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.
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.
We 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.
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
breaks as lists.
The following example uses the
autopoints default options, which automatically determines the color scale break labels using the the base
In the next example, we manually specify the z limits, after using the
summary function to determine appropriate limis.
## Al Ca ## Min. :1.120 Min. : 0.110 ## 1st Qu.:4.660 1st Qu.: 0.710 ## Median :5.540 Median : 1.230 ## Mean :5.531 Mean : 1.963 ## 3rd Qu.:6.353 3rd Qu.: 2.375 ## Max. :9.980 Max. :21.900
Finally, we manually specify the breaks of each variable, which allows for different numbers of colors for each plot.
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
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))
## Warning in autolayout(size, legend = legend, common.legend = common.legend, : There is no room in the outer margin for an outer title. ## Setting par(oma = c(0, 0, 3, 0)).
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.
Naturally, one may wish to change the color palette or the breaks associated with the colors. This can be done via the
breaks must be a sequential vector with length one element longer than
col. We also move the legend scale to the right side.
We add the locations and names of two Colorado cities to the Colorado geochemical data using the
text arguments. The appearance of these can be customized through the
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")
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 function partitions the graphic device into the sections needed to create a sequence of images. The most important function arguments include
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
## Warning in autolayout(c(1, 2), legend = "h", common.legend = FALSE, outer = TRUE): There is no room in the outer margin for an outer title. ## Setting par(oma = c(0, 0, 3, 0)).
# 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)