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Introduction

The developments below are in the github version of sp, and will become part of sp release 1.2-3 and higher.

Plotting spatial grids using the plot method was up till now not much fun, and limited to either showing a grid, as in

library(sp)
demo(meuse,ask=FALSE,echo=FALSE)
plot(geometry(meuse.grid))

which only shows the cell geometry, or using image as in

image(meuse.grid["dist"])

For more advanced plots, showing color scales, one needed to use spplot. ssplot is a powerful function, but may also be challenging to use, plots may be challenging to fine manipulate.

The are base plots coming from plot can be improved by all the bells and whistles of base plot, using par and by incrementally adding features. The big missing thing though is a colour legend showing the z-values. Both raster::plot and spatstat::plot provide colour scales, and it is about time to add this to sp’s plot methods for SpatialGridDataFrame and SpatialPixelsDataFrame objecs as well. With the help of this blog entry, this wasn’t too much work.

plot(meuse.grid["dist"], zlim = c(0,1))

which shows a grid using a larger part of the plotting area, and a color scale bar. zlim has been set here to ensure the scale goes to 1 (the data go to 0.99).

The rest of this blog post shows the options now available to this plot method.

Incrementally adding elements

The new plot method uses layout to make two plotting areas, one for the grid and one for the scale. Since the last element plotted is the grid, we can add to it:

plot(meuse.grid["dist"], zlim = c(0,1))
title("distance to river Meuse (normalized)")
points(meuse, col = 'green')
box()

Axes, scale placement and size

Axes can be added by specifying axes = TRUE, the location of the scale by specifying axis.pos, which follows the numbering of ?axis. Note how the size of the scale adapts to the axes:

plot(meuse.grid["dist"], zlim = c(0,1), axes = TRUE)


plot(meuse.grid["dist"], zlim = c(0,1), axes = TRUE, axis.pos = 1)

The scale size can be reduced by specifying scale.shrink; its thickness can be modified by scale.size:

plot(meuse.grid["dist"], zlim = c(0,1), axes = TRUE, scale.shrink = 1)


plot(meuse.grid["dist"], zlim = c(0,1), axes = TRUE, scale.size = lcm(2.4))

where lcm(2) indicates 2 cm, meaning that it isn’t affected by changing plot size. If a numeric values is given here, e.g. 1/6, the size is relative to the area area occupied by the grid.

to increase the white space around the gridded area, xaxs = "r" can be passed, which adds the usual 4% on each side.

Specifying the scale color breaks, tics and labels

Color breaks are specified by breaks, specifying breaks requires that a color ramp of matching length is specified (one less than the number of breaks). Tics and tic labels are defined by parameter at. When specifying breaks, zlim becomes obsolete.

b = c(0, 0.25, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
col = rev(bpy.colors(length(b)-1))
plot(meuse.grid["dist"], breaks = b, col = col, at = b)

Adding grid lines

Grid lines can be added by specifying a grid cell border color, as in

plot(meuse.grid["dist"], border = grey(0.6))

This also enlightens the difference between SpatialPixelsDataFrame (as plotted above), and SpatialGridDataFrame which has the complete set of grid cells in a rectangular area:

library(methods)
plot(as(meuse.grid, "SpatialGridDataFrame")["dist"], border = grey(0.6))

Plotting scale only, or image only

Sometimes, a user needs an image of the scale only, e.g. to glue it onto a leaflet plot when it does not properly support legends. In the following, the figure (device) width was set to 2 (inch), so that the scale still looks nice:

plot(meuse.grid["dist"], what = "scale", zlim = c(0,1))

The other option is to only plot the grid, without scale:

plot(meuse.grid["dist"], what = "image")

Plotting gridded categorical (factor) variables

Categorical (factor) variables need a different treatement, as continuous color change does not work for them, and neither do continuous color scales. The default color scale used in this case is Set2 from the RColorBrewer package; see also the colorbrewer and the command RColorBrewer::display.brewer.all().

plot(meuse.grid["ffreq"])


plot(meuse.grid["ffreq"], axis.pos = 1)

Factors: changing the density and width of scales, label width

The amount of space taken up by a colored field in a legend class can be manipulated. Argument scale.n determines how many fields would fill a complete side (default 15), increasing it decreases the (here vertical) size of a color field:

plot(meuse.grid["ffreq"], scale.n = 25)

Argument scale.frac determines which fraction of the scale area is taken by the coloured bar; in this case (axis.pos=4) it determines the width of the color area (default 0.3):

plot(meuse.grid["ffreq"], scale.frac = 0.5)

In the following example, wide labels need extra space; this is created by

  • increasing scale.size, so there is more space for the scale + labels
  • decreasing scale.frac so that the absolute size of the bar remains the same
levels(meuse.grid$ffreq) = c("frequent", "moderately frequent", "infrequent")
plot(meuse.grid["ffreq"], scale.size = lcm(5.8), scale.frac = 0.15)

Relative size of the categorical scale

The relative size of a scale compared to the rest of the image, including its fonts, can be manipulated by changing the total size of the graph. In the following, the size is set to 12 x 12 (inch); the (automatic) resizing of the final image gives a seemingly smaller scale:

plot(meuse.grid["dist"])

Arranging multiple plots

Multiple plots can be arranged on a single device using layout. Since plot already uses layout when arranging the image and the scale, simply putting two images with scales side by side is not simple – layouts cannot be nested. A solution is to create plots of only image and scale (taking care of zlim), and have them precede by an appropriate layout statement. For a shared legend, e.g. by

layout(matrix(1:3, 1, 3), widths = c(4,4,1))
plot(meuse.grid, what = "image", zlim = c(0,1))
plot(meuse.grid["dist"], what = "image", zlim = c(0,1))
plot(meuse.grid["dist"], what = "scale", zlim = c(0,1))

and for separate legends e.g. by

layout(matrix(1:4, 2, 2), heights = c(4,1))
levels(meuse.grid$ffreq) = c("fr", "mo", "inf")
plot(meuse.grid["ffreq"], what = "image")
plot(meuse.grid["ffreq"], what = "scale", axis.pos = 1)
plot(meuse.grid["dist"], what = "image", zlim = c(0,1))
plot(meuse.grid["dist"], what = "scale", axis.pos = 1, zlim = c(0,1))

More powerful arrangment methods are obtained when using plotting methods available in package grid, or higher-level plotting functions such as spplot or ggplot2::ggplot.

Challenges

  • integrate this with the plotting of other classes with attributes, in particular SpatialPolygonsDataFrame and SpatialLinesDataFrame
  • get the text labels along vertical continuous scales horizontal, meaning don’t use axis and more managment of space
  • unify the scaling of the scale bars for the two different scale types (continuous, categorical)
  • deal with the multiple plot issue more elegantly