Question

I would like to rotate a histogram in R, plotted by hist(). The question is not new, and in several forums I have found that it is not possible. However, all these answers date back to 2010 or even later.

Has anyone found a solution meanwhile?

One way to get around the problem is to plot the histogram via barplot() that offers the option “horiz=TRUE”. The plot works fine but I fail to overlay a density in the barplots. The problem probably lies in the x-axis since in the vertical plot, the density is centered in the first bin, while in the horizontal plot the density curve is messed up.

Any help is very much appreciated!

Thanks,

Niels

Code:

require(MASS)
Sigma <- matrix(c(2.25, 0.8, 0.8, 1), 2, 2)
mvnorm <- mvrnorm(1000, c(0,0), Sigma)

scatterHist.Norm <- function(x,y) {
 zones <- matrix(c(2,0,1,3), ncol=2, byrow=TRUE)
 layout(zones, widths=c(2/3,1/3), heights=c(1/3,2/3))
 xrange <- range(x) ; yrange <- range(y)
 par(mar=c(3,3,1,1))
 plot(x, y, xlim=xrange, ylim=yrange, xlab="", ylab="", cex=0.5)
 xhist <- hist(x, plot=FALSE, breaks=seq(from=min(x), to=max(x), length.out=20))
 yhist <- hist(y, plot=FALSE, breaks=seq(from=min(y), to=max(y), length.out=20))
 top <- max(c(xhist$counts, yhist$counts))
 par(mar=c(0,3,1,1))
 plot(xhist, axes=FALSE, ylim=c(0,top), main="", col="grey")
 x.xfit <- seq(min(x),max(x),length.out=40)
 x.yfit <- dnorm(x.xfit,mean=mean(x),sd=sd(x))
 x.yfit <- x.yfit*diff(xhist$mids[1:2])*length(x)
 lines(x.xfit, x.yfit, col="red")
 par(mar=c(0,3,1,1))
 plot(yhist, axes=FALSE, ylim=c(0,top), main="", col="grey", horiz=TRUE)
 y.xfit <- seq(min(x),max(x),length.out=40)
 y.yfit <- dnorm(y.xfit,mean=mean(x),sd=sd(x))
 y.yfit <- y.yfit*diff(yhist$mids[1:2])*length(x)
 lines(y.xfit, y.yfit, col="red")
}
scatterHist.Norm(mvnorm[,1], mvnorm[,2])


scatterBar.Norm <- function(x,y) {
 zones <- matrix(c(2,0,1,3), ncol=2, byrow=TRUE)
 layout(zones, widths=c(2/3,1/3), heights=c(1/3,2/3))
 xrange <- range(x) ; yrange <- range(y)
 par(mar=c(3,3,1,1))
 plot(x, y, xlim=xrange, ylim=yrange, xlab="", ylab="", cex=0.5)
 xhist <- hist(x, plot=FALSE, breaks=seq(from=min(x), to=max(x), length.out=20))
 yhist <- hist(y, plot=FALSE, breaks=seq(from=min(y), to=max(y), length.out=20))
 top <- max(c(xhist$counts, yhist$counts))
 par(mar=c(0,3,1,1))
 barplot(xhist$counts, axes=FALSE, ylim=c(0, top), space=0)
 x.xfit <- seq(min(x),max(x),length.out=40)
 x.yfit <- dnorm(x.xfit,mean=mean(x),sd=sd(x))
 x.yfit <- x.yfit*diff(xhist$mids[1:2])*length(x)
 lines(x.xfit, x.yfit, col="red")
 par(mar=c(3,0,1,1))
 barplot(yhist$counts, axes=FALSE, xlim=c(0, top), space=0, horiz=TRUE)
 y.xfit <- seq(min(x),max(x),length.out=40)
 y.yfit <- dnorm(y.xfit,mean=mean(x),sd=sd(x))
 y.yfit <- y.yfit*diff(yhist$mids[1:2])*length(x)
 lines(y.xfit, y.yfit, col="red")
}
scatterBar.Norm(mvnorm[,1], mvnorm[,2])

#

Source of scatter plot with marginal histograms (click first link after “adapted from…”):

http://r.789695.n4.nabble.com/newbie-scatterplot-with-marginal-histograms-done-and-axes-labels-td872589.html

Source of density in a scatter plot:

http://www.statmethods.net/graphs/density.html

Answer 1

scatterBarNorm <- function(x, dcol="blue", lhist=20, num.dnorm=5*lhist, ...){
    ## check input
    stopifnot(ncol(x)==2)
    ## set up layout and graphical parameters
    layMat <- matrix(c(2,0,1,3), ncol=2, byrow=TRUE)
    layout(layMat, widths=c(5/7, 2/7), heights=c(2/7, 5/7))
    ospc <- 0.5 # outer space
    pext <- 4 # par extension down and to the left
    bspc <- 1 # space between scatter plot and bar plots
    par. <- par(mar=c(pext, pext, bspc, bspc),
                oma=rep(ospc, 4)) # plot parameters
    ## scatter plot
    plot(x, xlim=range(x[,1]), ylim=range(x[,2]), ...)
    ## 3) determine barplot and height parameter
    ## histogram (for barplot-ting the density)
    xhist <- hist(x[,1], plot=FALSE, breaks=seq(from=min(x[,1]), to=max(x[,1]),
                                     length.out=lhist))
    yhist <- hist(x[,2], plot=FALSE, breaks=seq(from=min(x[,2]), to=max(x[,2]),
                                     length.out=lhist)) # note: this uses probability=TRUE
    ## determine the plot range and all the things needed for the barplots and lines
    xx <- seq(min(x[,1]), max(x[,1]), length.out=num.dnorm) # evaluation points for the overlaid density
    xy <- dnorm(xx, mean=mean(x[,1]), sd=sd(x[,1])) # density points
    yx <- seq(min(x[,2]), max(x[,2]), length.out=num.dnorm)
    yy <- dnorm(yx, mean=mean(x[,2]), sd=sd(x[,2]))
    ## barplot and line for x (top)
    par(mar=c(0, pext, 0, 0))
    barplot(xhist$density, axes=FALSE, ylim=c(0, max(xhist$density, xy)),
            space=0) # barplot
    lines(seq(from=0, to=lhist-1, length.out=num.dnorm), xy, col=dcol) # line
    ## barplot and line for y (right)
    par(mar=c(pext, 0, 0, 0))
    barplot(yhist$density, axes=FALSE, xlim=c(0, max(yhist$density, yy)),
            space=0, horiz=TRUE) # barplot
    lines(yy, seq(from=0, to=lhist-1, length.out=num.dnorm), col=dcol) # line
    ## restore parameters
    par(par.)
}

require(mvtnorm)
X <- rmvnorm(1000, c(0,0), matrix(c(1, 0.8, 0.8, 1), 2, 2))
scatterBarNorm(X, xlab=expression(italic(X[1])), ylab=expression(italic(X[2])))