I'm plotting a graph with igraph and I would like the edges to have different colors depending on the strength of the connections they represent. I could set the colors but I cannot relate them to the values of the connections' strength.
My current code is the following:
library(igraph)
library(raster)
library(ggplot2)
library(statnet)
library(qgraph)
connectivityMatrix <- as.matrix(read.table(file=myFile,sep='')))
coordinates <- as.matrix(read.table(file=coordinatesFile))
connectivityMatrix<-connectivityMatrix[1:833,1:833]
CM<-connectivityMatrix[subsetX,subsetY]
COORD<-coordinates[subset,]
net <- as.network(CM, matrix.type = "adjacency", directed = TRUE)
minX<-min(coordinates[,1])
maxX<-max(coordinates[,1])
minY<-min(coordinates[,2])
maxY<-max(coordinates[,2])
p<-plot(net, coord=COORD,xlim=c(minX,maxX),ylim=c(minY,maxY),edge.col=c('red','yellow','cyan','blue'),object.scale=0.005, vertex.col='dimgrey',edge.lwd=1)
In the code above is there a way to relate the colors specified using edge.col to the range of values they represent in the CM? This way, the edges corresponding to the value 0-x1 in the connectivity matrix would be plotted in red, x1-x2 in 'yellow',.... and x3-x4 in blue. x1, x2, x3 being the range limits and x4 being the maximum of the CM.
Does anyone have an idea on how to do it? Would it be possible to add a legend including the edges' color and the ranges of values they represent?
You can use colorRamp as a scaling function. see the code below for example.
library(igraph)
#Create a random weighted graph
g = erdos.renyi.game(10,0.5)
E(g)$weight = runif(ecount(g))
#Color scaling function
c_scale <- colorRamp(c('red','yellow','cyan','blue'))
#Applying the color scale to edge weights.
#rgb method is to convert colors to a character vector.
E(g)$color = apply(c_scale(E(g)$weight), 1, function(x) rgb(x[1]/255,x[2]/255,x[3]/255) )
#plot using igraph
plot.igraph(g)
Related
I'm using igraph in R, trying to extract a subgraph that comprises only the largest cliques in a graph. I want to plot the graph and the subgraph (1) without moving the vertices and (2) while maintaining numbering. I can't seem to make it work. I tried storing the layout coordinates directly in the vertices but plot seems to rescale things. Setting rescale=FALSE didn't work either. Here's what I have, first plotting the random graph, then highlighting the largest cliques, and finally displaying only the largest cliques:
# plot random graph
g <- sample_gnp(n=30, p=.1)
l=layout_with_fr(g)
V(g)$x <- l[,1]
V(g)$y <- l[,2]
V(g)$id <- 1:vcount(g)
plot(g,vertex.size=6,vertex.label.dist=1,vertex.label=V(g)$id,main="a random network",sub="where are the cliques?")
# highlight largest cliques
lc=unlist(largest_cliques(g))
vcol <- rep("grey80", vcount(g))
vcol[unlist(lc)] <- "gold"
plot(g, vertex.size=6,vertex.color=vcol,vertex.label.dist=1,vertex.label=V(g)$id,main="here they are!",
layout=l)
# plot only the largest cliques, without changing position or vertex numbers
sg = induced_subgraph(g,lc)
sl = cbind(V(sg)$x,V(sg)$y)
plot(sg,vertex.size=6,vertex.label.dist=1,vertex.label=V(sg)$id,vertex.color="gold",
layout=sl)
First of all, you did not set the random seed, so each time you run this code you will get a different graph and layout. I am setting the seed for reproducibility.
# plot random graph
set.seed(2021)
g <- sample_gnp(n=30, p=.1)
l=layout_with_fr(g)
V(g)$x <- l[,1]
V(g)$y <- l[,2]
V(g)$id <- 1:vcount(g)
# highlight largest cliques
lc=unlist(largest_cliques(g))
vcol <- rep("grey80", vcount(g))
vcol[unlist(lc)] <- "gold"
There are other ways to do this, but I will do it using rescale=F.
When you do that, you have to adjust xlim and ylim to the ranges of the layout. Also, it changes the scale, so you need to change the vertex size.
Putting all of that together, I get:
Rx =range(l[,1])
Ry =range(l[,2])
par(mfrow=c(1,2))
plot(g, vertex.size=45,vertex.color=vcol,vertex.label.dist=1,
vertex.label=V(g)$id,main="here they are!", layout=l,
rescale=F, xlim=Rx, ylim=Ry)
# plot only the largest cliques, without changing position or vertex numbers
sg = induced_subgraph(g,lc)
sl = cbind(V(sg)$x,V(sg)$y)
plot(sg,vertex.size=45, vertex.label.dist=1,vertex.label=V(sg)$id,vertex.color="gold",
layout=sl, rescale=F, xlim=Rx, ylim=Ry, main="Unmoved")
you can use set-vertex_attr to set the name attribute for graph g, and then subset the layout lc like below
# plot random graph
set.seed(2021)
g <- sample_gnp(n = 30, p = .1)
l <- layout_with_fr(g)
# highlight largest cliques
lc <- unlist(largest_cliques(g))
g %>%
set_vertex_attr(name = "name", value = seq(vcount(.))) %>%
induced.subgraph(lc) %>%
plot(layout = l[as.integer(names(V(.))), ], vertex.color = "gold")
and you will get
Assuming a matrix of distances between a number of samples, I would like to somehow reasonably map these distances to a color space. So for example if you have three apparent clusters, they should have different colors, and within a cluster you would have a number of shades of a color. However, I would like to avoid explicit clustering, if possible.
Clearly, the mapping cannot be perfect and universal: rather, it is a heuristic.
Is there a known algorithm for that? Or, perhaps, a ready solution for R?
Here is one possibility. No matter how many dimensions your original data was, you can use multi-dimensional scaling with the distance matrix to project the data to three dimensions, in a way that coarsely preserves distances. If you treat the three dimensions as R, G and B this will give a color scheme in which points that are close should have "close" colors.
Here is a simple example. I generate some 5-dimensional data with 4 clusters (although no cluster analysis is performed). From that, we get the distance matrix. Then, as above we use multi-dimensional scaling to turn this into a color map. The points are plotted to show the result.
## Generate some sample data
set.seed(1234)
v = c(rnorm(80,0,1), rnorm(80,0,1), rnorm(80,4,1), rnorm(80,4,1))
w = c(rnorm(80,0,1), rnorm(80,4,1), rnorm(80,0,1), rnorm(80,4,1))
x = c(rnorm(80,0,1), rnorm(80,0,1), rnorm(80,4,1), rnorm(80,4,1))
y = c(rnorm(80,0,1), rnorm(80,4,1), rnorm(80,0,1), rnorm(80,4,1))
z = c(rnorm(80,0,1), rnorm(80,4,1), rnorm(80,-4,1), rnorm(80,8,1))
df = data.frame(v,w,x,y,z)
## Distance matrix
D = dist(df)
## Project to 3-dimensions
PROJ3 = cmdscale(D, 3)
## Scale the three dimensions to [0,1] interval
ScaledP3 = apply(PROJ3, 2, function(x) { (x - min(x))/(max(x)-min(x)) })
colnames(ScaledP3) = c("red", "green", "blue")
X = as.data.frame(ScaledP3)
## Convert to color map
ColorMap = do.call(rgb, X)
plot(x,y, pch=20, col=ColorMap)
I have a very large bipartite network model that I created from 5 million lines of a dataset. I decompose my network model because I can not draw a graph of this size. Now all I need is to plot the decompose graphics one by one. There is no problem with that. But I want to draw the graph with a shape according to the attributes of each node. For example, I want a square for the "A" attributes on my graph G, and a triangle for the "B" attributes. In addition to this I want to add vertex labels by attributes. Here is my codes to plot first component of graph after creating bipartite G and its work:
components <- decompose(G)
plot(components[[1]])
I tried something like this to adding labels and changing vertex shapes according to graph attributes but it didn't work:
plot(components[[1]], vertex.label= V(G)$attributes,
vertex.shape=c("square", "triangle"))
Does anyone can help me, I'm stuck. Thank you so much!
the components function returns a list of vertices which make up a component. So you need to traverse the list, create a subgraph and plot. As for plotting attributes you need to provide a reproducible example for us to help.
library(igraph)
set.seed(8675309)
g <- sample_gnp(200, p = 0.01)
V(g)$name <- paste0("Node", 1:vcount(g))
V(g)$shape <- sample(c("circle","square"), vcount(g), replace = T)
clu <- components(g)
grps <- groups(clu)
lapply(grps, function(x) plot(induced_subgraph(g, x)))
I am trying to plot a network that changes in time. The network starts with a certain number of nodes and edges and each time step some of the nodes and edges are removed.
I want to be able to plot the network so that the nodes are in the same place in each. However when I try this. sometimes the nodes shift position in the plot frame even if the relation to each other is the same.
I am making the network change into a gif so even small changes are annoying. I think the change may occur when a large fraction of the nodes are removed but I am not sure.
The code below illustrates this using an ER graph.
library(igraph); library(dplyr)
#generate random graph
set.seed(500)
RandomGraph <- sample_gnm(1000, 2500)
#name nodes
V(RandomGraph)$name <- paste0("Node", 1:1000)
#Get the coordinates of the Nodes
Coords <- layout_with_fr(RandomGraph) %>%
as_tibble %>%
bind_cols(data_frame(names = names(V(RandomGraph))))
#Delete random vertices
deletevertex <-sample( V(RandomGraph)$name, 400)
RandomGraph2 <-delete.vertices(RandomGraph, deletevertex)
#get the coordinates of the remaining Nodes
NetCoords <- data_frame(names = names(V(RandomGraph2))) %>%
left_join(Coords, by= "names")
#plot both graphs
RandomGraph%>%
plot(.,vertex.size=.8, edge.arrow.size=.4, vertex.label = NA, layout = as.matrix(Coords[,1:2]))
RandomGraph2%>%
plot(.,vertex.size=.8, edge.arrow.size=.4, vertex.label = NA, layout = as.matrix(NetCoords[,2:3]))
#They nodes have the same relationship to each other but are not laid out in the same position in the frame
As you can see the plots have placed nodes in the same place relative to each other but not relative to the frame.
How can I have the plot position fixed.
plot.igraph rescales each axis by default (from -1 to +1 on both x and y).
You just need to turn that off: rescale = F and then explicitly set appropriate xlim and ylim values.
For your example code..
RandomGraph%>%
plot(.,vertex.size=.8, edge.arrow.size=.4, vertex.label = NA, layout = as.matrix(Coords[,1:2]),rescale=F,xlim=c(-25,30),ylim=c(-20,35))
RandomGraph2%>%
plot(.,vertex.size=.8, edge.arrow.size=.4, vertex.label = NA, layout = as.matrix(NetCoords[,2:3]),rescale=F,xlim=c(-25,30),ylim=c(-20,35))
The problem is that
identical(range(Coords[1]), range(NetCoords[2]))
# [1] FALSE
Since igraph normalizes the coordinates on a range between -1 and 1 before plotting, this leads to slightly different coordinates for NetCoords compared to Coords. I'd just calculate the normalized coordinates for all nodes beforehand:
coords_rescaled <- sapply(Coords[-3], function(x) -1+((x-min(x))*2)/diff(range(x)))
rownames(coords_rescaled) <- Coords$names
And then assign the normalized coordinates (or the required subset) and set rescale=FALSE (as #jul) already suggested:
par(mfrow=c(1,2), mar=c(1,.5,1,.5))
RandomGraph%>%
plot(.,edge.arrow.size=.4, layout = coords_rescaled, rescale=F);box()
RandomGraph2%>%
plot(.,edge.arrow.size=.4, layout = coords_rescaled[NetCoords$names, ], rescale=F);box()
# Erdos
par(mfrow=c(1,2))
g <- erdos.renyi.game(100, 1/100)
V(g)$size<-seq(0.05,5,0.05)
betweenness(g)
# Draw nodes and save positions
locs <- layout.fruchterman.reingold(g)
plot(g,
layout=locs,
vertex.label=NA,
main="Original",
vertex.color=degree(g))
g
vertex.color=degree(g)
did not work. Could anyone tell me how to color the vertices by "degree"?
Red (high value) to blue (low value) would be perfect.
Thanks!
A solution I found is to create a new color vector with the grey color R provides us with colors()[]. If you check colors()[] in your terminal, you can see the full list of colors that are readable by the plot.igraph() function.
You first charge your data (graph, etc.) :
edgelist <- read.csv(...)
graph <- make_graph_from_data(edgelist)
Then you create a vector of colors that corresponds to the length of your vertices list :
length(V(g)) # with a length of X vertices :
colors <- c(paste0(rep("grey",X),seq(X,1)))
Finally, you plot it with the attribute vertex.color :
plot(g,vertex.color=colors[degree(graph)])
However, one can only use this little trick for graph with less than 100 values in degree(graph)...