How to fill in the contour fully using stat_contour - r

Question

I am looking for ways to fully fill in the contour generated by ggplot2's stat_contour. The current result is like this:
# Generate data
library(ggplot2)
library(reshape2) # for melt
volcano3d <- melt(volcano)
names(volcano3d) <- c("x", "y", "z")
v <- ggplot(volcano3d, aes(x, y, z = z))
v + stat_contour(geom="polygon", aes(fill=..level..))
The desired result can be produced by manually modifying the codes as follows.
v + stat_contour(geom="polygon", aes(fill=..level..)) +
theme(panel.grid=element_blank())+ # delete grid lines
scale_x_continuous(limits=c(min(volcano3d$x),max(volcano3d$x)), expand=c(0,0))+ # set x limits
scale_y_continuous(limits=c(min(volcano3d$y),max(volcano3d$y)), expand=c(0,0))+ # set y limits
theme(panel.background=element_rect(fill="#132B43")) # color background
My question: is there a way to fully fill the plot without manually specifying the color or using geom_tile()?

Answer 1

As #tonytonov has suggested this thread, the transparent areas can be deleted by closing the polygons.
# check x and y grid
minValue<-sapply(volcano3d,min)
maxValue<-sapply(volcano3d,max)
arbitaryValue=min(volcano3d$z-10)
test1<-data.frame(x=minValue[1]-1,y=minValue[2]:maxValue[2],z=arbitaryValue)
test2<-data.frame(x=minValue[1]:maxValue[1],y=minValue[2]-1,z=arbitaryValue)
test3<-data.frame(x=maxValue[1]+1,y=minValue[2]:maxValue[2],z=arbitaryValue)
test4<-data.frame(x=minValue[1]:maxValue[1],y=maxValue[2]+1,z=arbitaryValue)
test<-rbind(test1,test2,test3,test4)
vol<-rbind(volcano3d,test)
w <- ggplot(vol, aes(x, y, z = z))
w + stat_contour(geom="polygon", aes(fill=..level..)) # better
# Doesn't work when trying to get rid of unwanted space
w + stat_contour(geom="polygon", aes(fill=..level..))+
scale_x_continuous(limits=c(min(volcano3d$x),max(volcano3d$x)), expand=c(0,0))+ # set x limits
scale_y_continuous(limits=c(min(volcano3d$y),max(volcano3d$y)), expand=c(0,0)) # set y limits
# work here!
w + stat_contour(geom="polygon", aes(fill=..level..))+
coord_cartesian(xlim=c(min(volcano3d$x),max(volcano3d$x)),
ylim=c(min(volcano3d$y),max(volcano3d$y)))
The problem remained with this tweak is finding methods aside from trial and error to determine the arbitaryValue.
[edit from here]
Just a quick update to show how I am determining the arbitaryValue without having to guess for every datasets.
BINS<-50
BINWIDTH<-(diff(range(volcano3d$z))/BINS) # reference from ggplot2 code
arbitaryValue=min(volcano3d$z)-BINWIDTH*1.5
This seems to work well for the dataset I am working on now. Not sure if applicable with others. Also, note that the fact that I set BINS value here requires that I will have to use bins=BINS in stat_contour.

Answer 2

Thanks for #chengvt's answer. I sometimes needs this technique, so I made a generalized function().
test_f <- function(df) {
colname <- names(df)
names(df) <- c("x", "y", "z")
Range <- as.data.frame(sapply(df, range))
Dim <- as.data.frame(t(sapply(df, function(x) length(unique(x)))))
arb_z = Range$z[1] - diff(Range$z)/20
df2 <- rbind(df,
expand.grid(x = c(Range$x[1] - diff(Range$x)/20, Range$x[2] + diff(Range$x)/20),
y = seq(Range$y[1], Range$y[2], length = Dim$y), z = arb_z),
expand.grid(x = seq(Range$x[1], Range$x[2], length = Dim$x),
y = c(Range$y[1] - diff(Range$y)/20, Range$y[2] + diff(Range$y)/20), z = arb_z))
g <- ggplot(df2, aes(x, y, z = z)) + labs(x = colname[1], y = colname[2], fill = colname[3]) +
stat_contour(geom="polygon", aes(fill=..level..)) +
coord_cartesian(xlim=c(Range$x), ylim=c(Range$y), expand = F)
return(g)
}
library(ggplot2); library(reshape2)
volcano3d <- melt(volcano)
names(volcano3d) <- c("xxx", "yyy", "zzz")
test_f(volcano3d) + scale_fill_gradientn(colours = terrain.colors(10))