A Quick Demo of SoilProfileCollection Methods and Plotting Functions
Jan 4, 2012 metroadminHere is a quick demo of some of the new functionality in AQP as of version 0.99-9.2. The demos below are based on soil profiles from an archive described in (Carre and Girard, 2002) available on the OSACA page. A condensed version of the collection is available as a SoilProfileCollection object in the AQP sample dataset "sp5". UPDATE 2010-01-12 Syntax has changed slightly, as profileApply() now iterates over a list of SoilProfileCollection objects, one for each profile from the original object.
Image: AQP Sample Dataset 5: Profile Sketches
Setup environment, and try some simple examples.
library(aqp) library(scales) # load sample data -- SoilProfileCollection class object data(sp5) # check out the show() method sp5 # more information: ?sp5 # 4x1 matrix of plotting areas layout(matrix(c(1,2,3,4), ncol=1), height=c(0.25,0.25,0.25,0.25)) # reduce figure margins par(mar=c(0,0,0,0)) # plot profiles, with points added to the mid-points of randomly selected horizons sub <- sp5[1:25, ] plot(sub, max.depth=300) ; mtext('Set 1', 2, line=-1.5, font=2) y.p <- profileApply(sub, function(x) {s <- sample(1:nrow(x), 1) ; h <- horizons(x); with(h[s,], (top+bottom)/2)}) points(1:25, y.p, bg='white', pch=21) # plot profiles, with arrows pointing to profile bottoms sub <- sp5[26:50, ] plot(sub, max.depth=300); mtext('Set 2', 2, line=-1.5, font=2) y.a <- profileApply(sub, function(x) max(x)) arrows(1:25, y.a-50, 1:25, y.a, len=0.1, col='white') # plot profiles, with points connected by lines: ideally reflecting some kind of measured data sub <- sp5[51:75, ] plot(sub, max.depth=300); mtext('Set 3', 2, line=-1.5, font=2) y.p <- 20*(sin(1:25) + 2*cos(1:25) + 5) points(1:25, y.p, bg='white', pch=21) lines(1:25, y.p, lty=2) # plot profiles, with polygons connecting horizons with max clay content (+/-) 10 cm sub <- sp5[76:100, ] y.clay.max <- profileApply(sub, function(x) {i <- which.max(x$clay) ; h <- horizons(x); with(h[i, ], (top+bottom)/2)} ) plot(sub, max.depth=300); mtext('Set 4', 2, line=-1.5, font=2) polygon(c(1:25, 25:1), c(y.clay.max-10, rev(y.clay.max+10)), border='black', col=rgb(0,0,0.8, alpha=0.25)) points(1:25, y.clay.max, pch=21, bg='white')
Image: AQP Sample Dataset 5: Profile Sketches and Environmental Gradients
Plot simulated environmental gradients above/below soil profiles.
# plotting parameters yo <- 100 # y-offset sf <- 0.65 # scaling factor # plot profile sketches par(mar=c(0,0,0,0)) plot(sp5[1:25, ], max.depth=300, y.offset=yo, scaling.factor=sf) # optionally add describe plotting area above profiles with lines # abline(h=c(0,90,100, (300*sf)+yo), lty=2) # simulate an environmental variable associated with profiles (elevation, etc.) r <- vector(mode='numeric', length=25) r[1] <- -50 ; for(i in 2:25) {r[i] <- r[i-1] + rnorm(mean=-1, sd=25, n=1)} # rescale r <- rescale(r, to=c(80, 0)) # illustrate gradient with points/lines/arrows lines(1:25, r) points(1:25, r, pch=16) arrows(1:25, r, 1:25, 95, len=0.1) # add scale for simulated gradient axis(2, at=pretty(0:80), labels=rev(pretty(0:80)), line=-1, cex.axis=0.75, las=2) # depict a secondary environmental gradient with polygons (water table depth, etc.) polygon(c(1:25, 25:1), c((100-r)+150, rep((300*sf)+yo, times=25)), border='black', col=rgb(0,0,0.8, alpha=0.25))
Image: AQP Sample Dataset 5: Profile Sketches and Between-Profile Dissimilarity
Compute and display dissimilarity between profile 1 (left-most) and other 24 profiles, from a random sampling of 25.
# sample 25 profiles from the collection s <- sp5[sample(1:length(sp5), size=25), ] # compute pair-wise dissimilarity d <- profile_compare(s, vars=c('R25','pH','clay','EC'), k=0, replace_na=TRUE, add_soil_flag=TRUE, max_d=300) # keep only the dissimilarity between profile 1 and all others d.1 <- as.matrix(d)[1, ] # rescale dissimilarities d.1 <- rescale(d.1, to=c(80, 0)) # sort in ascending order d.1.order <- rev(order(d.1)) # plotting parameters yo <- 100 # y-offset sf <- 0.65 # scaling factor # plot sketches plot(s, max.depth=300, y.offset=yo, scaling.factor=sf, plot.order=d.1.order) # add dissimilarity values with lines/points lines(1:25, d.1[d.1.order]) points(1:25, d.1[d.1.order], pch=16) # link dissimilarity values with profile sketches via arrows arrows(1:25, d.1[d.1.order], 1:25, 95, len=0.1) # add an axis for the dissimilarity scale axis(2, at=pretty(0:80), labels=rev(pretty(0:80)), line=-2, cex.axis=0.75, las=2)