Slice bivariate densities, or the Joy Division “waterfall plot”

October 8, 2014
By

(This article was first published on Robert Grant's stats blog » R, and kindly contributed to R-bloggers)

This has been on my to-do list for a long old time. Lining up slices through a bivariate density seems a much more intuitive way of depicting it than contour plots or some ghastly rotating 3-D thing (urgh). Of course, there is the danger of features being hidden, but you know I’m a semi-transparency nut, so it’s no surprise I think that’s the answer to this too.

slicedens

Here’s an R function for you:

# x, y: data

# slices: number of horizontal slices through the data
# lboost: coefficient to increase the height of the lines
# gboost: coefficient to increase the height of the graph (ylim)
# xinc: horizontal offset for each succesive slice
# (typically something like 1/80)
# yinc: vertical offset for each succesive slice
# bcol: background color
# fcol: fill color for each slice (polygon)
# lcol: line color for each slice
# lwidth: line width
# NB if you want to cycle slice colors through vectors, you
# need to change the function code; it sounds like a
# pretty bad idea to me, but each to their own.

slicedens<-function(x,y,slices=50,lboost=1,gboost=1,xinc=0,yinc=0.01,
bcol="black",fcol="black",lcol="white",lwidth=1) {
ycut<-min(y)+((0:(slices))*(max(y)-min(y))/slices)
height<-gboost*((slices*yinc)+max(density(x)$y))
plot( c(min(x),max(x)+((max(x)-min(x))/4)),
c(0,height),
xaxt="n",yaxt="n",ylab="",xlab="")
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col=bcol)
for(i in slices:1) {
miny<-ycut[i]
maxy<-ycut[i+1]
gx<-(i-1)*(max(x)-min(x))*xinc
gy<-(i-1)*(height)*yinc
dd<-density(x[y>=miny & y

Some places call this a waterfall plot. Anyway, the white-on-black color scheme is clearly inspired by the Joy Division album cover. Enjoy.

To leave a comment for the author, please follow the link and comment on their blog: Robert Grant's stats blog » R.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)