diff --git a/demo/aqp.R b/demo/aqp.R
index 3c626d05d..77fed1e20 100644
--- a/demo/aqp.R
+++ b/demo/aqp.R
@@ -1,19 +1,20 @@
 ##
-## main demo for AQP package-- a work in progress, largely just material from help files
+## main demo for AQP package
 ##
 
-# required packages
-require(aqp)
-require(ape)
-require(cluster)
-require(lattice)
-require(reshape)
+# load packages
+library(aqp)
+library(ape)
+library(cluster)
+library(lattice)
+library(data.table)
 
+# Example 1: sp1: 9 soil profiles from Pinnacles National Monument, CA.
+data(sp1)
 
 # 
 # 1. basic profile aggregation and plotting
 # 
-data(sp1)
 depths(sp1) <- id ~ top + bottom
 
 # aggregate all profiles into 1,5,10,20 cm thick slabs, computing mean values by slab
@@ -29,53 +30,69 @@ s20 <- slab(sp1, ~ prop, slab.fun=mean, na.rm=TRUE, slab.structure=20)
 head(s1)
 
 # variation in segment-weighted mean property: very little
-round(sapply(
-list(s1, s5, s10, s20), 
-function(i) {
-	with(i, sum((bottom - top) * value) / sum(bottom - top)) 
-	}
-), 1)
+round(sapply(list(s1, s5, s10, s20),
+             function(i) {
+               with(i, sum((bottom - top) * value) / sum(bottom - top))
+             }), 1)
 
 # combined viz
-g2 <- make.groups("1cm interval"=s1, "5cm interval"=s5, 
-"10cm interval"=s10, "20cm interval"=s20)
+g2 <- make.groups(
+  "1cm interval" = s1,
+  "5cm interval" = s5,
+  "10cm interval" = s10,
+  "20cm interval" = s20
+)
 
 # note special syntax for plotting step function
-xyplot(cbind(top,bottom) ~ value, groups=which, data=g2, id=g2$which,
-panel=panel.depth_function, ylim=c(250,-10), 
-scales=list(y=list(tick.number=10)), xlab='Property', 
-ylab='Depth (cm)', main='Soil Profile Aggregation by Regular Depth-slice',
-auto.key=list(columns=2, points=FALSE, lines=TRUE)
+xyplot(
+  cbind(top, bottom) ~ value,
+  groups = which,
+  data = g2,
+  id = g2$which,
+  panel = panel.depth_function,
+  ylim = c(250, -10),
+  scales = list(y = list(tick.number = 10)),
+  xlab = 'Property',
+  ylab = 'Depth (cm)',
+  main = 'Soil Profile Aggregation by Regular Depth-slice',
+  auto.key = list(
+    columns = 2,
+    points = FALSE,
+    lines = TRUE
+  )
 )
 
-
+# Example 2: sp3: 10 soil profiles from the Sierra Nevada Foothills Region of California.
+data(sp3)
 
 # 
 # 2. investigate the concept of a 'median profile'
 # note that this involves aggregation between two dissimilar groups of soils
 # 
-data(sp3)
 
 # generate a RGB version of soil colors
 # and convert to HSV for aggregation
-sp3$h <- NA ; sp3$s <- NA ; sp3$v <- NA
-sp3.rgb <- with(sp3, munsell2rgb(hue, value, chroma, return_triplets=TRUE))
-sp3[, c('h','s','v')] <- t(with(sp3.rgb, rgb2hsv(r, g, b, maxColorValue=1)))
+sp3$h <- NA
+sp3$s <- NA
+sp3$v <- NA
+sp3.rgb <- with(sp3, munsell2rgb(hue, value, chroma, return_triplets = TRUE))
+sp3[, c('h', 's', 'v')] <- t(with(sp3.rgb, rgb2hsv(r, g, b, maxColorValue = 1)))
 
 # promote to SoilProfileCollection
 depths(sp3) <- id ~ top + bottom
 
 # aggregate across entire collection
-a <- slab(sp3, fm= ~ clay + cec + ph + h + s + v, slab.structure=10)
+a <- slab(sp3,
+          fm = ~ clay + cec + ph + h + s + v,
+          slab.structure = 10)
 
 # check
 str(a)
 
 # convert back to wide format
-library(reshape)
-a.wide.q25 <- cast(a, top + bottom ~ variable, value=c('p.q25'))
-a.wide.q50 <- cast(a, top + bottom ~ variable, value=c('p.q50'))
-a.wide.q75 <- cast(a, top + bottom ~ variable, value=c('p.q75'))
+a.wide.q25 <- dcast(as.data.table(a), top + bottom ~ variable, value.var = c('p.q25'))
+a.wide.q50 <- dcast(as.data.table(a), top + bottom ~ variable, value.var = c('p.q50'))
+a.wide.q75 <- dcast(as.data.table(a), top + bottom ~ variable, value.var = c('p.q75'))
 
 # add a new id for the 25th, 50th, and 75th percentile pedons
 a.wide.q25$id <- 'Q25'
@@ -83,33 +100,38 @@ a.wide.q50$id <- 'Q50'
 a.wide.q75$id <- 'Q75'
 
 # combine original data with "mean profile"
-vars <- c('top','bottom','id','clay','cec','ph','h','s','v')
+vars <- c('id', 'top', 'bottom', 'clay', 'cec', 'ph', 'h', 's', 'v')
+
 # make data.frame version of sp3
-sp3.df <- as(sp3, 'data.frame')
-sp3.grouped <- rbind(
-sp3.df[, vars], a.wide.q25[, vars], a.wide.q50[, vars], a.wide.q75[, vars]
-)
+sp3.grouped <- as.data.frame(rbind(as.data.table(horizons(sp3))[, .SD, .SDcol = vars], 
+                     a.wide.q25[, .SD, .SDcol = vars],
+                     a.wide.q50[, .SD, .SDcol = vars], 
+                     a.wide.q75[, .SD, .SDcol = vars]))
 
 # re-constitute the soil color from HSV triplets
 # convert HSV back to standard R colors
 sp3.grouped$soil_color <- with(sp3.grouped, hsv(h, s, v))
 
 # give each horizon a name
-sp3.grouped$name <- paste(round(sp3.grouped$clay), '/' , 
-round(sp3.grouped$cec), '/', round(sp3.grouped$ph,1))
-
+sp3.grouped$name <- paste(round(sp3.grouped$clay), '/' ,
+                          round(sp3.grouped$cec), '/',
+                          round(sp3.grouped$ph, 1))
 
+plot(sp3.grouped)
 
 ## perform comparison, and convert to phylo class object
 ## D is rescaled to [0,]
-d <- profile_compare(sp3.grouped, vars=c('clay','cec','ph'), max_d=100, 
-k=0.01, replace_na=TRUE, add_soil_flag=TRUE, rescale.result=TRUE)
-
-require(cluster)
-h <- agnes(d, method='ward')
+d <- profile_compare(sp3.grouped,
+                     vars = c('clay', 'cec', 'ph'),
+                     max_d = 100,
+                     k = 0.01,
+                     replace_na = TRUE,
+                     add_soil_flag = TRUE, 
+                     rescale.result = TRUE)
+
+h <- agnes(d, method = 'ward')
 p <- ladderize(as.phylo(as.hclust(h)))
 
-
 # look at distance plot-- just the median profile
 plot_distance_graph(d, 12)
 
@@ -121,9 +143,14 @@ round(1 - (as.matrix(d)[12, ] / max(as.matrix(d)[12, ])), 2)
 depths(sp3.grouped) <- id ~ top + bottom
 
 # setup plot: note that D has a scale of [0,1]
-par(mar=c(1,1,1,1))
-p.plot <- plot(p, cex=0.8, label.offset=3, direction='up', y.lim=c(2,0), 
-x.lim=c(1.25,length(sp3.grouped)+1), show.tip.label=FALSE)
+par(mar = c(1, 1, 1, 1))
+p.plot <- plot(p,
+               cex = 0.8,
+               label.offset = 3,
+               direction = 'up',
+               y.lim = c(2, 0),
+               x.lim = c(1.25, length(sp3.grouped) + 1),
+               show.tip.label = FALSE)
 
 # get the last plot geometry
 lastPP <- get("last_plot.phylo", envir = .PlotPhyloEnv)
@@ -132,10 +159,18 @@ lastPP <- get("last_plot.phylo", envir = .PlotPhyloEnv)
 d.labels <- attr(d, 'Labels')
 
 new_order <- sapply(1:lastPP$Ntip,
-function(i) which(as.integer(lastPP$xx[1:lastPP$Ntip]) == i))
+                    function(i)
+                      which(as.integer(lastPP$xx[1:lastPP$Ntip]) == i))
 
 # plot the profiles, in the ordering defined by the dendrogram
 # with a couple fudge factors to make them fit
-plot(sp3.grouped, color="soil_color", plot.order=new_order,
-scaling.factor=0.01, width=0.1, cex.names=0.5,
-y.offset=max(lastPP$yy)+0.1, add=TRUE)
+plotSPC(
+  sp3.grouped,
+  color = "soil_color",
+  plot.order = new_order,
+  scaling.factor = 0.01,
+  width = 0.1,
+  cex.names = 0.5,
+  y.offset = max(lastPP$yy) + 0.1,
+  add = TRUE
+)