I know that in base R I can use par(lheight = ...) to adjust the space between two lines that are broken by \n, e.g. in axis labels. I was wondering how to adjust the space between lines in multi-line vertex labels in igraph. I could not find an answer here on Stack Overflow, I hope I have not overseen anything obvious. Or is there any other option to break lines than using \n in igraph that allows to adjust the space?
library(igraph)
relations <- data.frame(from=c("Bob\nSurname", "Cecil\nSurname", "Cecil\nSurname", "David\nSurname",
"David\nSurname", "Esmeralda\nSurname"),
to=c("Alice\nSurname", "Bob\nSurname", "Alice\nSurname", "Alice\nSurname", "Bob\nSurname", "Alice\nSurname"))
g <- graph_from_data_frame(relations, directed=FALSE)
plot(g)
# this does not change the space between first name and surname...
par(lheight = .1)
plot(g)
Your solution works perfectly. Maybe you tried to see too small difference? Here you see that igraph plotting is built on top of basic R graphics. Labels are plotted by calling text() (near line 394) which takes the lheight parameter either from par or as argument in ... (here not used). Therefore setting par(lheight = x) must determine the label line height. Testing with your graph:
png('graph_lheight1.png')
par(lheight = 1)
plot(g)
dev.off()
png('graph_lheight2.png')
par(lheight = 2)
plot(g)
dev.off()
In R, I have a raster object generated from a kernel density analysis using the ks package. I convert this into a raster object (from the raster package) and try to draw that raster object to a PNG using plot(). I want the png to have exactly one pixel for every pixel in the raster object. Simple enough, right? By default of course, I get all sorts of extraneous junk added to the plot. I can remove most of this using the various settings in plot() or par(), but no matter what I do, I don't seem able to get rid of the space formerly taken up by the legend on the right side of the plot.
library('ks')
library('raster')
# generate the data
set.seed(1)
x = matrix(rnorm(1000,1,0.5),500)
xpix = 100
ypix = 100
# calculate the density function
k = kde(
x,
H=matrix(c(0.1,0,0,0.1),2),
xmin=c(0,0),
xmax=c(1,1),
gridsize=c(xpix,ypix)
)
# convert to raster
r = raster(k)
# plot the image to PNG
png('file.png',width=xpix,height=ypix)
par(
mar=c(0,0,0,0),
bty='n',
bg='black',
plt=c(0,1,0,1)
)
plot(
r,
legend=FALSE,
axes=FALSE,
plt=c(0,1,0,1)
)
# see that 'plt' did not change
print(par())
dev.off()
If I check par before closing the device, I can see that the 'plt' value is not what I set it to; it shows the right margin, where the plotting area has been nudged over to make space for the non-legend. Sample code is above, and the image it generates is linked to here.
Incidentally, I was able to achieve the correct effect with the image() function instead of plot(), though that introduced it's own problems, namely that transparency no longer worked. Can I solve this with plot()? It's very frustrating that I'm so close but just can't seem to change the size of the plot area! I don't want to use another graphics package if there is any way to make the base function work.
I have a graph in igraph with a vertex size that is coded for a value.
I wish to add a legend with symbols (points in my case) with different sizes.
I tried the following code
require(igraph)
er_graph <- erdos.renyi.game(100, 5/100)
value<-runif(100)
n<-6
size_vec<-seq_len(n)
sizeCut<-cut(value,n)
vertex.size<-size_vec[sizeCut]
plot(er_graph, vertex.label=NA, vertex.size=vertex.size)
legend('topleft',legend=levels(sizeCut),pt.cex=size_vec,col='black')
but end with legend without symbols
see example
Any sugestions how I go about this?
You should set pch to some value to indicate which character you want to use for the bullets (see ?points to check the possible values).
Also, you should scale the pt.cex values in order to make the bullets not too big for the legend, and use pt.bg to set the background color of the bullets, e.g.
# scaled between 1 and 2
scaled <- 1 + ((2-1) * (size_vec - min(size_vec) ) / ( max(size_vec) - min(size_vec) ) )
legend('topleft',legend=levels(sizeCut),pt.cex=scaled,col='black',pch=21, pt.bg='orange')
EDIT :
Unfortunately, calculating the right sizes of the bullets is not easy; a possible workaround is plotting white bullets then manually add the vertices to the legend in the same way they are plotted inside the plot.igraph function :
# N.B. /200 is taken from plot.igraph source code
a <- legend('topleft',legend=levels(sizeCut),pt.cex=size_vec/200,col='white',
pch=21, pt.bg='white')
x <- (a$text$x + a$rect$left) / 2
y <- a$text$y
symbols(x,y,circles=size_vec/200,inches=FALSE,add=TRUE,bg='orange')
Disclaimer: this code heavily relies on the source code of plot.igraph function that might be changed in a future version of igraph. Probably you should search for another plot function for igraph which natively allows to add a legend.
Anyone looking at this who wants a continuous scale for node sizes instead of a discrete scale then this is the code you need to do it:
require(igraph)
er_graph <- erdos.renyi.game(100, 5/100)
value<-runif(100)
sizeCut<- c(0.2,0.4,0.6,0.8,1.0)
sizeCutScale <- sizeCut*10
vertex.size<-value*10
plot(er_graph, vertex.label=NA, vertex.size=vertex.size)
legend('topleft',legend=unique(sizeCut),pt.cex= sizeCutScale,col='black')
a <- legend('topleft',legend=unique(sizeCut),pt.cex=sizeCutScale/200,col='white',
pch=21, pt.bg='white')
x <- (a$text$x + a$rect$left) / 2
y <- a$text$y
symbols(x,y,circles=sizeCutScale/200,inches=FALSE,add=TRUE,bg='orange')
I'm making a network plot in R using iGraph. I first plot it using tkplot() so that I can manually reposition some of the nodes. Then I capture the new coordinates and then insert those in the plot function to replot the graph along with additional adjustments (changing the opacity of the nodes).
The problem is that even when using the tkplot.coords coordinates, the second graph doesn't look like the tkplot. Instead, some of the arrow heads appear in the middle of the edge rather than at the end, and the nodes are tightly clustered and overlapping, even though that isn't the case with the tkplot. Any suggestions for how I can get the plot() function to exactly mimic the plot produced using tkplot()?
I am using R Studio, so I am wondering if there is a conversion issue with that.
My simplified code is as follows:
Net1 <- graph.data.frame(myedgedata, vertices=nodeslist, directed=TRUE)
g <- graph.adjacency(get.adjacency(Net1), weighted = TRUE)
E(g)$weight <- E(g)$weight+1
tkplot(g)
coords <- tkplot.getcoords(1)
plot(g, edge.width=E(g)$weight, vertex.color = adjustcolor(nodeslist$colors, alpha=.5), layout=coords)
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.