The autoimage package makes it easy to plot a sequence of images with corresponding color scales, i.e., a sequence of heat maps, with straightforward, native options for projection of geographical coordinates. The package makes it simple to add lines, points, and other features to the images, even when the coordinates are projected.
The package also makes it easy to create a "heated scatterplot", which is similar to a heat map in that color is used to distinguish smaller and larger values of a variable observed over a two-dimensional domain, but is different from a heat map in that the observed data locations are not gridded. Instead of interpolating the data locations onto a grid, the colors are displayed through a typical two-dimensional scatterplot.
The autopoints
function is the main function for creating
a sequence of heated scatterplots; it relies on the
helper function heat_ppoints
, which can be used in more
complex scenarios. We provide the examples below with brief
explanation.
autopoints
ExamplesWe provide examples using the non-gridded Aluminum and Cadmium measurements for the state of Colorado, which are available in the gear package.
Here is a basic heated scatterplot for Aluminum with custom x and y axis labels.
library(autoimage) data(co, package = "gear") autopoints(co$longitude, co$latitude, co$Al, xlab = "lon", ylab = "lat")
In the sets of examples, we display heated scatterplots for
Aluminum and Cadmium while allowing them to have different z
limits and z limit breaks for the color scale. We illustrate
a few different combinations using defaults and by
specifying zlim
and breaks
as lists.
The following example uses the autopoints
default options,
which automatically determines the color scale break labels
using the the base pretty
function.
autopoints(co$longitude, co$latitude, co[, c("Al", "Ca")], xlab = "lon", ylab = "lat", common.legend = FALSE, main = c("Al", "Ca"))
In the next example, we manually specify the z limits,
after using the summary
function to determine appropriate
limis.
summary(co[, c("Al", "Ca")]) autopoints(co$longitude, co$latitude, co[, c("Al", "Ca")], xlab = "lon", ylab = "lat", common.legend = FALSE, main = c("Al", "Ca"), zlim = list(c(0, 10), c(0, 22)))
Finally, we manually specify the breaks of each variable, which allows for different numbers of colors for each plot.
autopoints(co$longitude, co$latitude, co[, c("Al", "Ca")], xlab = "lon", ylab = "lat", common.legend = FALSE, main = c("Al", "Ca"), breaks = list(seq(0, 10, by = 2.5), c(0, 2, 4, 6, 8, 22)))
In the following example, we project the coordinates using the
Bonne projection. This automatically adds longitude/latitude
lines (graticules), which we lighten dramatically through
the col
argument of the paxes.args
list. The
graticules do not extend far enough by default, so we extend
them further through the axis.args
via the xat
and
yat
arguments. We also add text above the Aluminum and
Cadmium plots via the main
argument and a common title
via the outer.title
argument. The mtext.args
argument
list can be used to customize the outer title.
autopoints(co$longitude, co$latitude, co[, c("Al", "Ca")], xlab = "lon", ylab = "lat", proj = "bonne", parameters = 40, paxes.args = list(col = "lightgrey"), axis.args = list(yat = 35:43, xat = -(110:101)), main = c("(a) Aluminum", "(b) Cadmium"), outer.title = "Geochemical measurements", mtext.args = list(col = "blue", cex = 2))
We can add selected borders via the map
arguments (which
makes use of the the maps::map
function. We can also add
borders/lines via the lines arguments. We demnstrate using
USA county maps (map = "county"
) and Colorado state
borders (lines = copoly
) where copoly
is a polygon list
available in the autoimage package. The lines can
be customized through the lines.args
argument list.
autopoints(co$longitude, co$latitude, co$Al, xlab = "lon", ylab = "lat", map = "county", lines.args = list(col = "magenta"))
data(copoly) autopoints(co$longitude, co$latitude, co$Al, xlab = "lon", ylab = "lat", lines = copoly, lines.args = list(col = "orange", lwd = 3))
Naturally, one may wish to change the color palette or the
breaks associated with the colors. This can be done via
the col
and breaks
arguments. breaks
must be a sequential
vector with length one element longer than col
. We also
move the legend scale to the right side.
autopoints(co$longitude, co$latitude, co$Al, legend = "v", xlab = "lon", ylab = "lat", col = colorspace::sequential_hcl(n = 4, palette = "Plasma"), breaks = c(0, 1, 2, 3, 8))
We add the locations and names of two Colorado cities to the
Colorado geochemical data using the points
and text
arguments. The appearance of these can be customized through
the points.args
and text.args
argument lists,
respectively.
citypoints = list(x = c(-104.98, -104.80), y = c(39.74, 38.85), labels = c("Denver", "Colorado Springs")) autopoints(co$lon, co$lat, co[,c("Al", "Ca")], common.legend = FALSE, main = c("Aluminum", "Cadmium"), points = citypoints, points.args = list(pch = 6, col = "magenta"), text = citypoints, text.args = list(pos = 3, col = "orange"), xlab = "lon", ylab = "lat")
autolayout
and autolegend
Suppose we want to add custom features to a sequences of images and heated scatterplots, with each plots receiving different features. One can create a richer sequence of images using the autolayout
and autolegend
functions.
The autolayout
function partitions the graphic device into the sections needed to create a sequence of images. The most important function arguments include size
, legend
, common.legend
, and lratio
, which correspond to the same arguments in the autoimage
function. The outer
argument specifies whether an outer.title
is desired. The default is FALSE
. By default, numbers identify the plotting order of the sections, though these can be hidden by setting show = FALSE
. As an initial example, we create a 2 $\times$ 3 grid of images with a common vertical legend.
We now create a complicated (though unrealistic) example of this. We first extract the borders of Hawaii and Alaska from the "world"
map in the maps package and store it as the hiak
list. We then select a small subset of cities in Colorado from the us.cities
dataset in the maps package and store this in the codf
data frame. Lastly, we select the U.S. capitals from the us.cities
dataset and store this in the capdf
data frame.
# load world map data(worldMapEnv, package = "maps") # extract hawaii and alaskan borders hiak <- maps::map("world", c("USA:Hawaii", "USA:Alaska"), plot = FALSE) # load us city information data(us.cities, package = "maps") # extract colorado cities from us.cities codf <- us.cities[us.cities$country.etc == "CO", ] # select smaller subset of colorado cities # extract capitals from us.cities capdf <- us.cities[us.cities$capital == 2,]
Having obtained the relevant information, we setup a 1 $\times$ 2 matrix of images with individual horizontal legends and an area for a common title. We create an image plot of NARCCAP data using the mercator projection and including grey state borders. The borders of Hawaii and Alaska are added using the plines
function. The state capitals are added to the image using the ppoints
function. The first image is then titled using the title
function. The legend is then added using the autolegend
function. Next, a heated scatterplots of the Colorado Aluminum measurements is created using the heat_ppoints
function. The coordinates are projected using the Bonne projection, the color scheme is customized, grey county borders are added to the plot, but the grid lines are removed. The ppoints
function is then used to add locations for several Colorado cities to the image. The ptext
function is then used to add the names of these cities to the image. The second heated scatterplot is then titled using the title
function. The appropriate legend is then added using the autolegend
function. Lastly, a common title is added using the mtext
function.
# setup plotting area autolayout(c(1, 2), legend = "h", common.legend = FALSE, outer = TRUE) # create image of NARCCAP data. # xlim is chosen so to include alaska and hawaii # add grey state borders # extend graticules (longitude/latitude grid lines) pimage(lon, lat, tasmax[,,1], legend = "none", proj = "mercator", map = "state", xlim = c(-180, 20), axis.args = list(xat = seq(-175, -25, by = 25), yat = seq(-10, 80, len = 10)), lines.args = list(col = "grey")) # add hawaii and alaskan borders plines(hiak, proj = "mercator", col = "grey") # add state captials to image ppoints(capdf$lon, capdf$lat, proj = "mercator", pch = 16) # title image title("tasmax for North America") # add legend for plot autolegend() # load colorado geochemical data data(co, package = "gear") # create image for colorado aluminum measurements # use bonne projection # customize legend colors # add grey county borders # exclude longitude/latitude heat_ppoints(co$lon, co$lat, co$Al, map = "county", legend = "none", proj = "bonne", parameters = 39, paxes.args = list(grid = FALSE), col = cm.colors(5), lines.args = list(col = "grey"), xlab = "lon", ylab = "lat") # add colorado city points to image ppoints(codf$lon, codf$lat, pch = 16, proj = "bonne") # add names of colorado cities to image ptext(codf$lon, codf$lat, labels = codf$name, proj = "bonne", pos = 4) # title plot title("Colorado Aluminum levels (%)") # add legend to current image autolegend() # add common title for plots mtext("Two complicated maps", col = "purple", outer = TRUE, cex = 2)
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