I tried to plot with barplot,when hist(breaks=200),barplot can in different color.But when hist(breaks=1000)cannot change color in barplot. And why ylab didn't show in the barplot when combine three plots?It's OK in single plot).
breaks=200
breaks=1000
Here is my code:breaks=1000，cannot change color.
def.par <- par(no.readonly = TRUE) # save default, for resetting...
x <- pmin(3, pmax(-3, rnorm(5000)))
y <- pmin(3, pmax(-3, rnorm(5000)))
xhist <- hist(x, breaks=1000, plot=FALSE)
yhist <- hist(y, breaks=1000,plot=FALSE)
top <- max(c(xhist$density, yhist$density))
xrange <- c(-3,3)
yrange <- c(-3,3)
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE)#layout.show(nf)ZSSS
par(mar=c(3,3,0,0))
plot(x, y, xlim=xrange, ylim=yrange, xlab="T1", ylab="T2")
par(mar=c(0,3,1,1))
barplot(xhist$density, axes=TRUE, ylim=c(0, top),ylab="T3",col="red")
par(mar=c(3,0,1,1))
barplot(yhist$density, axes=TRUE, xlim=c(0, top),xlab="T4",space=0, horiz=TRUE,col="green")
par(def.par)
Just change color of the border:
def.par <- par(no.readonly = TRUE) # save default, for resetting...
x <- pmin(3, pmax(-3, rnorm(5000)))
y <- pmin(3, pmax(-3, rnorm(5000)))
xhist <- hist(x, breaks=100, plot=FALSE)
yhist <- hist(y, breaks=100,plot=FALSE)
top <- max(c(xhist$density, yhist$density))
xrange <- c(-3,3)
yrange <- c(-3,3)
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3),TRUE)#layout.show(nf)ZSSS
par(mar=c(3,3,0,0))
plot(x, y, xlim=xrange, ylim=yrange, xlab="T1", ylab="T2")
par(mar=c(0,3,1,1))
barplot(xhist$density, axes=TRUE, ylim=c(0, top),ylab="T3",col="red", border = "red")
par(mar=c(3,0,1,1))
barplot(yhist$density, axes=TRUE, xlim=c(0, top),xlab="T4",space=0, horiz=TRUE,col="green", border = "green")
I borrowed this code from statmethods[dot]net. The result is a coloured area under a normal distribution.
mean=100; sd=15
lb=80; ub=120
x <- seq(-4,4,length=100)*sd + mean
hx <- dnorm(x,mean,sd)
plot(x, hx, type="n", xlab="IQ Values", ylab="Density",
main="Normal Distribution", axes=FALSE)
i <- x >= lb & x <= ub
lines(x, hx)
polygon(c(lb,x[i],ub), c(0,hx[i],0), col="red")
area <- pnorm(ub, mean, sd) - pnorm(lb, mean, sd)
result <- paste("P(",lb,"< IQ <",ub,") =",
signif(area, digits=3))
mtext(result,2)
I was wondering if it is possible to select an image as the colour of the red polygon?
Many thanks!
Working with your code, I basically suggest plotting an image (I used a random image below) using the png package (based on advice from In R, how to plot with a png as background?) and then plot your lines over that.
mean=100; sd=15
lb=80; ub=120
x <- seq(-4,4,length=100)*sd + mean
hx <- dnorm(x,mean,sd)
# load package and an image
library(png)
ima <- readPNG("Red_Hot_Sun.PNG")
# plot an empty plot with your labels, etc.
plot(1,xlim=c(min(x),max(x)), type="n", xlab="IQ Values", ylab="Density",
main="Normal Distribution", axes=FALSE)
# put in the image
lim <- par()
rasterImage(ima, lim$usr[1], lim$usr[3], lim$usr[2], lim$usr[4])
# add your plot
par(new=TRUE)
plot(x, hx, xlim=c(min(x),max(x)), type="l", xlab="", ylab="", axes=FALSE)
i <- x >= lb & x <= ub
lines(x, hx)
# add a polygon to cover the background above plot
polygon(c(x,180,180,20,20), c(hx,0,1,1,0), col="white")
# add polygons to cover the areas under the plot you don't want visible
polygon(c(-20,-20,x[x<=lb],lb), c(-10,min(hx),hx[x<=lb],-10), col="white")
polygon(c(ub,x[x>=ub],200,200), c(-1,hx[x>=ub],min(hx),-1), col="white")
# add your extra text
area <- pnorm(ub, mean, sd) - pnorm(lb, mean, sd)
result <- paste("P(",lb,"< IQ <",ub,") =",
signif(area, digits=3))
mtext(result,2)
Gives you:
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
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])))
It may be helpful to know that the hist() function invisibly returns all the information that you need to reproduce what it does using simpler plotting functions, like rect().
vals <- rnorm(10)
A <- hist(vals)
A
$breaks
[1] -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
$counts
[1] 1 3 3 1 1 1
$intensities
[1] 0.2 0.6 0.6 0.2 0.2 0.2
$density
[1] 0.2 0.6 0.6 0.2 0.2 0.2
$mids
[1] -1.25 -0.75 -0.25 0.25 0.75 1.25
$xname
[1] "vals"
$equidist
[1] TRUE
attr(,"class")
[1] "histogram"
You can create the same histogram manually like this:
plot(NULL, type = "n", ylim = c(0,max(A$counts)), xlim = c(range(A$breaks)))
rect(A$breaks[1:(length(A$breaks) - 1)], 0, A$breaks[2:length(A$breaks)], A$counts)
With those parts, you can flip the axes however you like:
plot(NULL, type = "n", xlim = c(0, max(A$counts)), ylim = c(range(A$breaks)))
rect(0, A$breaks[1:(length(A$breaks) - 1)], A$counts, A$breaks[2:length(A$breaks)])
For similar do-it-yourselfing with density(), see:
Axis-labeling in R histogram and density plots; multiple overlays of density plots
I'm not sure whether it is of interest, but I sometimes want to use horizontal histograms without any packages and be able to write or draw at any position of the graphic.
That's why I wrote the following function, with examples provided below. If anyone knows a package to which this would fit well, please write me: berry-b at gmx.de
Please be sure not to have a variable hpos in your workspace, as it will be overwritten with a function. (Yes, for a package I would need to insert some safety parts in the function).
horiz.hist <- function(Data, breaks="Sturges", col="transparent", las=1,
ylim=range(HBreaks), labelat=pretty(ylim), labels=labelat, border=par("fg"), ... )
{a <- hist(Data, plot=FALSE, breaks=breaks)
HBreaks <- a$breaks
HBreak1 <- a$breaks[1]
hpos <<- function(Pos) (Pos-HBreak1)*(length(HBreaks)-1)/ diff(range(HBreaks))
barplot(a$counts, space=0, horiz=T, ylim=hpos(ylim), col=col, border=border,...)
axis(2, at=hpos(labelat), labels=labels, las=las, ...)
print("use hpos() to address y-coordinates") }
For examples
# Data and basic concept
set.seed(8); ExampleData <- rnorm(50,8,5)+5
hist(ExampleData)
horiz.hist(ExampleData, xlab="absolute frequency")
# Caution: the labels at the y-axis are not the real coordinates!
# abline(h=2) will draw above the second bar, not at the label value 2. Use hpos:
abline(h=hpos(11), col=2)
# Further arguments
horiz.hist(ExampleData, xlim=c(-8,20))
horiz.hist(ExampleData, main="the ... argument worked!", col.axis=3)
hist(ExampleData, xlim=c(-10,40)) # with xlim
horiz.hist(ExampleData, ylim=c(-10,40), border="red") # with ylim
horiz.hist(ExampleData, breaks=20, col="orange")
axis(2, hpos(0:10), labels=F, col=2) # another use of hpos()
One shortcoming: the function doesn't work with breakpoints provided as a vector with different widths of the bars.
Thank you, Tim and Paul. You made me think harder and use what hist() actually provides.
This is my solution now (with great help from Alex Pl.):
scatterBar.Norm <- function(x,y) {
zones <- matrix(c(2,0,1,3), ncol=2, byrow=TRUE)
layout(zones, widths=c(5/7,2/7), heights=c(2/7,5/7))
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$density, yhist$density))
par(mar=c(0,3,1,1))
barplot(xhist$density, axes=FALSE, ylim=c(0, top), space=0)
x.xfit <- seq(min(x),max(x),length.out=40)
x.yfit <- dnorm(x.xfit, mean=mean(x), sd=sd(x))
x.xscalefactor <- x.xfit / seq(from=0, to=19, length.out=40)
lines(x.xfit/x.xscalefactor, x.yfit, col="red")
par(mar=c(3,0,1,1))
barplot(yhist$density, axes=FALSE, xlim=c(0, top), space=0, horiz=TRUE)
y.xfit <- seq(min(y),max(y),length.out=40)
y.yfit <- dnorm(y.xfit, mean=mean(y), sd=sd(y))
y.xscalefactor <- y.xfit / seq(from=0, to=19, length.out=40)
lines(y.yfit, y.xfit/y.xscalefactor, col="red")
}
For examples:
require(MASS)
#Sigma <- matrix(c(2.25, 0.8, 0.8, 1), 2, 2)
Sigma <- matrix(c(1, 0.8, 0.8, 1), 2, 2)
mvnorm <- mvrnorm(1000, c(0,0), Sigma) ; scatterBar.Norm(mvnorm[,1], mvnorm[,2])
An asymmetric Sigma leads to a somewhat bulkier histogram of the respective axis.
The code is left deliberately "unelegant" in order to increase comprehensibility (for myself when I revisit it later...).
Niels
When using ggplot, flipping axes works very well. See for example this example which shows how to do this for a boxplot, but it works equally well for a histogram I assume. In ggplot one can quite easily overlay different plot types, or geometries in ggplot2 jargon. So combining a density plot and a histogram should be easy.