I have a weighted directed graph with three strongly connected components(SCC).
The SCCs are obtained from the igraph::clusters function
library(igraph)
SCC<- clusters(graph, mode="strong")
SCC$membership
[1] 9 2 7 7 8 2 6 2 2 5 2 2 2 2 2 1 2 4 2 2 2 3 2 2 2 2 2 2 2 2
SCC$csize
[1] 1 21 1 1 1 1 2 1 1
SCC$no
[1] 9
I want to visualize the SCCs with circles and a colored background as the graph below, is there any ways to do this in R? Thanks!
Take a look at the mark.groups argument of plot.igraph. Something like the following will do the trick:
# Create some toy data
set.seed(1)
library(igraph)
graph <- erdos.renyi.game(20, 1/20)
# Do the clustering
SCC <- clusters(graph, mode="strong")
# Add colours and use the mark.group argument
V(graph)$color <- rainbow(SCC$no)[SCC$membership]
plot(graph, mark.groups = split(1:vcount(graph), SCC$membership))
Related
suppose I have a network like this with multiple subgraphs.
How can I only keep the subgraph with the most number of vertices while removing the rest? In this case I want to keep the subgraph on the left and remove the 3-vertices one the lower right. Thanks!
Given
set.seed(1)
g <- sample_gnp(20, 1 / 20)
plot(g)
we wish to keep the subgraph with 6 vertices. Using
(clu <- components(g))
# $membership
# [1] 1 2 3 4 5 4 5 5 6 7 8 9 10 3 5 11 5 3 12 5
# $csize
# [1] 1 1 3 2 6 1 1 1 1 1 1 1
# $no
# [1] 12
gMax <- induced_subgraph(g, V(g)[clu$membership == which.max(clu$csize)])
we then get
plot(gMax)
This assumes that there is a single largest connected subgraph. Otherwise the "first" one will be chosen.
is there anyway to change the order of the edges in a network graph,
using any of the igraph, visNetwork or even JS within R?
For example i would like a network to have all the arrows going to, from and to;from all in order,
however found nothing online to edit the way the order of the edges is produced,
any help appreciated?
Using igraph you could convert the graph into a data frame and then arrange it:
set.seed(4321)
g <- igraph::sample_gnp(10, .4) %>%
igraph::as.directed()
df <- igraph::as_data_frame(g)
dplyr::arrange(df, from)
This hsould give you something like:
from to
1 1 4
2 1 5
3 1 6
4 1 7
5 1 8
6 1 10
7 2 4
8 2 8
9 2 9
10 2 10
Is there a way to plot a graph in r with bigger edge lengths?
I am simply using
library(igraph)
plot(graph)
and do anybody knows why all the edges have variable length?
** V1 V2
1 6 1
2 6 5
3 1 0
4 1 6
5 1 385
6 5 4
7 5 6
8 5 98
9 0 1
10 0 2
I have data in this format and I am generating a network graph.
You could try a few things:
You could change the margins on your plot:
par(mar=c(0,0,0,0))
plot(graph)
You could change the layout parameters by exploring the igraph documentation on layouts to do things like:
test.layout <- layout_(g,with_dh(weight.edge.lengths = edge_density(g)/1000))
plot(g, layout = test.layout)
I am using TSclust package for SAX (symbolic aggregate aggregation) plots. In accordance to example shown on page 25, I am using the function
SAX.plot(as.ts(df$power), w=30, alpha=4)
But, it generates error as:
Error in if ((n <- as.integer(n[1L])) > 0) { : argument is of length zero
I am not able to debug it. Even I looked into the source code of SAX.plot function but I do not find the relevant error message typed in.
The required R dataobject can be found at link
R version: 3.2
TSclust version:1.2.3
Hello apparently it's because you need to normalize your data, check out this example :
# Parameters
w <- 30
alpha <- 4
# PAA
x <- df$power
paax <- PAA(x, w)
plot(x, type="l", main="PAA reduction of series x")
p <- rep(paax,each=length(x)/length(paax)) #just for plotting the PAA
lines(p, col="red")
# SAX
convert.to.SAX.symbol(paax , alpha)
# [1] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
# You need to scale PAA result
convert.to.SAX.symbol(scale(paax) , alpha)
# [1] 1 1 1 1 1 1 1 1 1 2 2 1 4 3 3 1 2 2 2 4 4 4 1 1 2 4 3 3 4 4
# SAX plot : with scaling this works
SAX.plot(as.ts(scale(df$power)), w=w, alpha=alpha)
That's likely the example you can found in the function help page.
I have the following code that perform hiearchical
clustering and plot them in heatmap.
library(gplots)
set.seed(538)
# generate data
y <- matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep="")))
# the actual data is much larger that the above
# perform hiearchical clustering and plot heatmap
test <- heatmap.2(y)
Which plot this:
What I want to do is to get the cluster member from each hierarchy of in the plot
yielding:
Clust 1: g3-g2-g4
Clust 2: g2-g4
Clust 3: g4-g7
etc
Cluster last: g1-g2-g3-g4-g5-g6-g7-g8-g9-g10
Is there a way to do it?
I did have the answer, after all! #zkurtz identified the problem ... the data I was using were different than the data you were using. I added a set.seed(538) statement to your code to stabilize the data.
Use this code to create a matrix of cluster membership for the dendrogram of the rows using the following code:
cutree(as.hclust(test$rowDendrogram), 1:dim(y)[1])
This will give you:
1 2 3 4 5 6 7 8 9 10
g1 1 1 1 1 1 1 1 1 1 1
g2 1 2 2 2 2 2 2 2 2 2
g3 1 2 2 3 3 3 3 3 3 3
g4 1 2 2 2 2 2 2 2 2 4
g5 1 1 1 1 1 1 1 4 4 5
g6 1 2 3 4 4 4 4 5 5 6
g7 1 2 2 2 2 5 5 6 6 7
g8 1 2 3 4 5 6 6 7 7 8
g9 1 2 3 4 4 4 7 8 8 9
g10 1 2 3 4 5 6 6 7 9 10
This solution requires computing the cluster structure using a different packags:
# Generate data
y = matrix(rnorm(50), 10, 5, dimnames=list(paste("g", 1:10, sep=""), paste("t", 1:5, sep="")))
# The new packags:
library(nnclust)
# Create the links between all pairs of points with
# squared euclidean distance less than threshold
links = nncluster(y, threshold = 2, fill = 1, give.up =1)
# Assign a cluster number to each point
clusters=clusterMember(links, outlier = FALSE)
# Display the points that are "alone" in their own cluster:
nas = which(is.na(clusters))
print(rownames(y)[nas])
clusters = clusters[-nas]
# For each cluster (with at least two points), display the included points
for(i in 1:max(clusters, na.rm = TRUE)) print(rownames(y)[clusters == i])
Obviously you would want to revise this into a function of some kind to be more user friendly. In particular, this gives the clusters at only one level of the dendrogram. To get the clusters at other levels, you would have to play with the threshold parameter.