOC <- GOC(tinyPatchMPG, nThresh = 5)
if (interactive()) {
library(ggplot2)
## MPG and showing simplified links among the perimeters of patches
plot(tinyPatchMPG)
## MPG showing links among the nodes of connected patches
plot(tinyPatchMPG, quick = "network")
## MPG showing the shortest paths between patches actually used to
## to calculate link weight values
plot(tinyPatchMPG, quick = "mpgPlot")
## A grain of connectivity network plot with Voronoi boundaries
plot(grain(tinyPatchGOC, 3), quick = "grainPlot")
## Capture plot output for further processing with ggplot
g <- plot(tinyPatchMPG, print = FALSE, theme = FALSE)
g <- g + theme_minimal() + ggtitle("Minimum planar graph") +
theme(plot.title = element_text(size = 20, hjust = 0.5)) +
theme(legend.position = "none") +
xlab("Easting") + ylab("Northing")
g
## To change aesthetics it is best to build the plot from scratch
## using grainscape::ggGS(). See examples therein.
}