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I'm trying to plot half circles using R. My final aim is to draw a circle, divided in the middle by color. The only way I have found yet is to draw two half-circles with different colors.
So I have created my own functions:
upper.half.circle <- function(x,y,r,nsteps=100,...){
rs <- seq(0,pi,len=nsteps)
xc <- x+r*cos(rs)
yc <- y+r*sin(rs)
polygon(xc,yc,...)
}
lower.half.circle <- function(x,y,r,nsteps=100,...){
rs <- seq(0,pi,len=nsteps)
xc <- x-r*cos(rs)
yc <- y-r*sin(rs)
polygon(xc,yc,...)
}
However, for some reason my half-circles end up more like half-ellipses. For example, try running:
plot(1, type="n",axes=F,xlab="", ylab="",xlim=c(0,200),ylim=c(0,200))
upper.half.circle(15,170,10,nsteps=1000,col='red')
Does anyone know why I'm having this trouble, or alternatively, knows of a better way to do what I want?
Thanks!
The problem is the default aspect ratio is not 1:1.
To fix this, set asp=1 in plot:
Inspired by this Q & A. You could have sniffed out this was the case by turning on the axes and x/y labels.
If using the grid package would be also an opportunity for you, there is a much simpler solution:
library(grid)
vp <- viewport(width=0.5, height=0.5, clip = "on")
grid.circle(0.5,0,r=0.5, gp = gpar(fill = 'red'), vp = vp)
This creates a viewport with clipping, i.e., an appropriate positioning of the filled circle creates a half circle.
If you want to add your half circles to an existing plot (and therefore cannot control the aspect ratio directly) then one option for this specific case is to use the floating.pie function from the plotrix package.
A more general tool for creating custom symbols and adding them to plots (with the symbols having a different aspect ratio from the overall plot) is to use the my.symbols function from the TeachingDemos package.
I generate a plot using the package hexbin:
# install.packages("hexbin", dependencies=T)
library(hexbin)
set.seed(1234)
x <- rnorm(1e6)
y <- rnorm(1e6)
hbin <- hexbin(
x = x
, y = y
, xbin = 50
, xlab = expression(alpha)
, ylab = expression(beta)
)
## Using plot method for hexbin objects:
plot(hbin, style = "nested.lattice")
abline(h=0)
This seems to generate an S4 object (hbin), which I then plot using plot.
Now I'd like to add a horizontal line to that plot using abline, but unfortunately this gives the error:
plot.new has not yet been called
I have also no idea, how I can manipulate e.g. the position of the axis labels (alpha and beta are within the numbers), change the position of the legend, etc.
I'm familiar with OOP, but so far I could not find out how plot() handles the object (does it call certain methods of the object?) and how I can manipulate the resulting plot.
Why can't I simply draw a line onto the plot?
How can I manipulate axis labels?
Use lattice version of hex bin - hexbinplot(). With panel you can add your line, and with style you can choose different ways of visualizing hexagons. Check help for hexbinplot for more.
library(hexbin)
library(lattice)
x <- rnorm(1e6)
y <- rnorm(1e6)
hexbinplot(x ~ y, aspect = 1, bins=50,
xlab = expression(alpha), ylab = expression(beta),
style = "nested.centroids",
panel = function(...) {
panel.hexbinplot(...)
panel.abline(h=0)
})
hexbin uses grid graphics, not base. There is a similar function, grid.abline, which can draw lines on plots by specifying a slope and intercept, but the co-ordinate system used is confusing:
grid.abline(325,0)
gets approximately what you want, but the intercept here was found by eye.
You will have more luck using ggplot2:
library(ggplot2)
ggplot(data,aes(x=alpha,y=beta)) + geom_hex(bins=10) + geom_hline(yintercept=0.5)
I had a lot of trouble finding a lot of basic plot adjustments (axis ranges, labels, etc.) with the hexbin library but I figured out how to export the points into any other plotting function:
hxb<-hexbin(x=c(-15,-15,75,75),
y=c(-15,-15,75,75),
xbins=12)
hxb#xcm #gives the x co-ordinates of each hex tile
hxb#ycm #gives the y co-ordinates of each hex tile
hxb#count #gives the cell size for each hex tile
points(x=hxb#xcm, y=hxb#ycm, pch=hxb#count)
You can just feed these three vectors into any plotting tool you normally use.. there is the usual tweaking of size scaling, etc. but it's far better than the stubborn hexplot function. The problem I found with the ggplot2 stat_binhex is that I couldn't get the hexes to be different sizes... just different colors.
if you really want hexagons, plotrix has a hexagon drawing function that i think is fine.
I've run a 2d simulation in some modelling software from which i've got an export of x,y point locations with a set of 6 attributes. I wish to recreate a figure that combines the data, like this:
The ellipses and the background are shaded according to attribute 1 (and the borders of these are of course representing the model geometry, but I don't think I can replicate that), the isolines are contours of attribute 2, and the arrow glyphs are from attributes 3 (x magnitude) and 4 (y magnitude).
The x,y points are centres of the triangulated mesh I think, and look like this:
I want to know how I can recreate a plot like this with R. To start with I have irregularly-spaced data due to it being exported from an irregular mesh. That's immediately where I get stuck with R, having only ever used it for producing box-and-whisper plots and the like.
Here's the data:
https://dl.dropbox.com/u/22417033/Ellipses_noheader.txt
Edit: fields: x, y, heat flux (x), heat flux (y), thermal conductivity, Temperature, gradT (x), gradT (y).
names(Ellipses) <- c('x','y','dfluxx','dfluxy','kxx','Temps','gradTx','gradTy')
It's quite easy to make the lower plot (making the assumption that there is a dataframe named 'edat' read in with:
edat <- read.table(file=file.choose())
with(edat, plot(V1,V2), cex=0.2)
Things get a bit more beautiful with:
with(edat, plot(V1,V2, cex=0.2, col=V5))
So I do not think your original is being faithfully represented by the data. The contour lines are NOT straight across the "conductors". I call them "conductors" because this looks somewhat like iso-potential lines in electrostatics. I'm adding some text here to serve as a search handle for others who might be searching for plotting problems in real world physics: vector-field (the arrows) , heat equations, gradient, potential lines.
You can then overlay the vector field with:
with(edat, arrows(V1,V2, V1-20*V6*V7, V2-20*V6*V8, length=0.04, col="orange") )
You could"zoom in" with xlim and ylim:
with(edat, plot(V1,V2, cex=0.3, col=V5, xlim=c(0, 10000), ylim=c(-8000, -2000) ))
with(edat, arrows(V1,V2, V1-20*V6*V7, V2-20*V6*V8, length=0.04, col="orange") )
Guessing that the contour requested if for the Temps variable. Take your pick of contourplots.
require(akima)
intflow<- with(edat, interp(x=x, y=y, z=Temps, xo=seq(min(x), max(x), length = 410),
yo=seq(min(y), max(y), length = 410), duplicate="mean", linear=FALSE) )
require(lattice)
contourplot(intflow$z)
filled.contour(intflow)
with( intflow, contour(x=x, y=y, z=z) )
The last one will mix with the other plotting examples since those were using base plotting functions. You may need to switch to points instead of plot.
There are several parts to your plot so you will probably need several tools to make the different parts.
The background and ellipses can be created with polygon (once you figure where they should be).
The contourLines function can calculate the contour lines for you which you can add with the lines function (or contour has and add argument and could probably be used to add the lines directly).
The akima package has a function interp which can estimate values on a grid given the values ungridded.
The my.symbols function along with ms.arrows, both from the TeachingDemos package, can be used to draw the vector field.
#DWin is right to say that your graph don't represent faithfully your data, so I would advice to follow his answer. However here is how to reproduce (the closest I could) your graph:
Ellipses <- read.table(file.choose())
names(Ellipses) <- c('x','y','dfluxx','dfluxy','kxx','Temps','gradTx','gradTy')
require(splancs)
require(akima)
First preparing the data:
#First the background layer (the 'kxx' layer):
# Here the regular grid on which we're gonna do the interpolation
E.grid <- with(Ellipses,
expand.grid(seq(min(x),max(x),length=200),
seq(min(y),max(y),length=200)))
names(E.grid) <- c("x","y") # Without this step, function inout throws an error
E.grid$Value <- rep(0,nrow(E.grid))
#Split the dataset according to unique values of kxx
E.k <- split(Ellipses,Ellipses$kxx)
# Find the convex hull delimiting each of those values domain
E.k.ch <- lapply(E.k,function(X){X[chull(X$x,X$y),]})
for(i in unique(Ellipses$kxx)){ # Pick the value for each coordinate in our regular grid
E.grid$Value[inout(E.grid[,1:2],E.k.ch[names(E.k.ch)==i][[1]],bound=TRUE)]<-i
}
# Then the regular grid for the second layer (Temp)
T.grid <- with(Ellipses,
interp(x,y,Temps, xo=seq(min(x),max(x),length=200),
yo=seq(min(y),max(y),length=200),
duplicate="mean", linear=FALSE))
# The regular grids for the arrow layer (gradT)
dx <- with(Ellipses,
interp(x,y,gradTx,xo=seq(min(x),max(x),length=15),
yo=seq(min(y),max(y),length=10),
duplicate="mean", linear=FALSE))
dy <- with(Ellipses,
interp(x,y,gradTy,xo=seq(min(x),max(x),length=15),
yo=seq(min(y),max(y),length=10),
duplicate="mean", linear=FALSE))
T.grid2 <- with(Ellipses,
interp(x,y,Temps, xo=seq(min(x),max(x),length=15),
yo=seq(min(y),max(y),length=10),
duplicate="mean", linear=FALSE))
gradTgrid<-expand.grid(dx$x,dx$y)
And then the plotting:
palette(grey(seq(0.5,0.9,length=5)))
par(mar=rep(0,4))
plot(E.grid$x, E.grid$y, col=E.grid$Value,
axes=F, xaxs="i", yaxs="i", pch=19)
contour(T.grid, add=TRUE, col=colorRampPalette(c("blue","red"))(15), drawlabels=FALSE)
arrows(gradTgrid[,1], gradTgrid[,2], # Here I multiply the values so you can see them
gradTgrid[,1]-dx$z*40*T.grid2$z, gradTgrid[,2]-dy$z*40*T.grid2$z,
col="yellow", length=0.05)
To understand in details how this code works, I advise you to read the following help pages: ?inout, ?chull, ?interp, ?expand.grid and ?contour.
I am having problems getting segments of small lengths to appear in my plot.
Assuming the following sample data:
x=c(11,22,33,44,55)
y=c(15,23,33,45,57)
z=strptime(20120101:20120105,'%Y%m%d')
If I were to create segments out of this data my segment for the third record does not show up if I want square or butt line ends. It does show up if I allow my line ends to be round lend=0.
plot(z,x,type='n')
segments(as.numeric(z),x,as.numeric(z),y,lwd=5,lend=2)
If I try this:
segments(as.numeric(z),x,as.numeric(z),y,lwd=5,lend=0)
It shows a circle at 33. Is there a way to get at the very least a flat line that will appear at 33 (hopefully in base)?
I would have used my actual data which is also doing this when the range is small for instance 33.0005 to 33.0010, but that data is huge and I was hoping solving for when they are identical would also solve for small ranges.
ETA: If lwd=15 the circle looks even more ridiculous.
Maybe segments are not the right way to approach this?
This is for a candlestick chart, so these numbers would represent open and close. I also have high and low numbers which extend beyond this range and are drawn using lwd=1 under these segments.
As #Joran points out, this may well be the "correct" behaviour.
But a kludgy workaround is to simply add an arbitrary small number to the values. This value should be small enough to not "distort" the data, but large enough to show up in your plot, given your plot device resolution.
delta <- pmax(0.2, y - x)
plot(z,x,type='n')
segments(as.numeric(z),x ,y1 = y + delta, lwd=10, lend=1)
PS. I advise against this. You have been warned.
Base graphics does supply rect. And in fact, it does what you want. Using your definitions above.
xdiff <- max(as.numeric(z)) - min(as.numeric(z))
segwidth <- xdiff/50
plot(z,x,type='n')
rect(z-segwidth/2, x, z+segwidth/2, y, col="black")
Given the edits to your question, I suspect the way to go about this is to plot points to indicate your open and close, and a segment to indicate the range.
In this way, if your open and close points are identical (or close), you get a symbol at the correct point.
x <- strptime(20120101:20120105,'%Y%m%d')
y1 <- c(11,22,33,44,55)
y2 <- c(15,23,33,45,57)
r <- range(c(y1, y2))
plot(c(x, x), c(y1, y2), type="n", xlab="Date", ylab="y")
points(x, y1, pch=18)
points(x, y2, pch=18)
segments(as.numeric(x), y0=y1, y1=y2)
There's something a little odd about "square" lineend
library(grid)
epsilon <- 1e-4
grid.newpage()
grid.points(x=c(0.5-epsilon,0.5+epsilon), y=c(0.5,0.5), pch="+", gp=gpar(cex=2), def="npc")
grid.segments(0.5-epsilon, 0.5, 0.5+epsilon, 0.5, gp=gpar(lineend="square",lwd=50, alpha=0.2))
grid.segments(0.5-epsilon, 0.5, 0.5+epsilon, 0.5, gp=gpar(lineend="round",lwd=50, alpha=0.2))
grid.segments(0.5-epsilon, 0.5, 0.5+epsilon, 0.5, gp=gpar(lineend="butt",lwd=50, alpha=0.2))
the behavior has a jump at epsilon = 0,
for epsilon=1e-4 vs
for epsilon=0
As a workaround, I would draw rectangles instead of lines; they always have at least one linewidth.
grid.newpage()
grid.rect(x=0.5, y=0.5, width=0.01, height=0, gp=gpar(fill="black", col="red", lwd=10, linejoin="mitre"))
I want to plot a barplot of some data with some x-axis labels but so far I just keep running into the same problem, as the axis scaling is completely off limits and therefore my labels are wrongly positioned below the bars.
The most simple example I can think of:
x = c(1:81)
barplot(x)
axis(side=1,at=c(0,20,40,60,80),labels=c(20,40,60,80,100))
As you can see, the x-axis does not stretch along the whole plot but stops somewhere in between. It seems to me as if the problem is quite simple, but I somehow I am not able to fix it and I could not find any solution so far :(
Any help is greatly appreciated.
The problem is that barplot is really designed for plotting categorical, not numeric data, and as such it pretty much does its own thing in terms of setting up the horizontal axis scale. The main way to get around this is to recover the actual x-positions of the bar midpoints by saving the results of barplot to a variable, but as you can see below I haven't come up with an elegant way of doing what you want in base graphics. Maybe someone else can do better.
x = c(1:81)
b <- barplot(x)
## axis(side=1,at=c(0,20,40,60,80),labels=c(20,40,60,80,100))
head(b)
You can see here that the actual midpoint locations are 0.7, 1.9, 3.1, ... -- not 1, 2, 3 ...
This is pretty quick, if you don't want to extend the axis from 0 to 100:
b <- barplot(x)
axis(side=1,at=b[c(20,40,60,80)],labels=seq(20,80,by=20))
This is my best shot at doing it in base graphics:
b <- barplot(x,xlim=c(0,120))
bdiff <- diff(b)[1]
axis(side=1,at=c(b[1]-bdiff,b[c(20,40,60,80)],b[81]+19*bdiff),
labels=seq(0,100,by=20))
You can try this, but the bars aren't as pretty:
plot(x,type="h",lwd=4,col="gray",xlim=c(0,100))
Or in ggplot:
library(ggplot2)
d <- data.frame(x=1:81)
ggplot(d,aes(x=x,y=x))+geom_bar(stat="identity",fill="lightblue",
colour="gray")+xlim(c(0,100))
Most statistical graphics nerds will tell you that graphing quantitative (x,y) data is better done with points or lines rather than bars (non-data-ink, Tufte, blah blah blah :-) )
Not sure exactly what you wnat, but If it is to have the labels running from one end to the other evenly places (but not necessarily accurately), then:
x = c(1:81)
bp <- barplot(x)
axis(side=1,at=bp[1+c(0,20,40,60,80)],labels=c(20,40,60,80,100))
The puzzle for me was why you wanted to label "20" at 0. But this is one way to do it.
I run into the same annoying property of batplots - the x coordinates go wild. I would add one another way to show the problem, and that is adding more lines to the plot.
x = c(1:81)
barplot(x)
axis(side=1,at=c(0,20,40,60,80),labels=c(20,40,60,80,100))
lines(c(81,81), c(0, 100)) # this should cross the last bar, but it does not
The best I came with was to define a new barplot function that will take also the parameter "at" for plotting positions of the bars.
barplot_xscaled <- function(bar_heights, at = NA, width = 0.5, col = 'grey'){
if ( is.na(at) ){
at <- c(1:length(bar_heights))
}
plot(bar_heights, type="n", xlab="", ylab="",
ylim=c(0, max(bar_heights)), xlim=range(at), bty = 'n')
for ( i in 1:length(bar_heights)){
rect(at[i] - width, 0, at[i] + width, bar_heights[i], col = col)
}
}
barplot_xscaled(x)
lines(c(81, 81), c(0, 100))
The lines command crosses the last bar - the x scale works just as naively expected, but you could also now define whatever positions of the bars you would like (you could play more with the function a bit to have the same properties as other R plotting functions).