Just wondering if it is possible to increase the size of the plot so that the nodes and edges can be more scattered over the plot.
Original plot:
What are expected:
I tried many parameters in the layout function such as area, niter, and so on, but all of them do not work. By the way, I am using 'igraph' package in R.
If you are referring to the actual size of the produced output (pdf, png, etc), you can configure it with the width and height parameters. Check this link for png,bpm, etc, and this link for PDF format.
A MWE is something like this:
png("mygraph.png", heigh=400, width=600)
#functions to plot your graph
dev.off()
If you are referring to the size of the graphic produced by the layout function, as #MrFlick referred, you should check the parameters of the particular layout you are using.
Hope it helps you.
In your second graph, it's obviously the graph can be divided into several clusters (or sections). If I understood you correctly, you want to have a layout that separates your clusters more visibly.
Then you can draw this by calculating a two-level layout:
First, calculate the layout of the graph in order to find a place for each cluster.
Second, calculate the layout in each cluster according to first step and plot nodes in the corresponding place.
Related
I am new at R but I haven't find any topic that could answer my question.
I want to plot a graph named g, using the package igraph. However the result is not fully satisfying as the nodes names aren't all readable.
This is the code I used to plot :
plot(g, layout_nicely(g), asp=FALSE, vertex.shape=V(g)$shape, vertex.label.dist=0.3,
vertex.label.degree=-pi/2)
And this is the result
I've tried to reduce the number of characters of each label, or to reduce their font sizes but it wasn't enough. I also used several layout such as layout.kamada.kawai, layout.fruchterman.reingold but the labels are always too close to each others.
Is there any way to choose a better position for the labels or to increase the length of the edges ?
Any help would be appreciated
I'm trying to plot the cluster obtained from fuzzy c-means clustering.
The plot should look like this.
code for the plot
plot(data$Longitude, data$Latitude, main="Fuzzy C-Means",col=data$Revised, pch=16, cex=.6,
xlab="Longitude",ylab="Latitude")
library(maps)
map("state", add=T)
However, when I tried to use clusplot the plot is displaying in opposite direction(both top and bottom and left and right) as below.
I wanna know if there's a way to reverse the plot to show in the order as the above picture.
Also, for the very dense area, it's hard to find the ellipse label. I wanna know if there's a way to show the label inside the ellipse instead of outside.
code for 2nd pic
library(cluster)
clusplot(cbind(Geocode$Longitude, Geocode$Latitude), cluster, color=TRUE,shade=TRUE,
labels=4, lines=0,col.p=cluster,
xlab="Longitude",ylab="Latitude",cex=1)
clusplot is a function that performs a lot of magic for you. In particular it projects the data set - which happens in a way you don't like, unfortunately. (Also note the scales - it centered and scaled the data, too)
clusplot.default: Creates a bivariate plot visualizing a partition (clustering) of the data. All observation are represented by points in the plot, using principal components or multidimensional scaling.
As far as I can tell, clusplot doesn't have map support, but you will want such a map I guess...
While maybe you can use the s.x.2d parameter to specify the exact projection (and this way disable automatic scaling), it probably is still difficult to add the map. Maybe look at the source of clusplot instead, and take only the parts you want?
I am trying to plot quite large and dense networks (dput here). All I end up with is a bunch of overlapping dots, which does not really give me a sense of the structure or density of the network:
library(sna)
plot(data, mode = "fruchtermanreingold")
However, I have seen plots which utilizes fading to visualize the degree to which points overlap, e.g.:
How can I implement this "fading" in a plot of a graph?
Here's one way:
library(sna)
library(network)
source("modifieddatafromgist.R")
plot.network(data,
vertex.col="#FF000020",
vertex.border="#FF000020",
edge.col="#FFFFFF")
First, I added a data <- to the gist so it could be sourced.
Second, you need to ensure the proper library calls so the object classes are assigned correctly and the proper plot function will be used.
Third, you should use the extra parameters for the fruchtermanreingold layout (which is the default one for plot.network) to expand the area and increase the # of iterations.
Fourth, you should do a set.seed before the plot so folks can reproduce the output example.
Fifth, I deliberately removed cruft so you can see the point overlap, but you can change the alpha for both edges & vertices (and you should change the edge width, too) to get the result you want.
There's a ton of help in ?plot.network to assist you in configuring these options.
I have this pairs plot
I want to make this plot bigger, but I don't know how.
I've tried
window.options(width = 800, height = 800)
But nothing changes.
Why?
That thing's huge. I would send it to a pdf.
> pdf(file = "yourPlots.pdf")
> plot(...) # your plot
> dev.off() # important!
Also, there is an answer to the window sizing issue in this post.
If your goal is to explore the pairwise relationships between your variables, you could consider using the shiny interface from the pairsD3 R package, which provides a way to interact with (potentially large) scatter plot matrices by selecting a few variables at a time.
An example with the iris data set:
install.packages("pairsD3")
require("pairsD3")
shinypairs(iris)
More reference here
I had the same problem with the pairs() function. Unfortunately, I couldn't find a direct answer to your question.
However, something that could help you is to plot a selected number of variables only. For this, you can either subset the default plot. Refer to this answer I received on a different question.
Alternatively, you can use the pairs2 function which I came across through this post.
To make the plot bigger, write it to a file. I found that a PDF file works well for this. If you use "?pdf", you will see that it comes with height and width options. For something this big, I suggest 6000 (pixels) for both the height and width. For example:
pdf("pairs.pdf", height=6000, width=6000)
pairs(my_data, cex=0.05)
dev.off()
The "cex=0.05" is to handle a second issue here: The points in the array of scatter plots are way too big. This will make them small enough to show the arrangements in the embedded scatter plots.
The labels not fitting into the diagonal boxes is resolved by the increased plot size. It could also be handled by changing the font size.
I am using ape (Analysis of Phylogenetics and Evolution) package in R that has dendrogram drawing functionality. I use following commands to read the data in Newick format, and draw a dendrogram using the plot function:
library("ape")
gcPhylo <-read.tree(file = "gc.tree")
plot(gcPhylo, show.node.label = TRUE)
As the data set is quite large, it is impossible to see any details in the lower levels of the tree. I can see just black areas but no details. I can only see few levels from the top, and then no detail.
I was wondering if there is any zoom capability of the plot function. I tried to limit the area using xLim and yLim, however, they just limit the area, and do not zoom to make the details visible. Either zooming, or making the details visible without zooming will solve my problem.
I am also appreciated to know any other package, function, or tool that will help me overcoming the problem.
Thanks.
It is possible to cut a dendrogram at a specified height and plot the elements:
First create a clustering using the built-in dataset USArrests. Then convert to a dendrogram:
hc <- hclust(dist(USArrests))
hcd <- as.dendrogram(hc)
Next, use cut.dendrogram to cut at a specified height, in this case h=75. This produces a list of a dendrogram for the upper bit of the cut, and a list of dendograms, one for each branch below the cut:
par(mfrow=c(3,1))
plot(hcd, main="Main")
plot(cut(hcd, h=75)$upper,
main="Upper tree of cut at h=75")
plot(cut(hcd, h=75)$lower[[2]],
main="Second branch of lower tree with cut at h=75")
The cut function described in the other answer is a very good solution; if you would like to maintain the whole tree on one page for some interactive investigation you could also plot to a large page on a PDF.
The resulting PDF is vectorized so you can zoom in closely with your favourite PDF viewer without loss of resolution.
Here's an example of how to direct plot output to PDF:
# Open a PDF for plotting; units are inches by default
pdf("/path/to/a/pdf/file.pdf", width=40, height=15)
# Do some plotting
plot(gcPhylo)
# Close the PDF file's associated graphics device (necessary to finalize the output)
dev.off()