Quick way to reduce dimensions - r

I have a data frame consisting of 1200 columns. I would like to reduce this to a more manageable number, and thought of performing a cross correlation between each of the columns, and combining columns which have a r>0.9.
here is the result from dput(head(a, 20))
ClassTechnology..Writing.instruments = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ClassTechnology..Ink.and.lead.refills = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Printing.machinery.and.equipment = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Printing.machinery.accessories = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ClassTechnology..Printing.accessories = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Book.binding.and.sewing.equipment.and.accessories = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Printing.laboratory.equipment.and.accessories = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Lecterns.and.sound.systems.and.accessories = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), ClassTechnology..Projectors.and.supplies = c(0,
0, 0, 0, 0, 0, 0, 0, 0.28903076, 0, 0, 0, 0, 0, 0.086521352,
0, 0, 0, 0, 0), ClassTechnology..Audio.presentation.and.composing.equipment.and.hardware.and.controllers = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Video.and.combination.video.and.audio.presentation.equipment.and.hardware.and.controllers = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Phone.and.video.conference.equipment.and.hardware.and.controllers = c(0.0695846,
0, 0, 0.2, 0, 0, 0, 0, 0.190946024, 0, 0.2, 0, 0, 0, 0.086521352,
0.2, 0, 0, 0, 0), ClassTechnology..Microfilm.equipment.and.supplies = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Cameras = c(0.8608308, 0, 0, 0.2, 0, 0,
0.25, 0, 0, 0, 0.2, 0.792574627, 0.25, 0.25, 0.913478648,
0.2, 0, 0, 1, 0), ClassTechnology..Camera.accessories = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.086521352, 0, 0,
0, 0, 0), ClassTechnology..Photographic.processing.equipment = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Microfilm.production.equipment.and.supplies = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Darkroom.supplies = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), ClassTechnology..Firearms = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.740435943, 0, 0,
0, 0, 0), ClassTechnology..Arms.and.ammunition.accessories = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L)
ClassTechnology..Video.and.combination.video.and.audio.presentation.equipment.and.hardware.and.controllers = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Phone.and.video.conference.equipment.and.hardware.and.controllers = c(0.0695846,
0, 0, 0.2, 0, 0, 0, 0, 0.190946024, 0, 0.2, 0, 0, 0, 0.086521352,
0.2, 0, 0, 0, 0), ClassTechnology..Microfilm.equipment.and.supplies = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
ClassTechnology..Cameras = c(0.8608308, 0, 0, 0.2, 0, 0,
0.25, 0, 0, 0, 0.2, 0.792574627, 0.25, 0.25, 0.913478648,
0.2, 0, 0, 1, 0),
I ran the following code:
df=NULL
for(i in 22:ncol(a)){
for(j in 22:ncol(a)){
if (j<=i){next}
df=rbind(df,cbind(colnames(a)[i],colnames(a)[j],cor(a[,i],a[,j])))
}
}
df=as.data.frame(df)
df[,3]=as.numeric(as.character(df[,3]))
But it takes an incredibly long time (>2hours now), and wonder if anyone has any suggestions as to how I can improve this calculation, or a different way to reduce the dimensions

Related

Odd edge behavior in `qgraph` after scaling node size with an attribute

[[Reproducible data for this question is found at bottom of question.]]
When plotting a network with qgraph, the edges usually link to nodes in a relatively straightforward way.
library(qgraph)
qgraph(Network)
But as soon as I add a size to my nodes, the edges often overshoot the nodes:
qgraph(Network,
vsize=log(Attributes)*3, # scale nodes
vTrans=150, #Transparency of the nodes
label.scale=F # don't scale labels along with nodes
)
Some node scaling sizes work better than others:
qgraph(Network,vsize=Attributes/5,
vTrans=150,#Transparency of the nodes, must be an integer between 0 and 255, 255 indicating no transparency
label.scale=F)
But it isn't clear why this is the case, or how I can override the edges to meet the node appropriately (either at the boundary of the scaled node or at the centerpoint of the node). Any thoughts welcome.
Data:
Network<-structure(list(V4 = c(0, 0, 0.6, 0.01, 0.06, 0.09, 0.01, 0.01,
0, 0.01, 0.03, 0, 0, 0, 0.12, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V5 = c(0, 0, 0.6,
0.01, 0.06, 0.09, 0.01, 0.01, 0, 0.01, 0.03, 0, 0, 0, 0.13, 0.04,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), V6 = c(0, 0, 0, 0.02, 0.12, 0.08, 0, 0.01, 0, 0.01, 0.02,
0, 0, 0, 0.04, 0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), V7 = c(0, 0, 0, 0, 0, 0, 0.01, 0.01,
0.01, 0.03, 0.01, 0.03, 0.05, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0.01, 0, 0, 0), V8 = c(0,
0, 0, 0, 0, 0, 0.01, 0.01, 0.01, 0.03, 0.01, 0.03, 0.06, 0, 0.03,
0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0,
0, 0.01, 0, 0, 0), V9 = c(0, 0, 0, 0, 0, 0, 0.01, 0.01, 0.01,
0.03, 0.01, 0.03, 0.01, 0, 0.04, 0.02, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0.01, 0, 0, 0), V10 = c(0,
0, 0, 0, 0, 0, 0, 0, 0.01, 0.01, 0.01, 0.04, 0.05, 0, 0.01, 0,
0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0.01, 0, 0, 0,
0, 0.01, 0, 0, 0), V11 = c(0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0.01,
0.01, 0.03, 0.08, 0, 0, 0, 0.02, 0, 0.02, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0, 0, 0), V12 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0.01, 0, 0.07, 0, 0, 0, 0, 0.01, 0, 0.02, 0,
0.02, 0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0.01, 0, 0, 0.01,
0, 0, 0, 0, 0), V13 = c(0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0.01,
0.04, 0.05, 0, 0, 0, 0.02, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.01, 0.01, 0, 0, 0, 0, 0.01, 0, 0, 0), V14 = c(0, 0,
0, 0, 0, 0, 0, 0.01, 0.01, 0.02, 0, 0.01, 0.09, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), V15 = c(0, 0, 0, 0, 0, 0, 0, 0.01, 0.01, 0.02, 0, 0, 0.09,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), V16 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), V17 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), V18 = c(0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0), V19 = c(0, 0, 0, 0, 0, 0, 0,
0, 0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0.01, 0, 0.01,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0), V20 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0.01, 0, 0, 0.08, 0,
0.01, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0), V21 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), V22 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
0, 0.01, 0.01, 0.09, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), V23 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.01, 0, 0, 0, 0, 0, 0.01, 0.01, 0.09, 0, 0.01, 0, 0.01, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V24 = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0.01, 0.09,
0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), V25 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
0, 0.01, 0.01, 0.09, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), V26 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.01, 0, 0, 0, 0, 0, 0.01, 0.01, 0.09, 0, 0.01, 0, 0.01, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V27 = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0.01, 0.09,
0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), V28 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
0, 0.01, 0.01, 0.09, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), V29 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.01, 0, 0, 0, 0, 0, 0.01, 0, 0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V30 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0.01, 0.09, 0, 0.01, 0,
0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V31 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0.01, 0.01, 0.09,
0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), V32 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.07, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0,
0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), V33 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.02, 0.02, 0, 0, 0.01, 0.01, 0.01, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V34 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0.03, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V35 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0.03, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V36 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0.03,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V37 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V38 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V39 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V40 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0, 0, 0.01,
0.01, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), V41 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0), V42 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01,
0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), V43 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), V44 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.06, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), V45 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.01, 0, 0, 0.01, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), V46 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.02, 0.07, 0.02, 0, 0, 0.01, 0, 0.01, 0, 0, 0.01, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V47 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V48 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0, 0.01, 0.01, 0.01, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V49 = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0.01, 0, 0, 0.01,
0.01, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), V50 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.02, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), V51 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.01, 0.03, 0.01, 0, 0, 0.01, 0.01, 0.01, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), V52 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.02, 0.01, 0.03, 0, 0.01, 0.02, 0.02, 0.02,
0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V53 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09, 0,
0.06, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V54 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
0, 0, 0, 0.01, 0.01, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0.01,
0.09, 0, 0.01, 0, 0, 0, 0.02, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0,
0, 0, 0, 0, 0), V55 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0.01, 0.01, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.08, 0, 0, 0, 0, 0, 0.02, 0, 0, 0, 0, 0,
0.01, 0, 0, 0, 0, 0.02, 0, 0, 0, 0), V56 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.05, 0.08, 0, 0.02, 0, 0, 0.01,
0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V57 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.01, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0.01, 0.02, 0.03, 0, 0.01,
0), V58 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01,
0, 0.08, 0, 0, 0, 0, 0, 0.03, 0.01, 0.01, 0.01, 0.01, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), V59 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0.03, 0, 0, 0.01, 0.01,
0, 0, 0, 0, 0, 0.02, 0, 0.02, 0, 0, 0), V60 = c(0, 0, 0, 0, 0,
0, 0, 0, 0.01, 0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0.04, 0, 0, 0, 0), V61 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0), V62 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.06, 0.04, 0.01, 0, 0),
V63 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.03, 0.01, 0, 0, 0)), class = "data.frame", row.names = c("4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59",
"60", "61", "62", "63"))
Attributes<-c(34.93768692, 4.75733614, 13.93967533, 2.833557367, 8.325469971,
8.177970886, 2.928951502, 2.174068213, 7.494392872, 6.128136158,
2.818100929, 1.909636378, 3.748121262, 1e-05, 70.72342682, 22.41350937,
2.115944386, 0.005, 1.84581995, 0.102126002, 15.20289135, 2.613022089,
4.338716984, 0.032485999, 0.059714999, 0.080463, 0.035101, 0.011345,
1, 3.151705027, 0.239722997, 0.137802005, 0.017914001, 0.036782667,
1.388822675, 0.435640007, 3.397774458, 2.329986095, 21.80796051,
0.200000003, 1.358244658, 0.687838018, 2.832928419, 1.016921043,
11.10915184, 2.84529686, 0.925952315, 4.18819809, 3.080216408,
0.276921213, 1.808943033, 3.043907881, 0.426636606, 80, 3.872853518,
7.236839294, 1.322934866, 11.1804142, 3.803627491, 31.66708755
)

The edges aren't necessarily wrong. You've given many of the nodes negative values. if you even set them to 1, the arrows do as you expect. For example, vsize = ifelse(log(Attributes) * 3 > 0, log(Attributes) * 3, 1) will present with all meaningful arrows.
I'm surprised it didn't cause an error when you made the nodes negative... it's actually really nice that it didn't. It probably made it a lot easier to figure out what was wrong. When you used Attributes/5 you didn't end up with negative values.

How to get presence/absence summary by month for each taxa to create ggplot bar plot?

I have fish stomach contents/diet data and I would like to get presence/absence information for each taxa in my df by month. Each observation (row) has information on the taxa absent (== 0) or present (== 1) in each fish's stomach. I have already transformed my original data to presence/absence values, however, I am not sure how to obtain a summary of what taxa was present or absent by month.
structure(list(id = c("607_6", "808_4", "801_3", "807_11", "801_16",
"724_13", "1030_40", "723_78", "701_4", "634_2", "1023_2", "1031_2",
"643_4", "606_3", "723_79", "801_4", "629_4", "642_10", "801_10",
"801_11", "1001_35", "616_4", "701_9", "627_2", "601_5"), Daphnia = c(0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), Byths = c(0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1), Chiro.Pupae = c(0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0
), Empty = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Chiro.Larvae = c(0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), Amphipod = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0), Isopod = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
Chironomidae = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Hemimysis = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), Copepoda = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0), Sphaeriidae = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), Chiro.Adult = c(0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Trichopteran = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0), UID.Fish = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Chydoridae = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), Cyclopoid = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Fish.Eggs = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), EggMass = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Dreissena = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), Goby = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Eurycercidae = c(0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), Hirudinea = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), totalnumPrey = c(0,
5, 0, 0, 102, 7, 220, 45, 0, 0, 0, 25, 116, 49, 119, 0, 7,
5, 0, 0, 0, 595, 105, 58, 20), MONTH = c(6L, 8L, 8L, 8L,
8L, 7L, 11L, 7L, 7L, 6L, 11L, 11L, 6L, 6L, 7L, 8L, 6L, 6L,
8L, 8L, 11L, 6L, 7L, 6L, 6L), empty = c("Empty", "Not_empty",
"Empty", "Empty", "Not_empty", "Not_empty", "Not_empty",
"Not_empty", "Empty", "Empty", "Empty", "Not_empty", "Not_empty",
"Not_empty", "Not_empty", "Empty", "Not_empty", "Not_empty",
"Empty", "Empty", "Empty", "Not_empty", "Not_empty", "Not_empty",
"Not_empty")), row.names = c(NA, -25L), class = c("data.table",
"data.frame"))
I looked online and various SO posts like this one, but I am not getting exactly what I need.
I would like to end up with something like this (or similar) for each month for all taxa in my df (the values here are made up, might not reflect the real data):
Month
Daphnia
Byths
Chiro.Pupae
Isopod
Goby
11
1
1
0
1
0
My ultimate goal is to make a bar plot in ggplot that looks like this:
Originally, the data was in long format but this results in multiple rows per fish. I changed to wide format to end up with one observation(row) per fish.
How can I achieve this to ultimately plot presence/absence by month? Thank you!

Maybe you want something like with your selected column converted to a longer format. After that to show the zeros bars, you can say that there is a bar by giving it a small negative number (if you want to show zero bars). At last, the y-axis has a binary format. You can use the following code:
library(dplyr)
library(ggplot2)
library(tidyr)
library(lubridate)
df %>%
select(MONTH, Daphnia, Byths, Chiro.Pupae, Isopod, Goby) %>%
mutate(MONTH = month.name[MONTH]) %>%
pivot_longer(cols = -c(MONTH), values_transform = as.numeric) %>%
ggplot(aes(x = MONTH, y = sapply(value, FUN=function(x) ifelse(x==0,-0.1,x)), fill = name)) +
geom_bar(position = "dodge", stat = "identity") +
scale_y_continuous(breaks = c(0,1)) +
labs(y = "Absence", x = "Month")
Created on 2022-07-30 by the reprex package (v2.0.1)

how to change the strength and symmetry of the edges in the igraph in R?

I'm looking to make a graph similar to this one:
Where not only do the vertices have different sizes according to their values, but the edges have different widths according to the values/force.
I have this data set here:
data = structure(c(NA, 0, 0, 0, 0.003122927, 0.00999241, 0.008685473,
0.007730365, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.003573423, 0, 0, 0,
0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.18893711, 0, 0, 0, NA, 0.183237263,
0.139293056, 0.120902907, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.132071652,
0, 0.457142857, 0.114500717, 0.322255215, 0, 0, 0, 0.097676062,
NA, 0.261095249, 0.131416203, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.146191646,
0, 0, 0, 0, 0.086854728, 0, 0, 0, 0.023023646, 0.080959767, NA,
0.034786642, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.09469697,
0, 0, 0, 0.024480341, 0.049917782, 0.042613636, NA, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.255554962, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.765625,
0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0,
0, 0.040201005, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.041930937, 0, 0.192970073,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0.030562035,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0,
0, 0, 0, 0, 0, 0.151121606, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, NA, 0.039751553, 0, 0, 0, 0, 0, 0.026693325, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.011428571, NA), .Dim = c(23L,
23L), .Dimnames = list(c("sp1", "sp2", "sp3", "sp4", "sp5", "sp6",
"sp7", "sp8", "sp9", "sp10", "sp11", "sp12", "sp13", "sp14",
"sp15", "sp16", "sp17", "sp18", "sp19", "sp20", "sp21", "sp22",
"sp23"), c("sp1", "sp2", "sp3", "sp4", "sp5", "sp6", "sp7", "sp8",
"sp9", "sp10", "sp11", "sp12", "sp13", "sp14", "sp15", "sp16",
"sp17", "sp18", "sp19", "sp20", "sp21", "sp22", "sp23")))
This is my script:
library (igraph)
View (data)
class (data)
data= data.matrix(data, rownames.force = NA)
class (data)
graph <- graph_from_adjacency_matrix(data, mode = "directed", weighted = TRUE)
as_edgelist(graph, names=F)
as_adjacency_matrix(graph, attr="weight")
as_data_frame(graph, what="edges")
as_data_frame(graph, what="vertices")
graph = simplify(graph, edge.attr.comb=list(weight="sum","ignore"))
deg <- degree(graph, mode="all")
L <- layout_in_circle(graph)
plot(graph, edge.arrow.size=.1, vertex.color="black",vertex.size=deg*1.5,
vertex.frame.color="black", vertex.label.color="grey10", vertex.label.degree=-pi/6,
vertex.shape="circle",vertex.label.cex=1, vertex.label.dist=2,
vertex.label.font=1, edge.arrow.size=8, edge.width=0, edge.curved=0, edge.color="black",
edge.lty=1, layout=L)
This code above generated this graph for me:
Can someone help me? I'm really confused about that. I don't have much experience with graphs. Thank you.

Remove multiple matching columns from multiple character string

I have a dataframe of over 200 variables, many of which end with a code for a given species. I want to eliminate any columns that contain one of several codes, contained in a separate vector of character strings. How can I remove these multiple columns matching the multiple codes at the same time? The column names don't match the code values exactly, but contain the codes at the end of the column name. For example:
"rev230" "rev3360" "rev3508"
Manually, I've done this (using the dplyr package):
sub = select(df, -contains("3781"), -contains("3751"), -contains("1408"),
-contains("1409"), -contains("4469"), -contains("1789"), -contains("4559"),
-contains("1453"),-contains("8"), -contains("3508"), -contains("4656"),
-contains("5131"), -contains("9999"))
This gets me what I want (eliminating all columns that contain data on the species matching these codes), but obviously this is very tedious.
I'd like something more like:
sub = select(df, -contains(species$codes))
# I realize this isn't the right syntax
I tried a loop to remove individual columns, using something like this:
foreach(i=1:length(species$codes), .combine=rbind)%do%
select(df, -contains(species$codes[i]))
but that didn't work either. Thanks in advance!
reproducible example:
Species codes (contained in a character vector):
dput(species)
c("3754", "3755", "3758", "3764", "3765", "3771", "3772", "3782",
"3761", "3762", "3763", "3767", "3768", "1790", "1412", "1413",
"1416", "1422", "1423", "1424", "1425", "1426", "1410", "1411",
"1414", "1415", "1420", "3770", "4740", "4470", "4472", "4474",
"4476", "4479", "4480", "1812", "1815", "1799", "4560", "3810",
"1440", "1441", "3302", "3295", "3560", "3360", "1940", "3840",
"570", "1050", "4710", "230")
Here are the first 10 rows of the data, with only columns for species data
dput(logsub)
structure(list(lbs3781 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 708), lbs3764 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3765 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 708), lbs3758 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3755 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3782 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), lbs3751 = c(0, 0, 4, 0, 0, 0, 21, 0, 18, 0), lbs3761 = c(0,
0, 0, 0, 0, 0, 18, 0, 0, 0), lbs3762 = c(0, 0, 4, 0, 0, 0, 3,
0, 0, 0), lbs3763 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3767 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3768 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), lbs3754 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3771 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3772 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), lbs1790 = c(0, 0, 0, 0, 0, 0, 0, 0, 18, 0), lbs1409 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 86), lbs1411 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), lbs1414 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs1415 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 86), lbs4740 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), lbs1420 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3770 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), lbs1408 = c(2508, 785, 57, 0, 132,
5003, 18, 104, 636, 0), lbs1412 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0), lbs1413 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs1416 = c(2331,
654, 57, 0, 81, 4284, 15, 104, 120, 0), lbs1422 = c(177, 0, 0,
0, 51, 719, 3, 0, 0, 0), lbs1423 = c(0, 131, 0, 0, 0, 0, 0, 0,
502, 0), lbs1424 = c(0, 0, 0, 0, 0, 0, 0, 0, 14, 0), lbs1425 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), lbs1426 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), lbs1410 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs4469 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), lbs4470 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), lbs4472 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
lbs4474 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs4476 = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), lbs4479 = c(0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), lbs4480 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0), lbs1789 = c(0, 0, 0, 863, 0, 0, 0, 0,
0, 98), lbs1812 = c(0, 0, 0, 863, 0, 0, 0, 0, 0, 27), lbs1815 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 71), lbs1799 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), lbs4559 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 12),
lbs4560 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 12), lbs3810 = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), lbs1453 = c(0, 0, 5,
0, 0, 0, 21, 0, 15, 235), lbs1440 = c(0, 0, 5, 0, 0, 0, 21,
0, 15, 0), lbs1441 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3560 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3302 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 235), lbs3295 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
lbs0008 = c(0, 97, 99, 0, 0, 0, 0, 0, 0, 0), lbs1940 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3840 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), lbs1050 = c(0, 0, 31, 0, 0, 0, 0, 0, 0, 0),
lbs4710 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs570 = c(0,
97, 68, 0, 0, 0, 0, 0, 0, 0), lbs230 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), lbs3360 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), lbs3508 = c(0,
0, 5043, 0, 0, 0, 0, 0, 0, 0), lbs4656 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0), lbs9999 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
rev3781 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1688.144979), rev3764 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), rev3765 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 1688.144979), rev3758 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0), rev3755 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev3782 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), rev3751 = c(0, 0, 7.063636364,
0, 0, 0, 33.44605263, 0, 32.53608247, 0), rev3761 = c(0,
0, 0, 0, 0, 0, 27.34105263, 0, 0, 0), rev3762 = c(0, 0, 7.063636364,
0, 0, 0, 6.105, 0, 0, 0), rev3763 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), rev3767 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev3768 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), rev3754 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), rev3771 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev3772 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), rev1790 = c(0, 0, 0, 0, 0, 0,
0, 0, 32.53608247, 0), rev1409 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 260.0068669), rev1411 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), rev1414 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev1415 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 260.0068669), rev4740 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0), rev1420 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0), rev3770 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev1408 = c(6349.327025,
2014.2837, 142.8362084, 0, 339.5618788, 13265.98305, 41.94345809,
235.6862428, 1835.487932, 0), rev1412 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0), rev1413 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
rev1416 = c(5841.249152, 1623.155767, 142.8362084, 0, 194.2835976,
11101.38378, 33.99320809, 235.6862428, 299.2968186, 0), rev1422 = c(508.0778723,
0, 0, 0, 145.2782813, 2164.599274, 7.95025, 0, 0, 0), rev1423 = c(0,
391.1279328, 0, 0, 0, 0, 0, 0, 1494.676782, 0), rev1424 = c(0,
0, 0, 0, 0, 0, 0, 0, 41.51433134, 0), rev1425 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0), rev1426 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0), rev1410 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev4469 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), rev4470 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), rev4472 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev4474 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), rev4476 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), rev4479 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), rev4480 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev1789 = c(0,
0, 0, 963.8520574, 0, 0, 0, 0, 0, 95.34540063), rev1812 = c(0,
0, 0, 963.8520574, 0, 0, 0, 0, 0, 30.02711217), rev1815 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 65.31828847), rev1799 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0), rev4559 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 12.94965112), rev4560 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 12.94965112
), rev3810 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev1453 = c(0,
0, 3.505617978, 0, 0, 0, 13.9460241, 0, 10.93726937, 225.778089
), rev1440 = c(0, 0, 3.505617978, 0, 0, 0, 13.9460241, 0,
10.93726937, 0), rev1441 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), rev3560 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev3302 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 225.778089), rev3295 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0), rev0008 = c(0, 180.3441341, 169.7750491,
0, 0, 0, 0, 0, 0, 0), rev1940 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0), rev3840 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev1050 = c(0,
0, 48.71428571, 0, 0, 0, 0, 0, 0, 0), rev4710 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0), rev570 = c(0, 180.3441341, 121.0607634,
0, 0, 0, 0, 0, 0, 0), rev230 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0), rev3360 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), rev3508 = c(0,
0, 2620.957866, 0, 0, 0, 0, 0, 0, 0), rev4656 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0), rev9999 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0)), .Names = c("lbs3781", "lbs3764", "lbs3765", "lbs3758",
"lbs3755", "lbs3782", "lbs3751", "lbs3761", "lbs3762", "lbs3763",
"lbs3767", "lbs3768", "lbs3754", "lbs3771", "lbs3772", "lbs1790",
"lbs1409", "lbs1411", "lbs1414", "lbs1415", "lbs4740", "lbs1420",
"lbs3770", "lbs1408", "lbs1412", "lbs1413", "lbs1416", "lbs1422",
"lbs1423", "lbs1424", "lbs1425", "lbs1426", "lbs1410", "lbs4469",
"lbs4470", "lbs4472", "lbs4474", "lbs4476", "lbs4479", "lbs4480",
"lbs1789", "lbs1812", "lbs1815", "lbs1799", "lbs4559", "lbs4560",
"lbs3810", "lbs1453", "lbs1440", "lbs1441", "lbs3560", "lbs3302",
"lbs3295", "lbs0008", "lbs1940", "lbs3840", "lbs1050", "lbs4710",
"lbs570", "lbs230", "lbs3360", "lbs3508", "lbs4656", "lbs9999",
"rev3781", "rev3764", "rev3765", "rev3758", "rev3755", "rev3782",
"rev3751", "rev3761", "rev3762", "rev3763", "rev3767", "rev3768",
"rev3754", "rev3771", "rev3772", "rev1790", "rev1409", "rev1411",
"rev1414", "rev1415", "rev4740", "rev1420", "rev3770", "rev1408",
"rev1412", "rev1413", "rev1416", "rev1422", "rev1423", "rev1424",
"rev1425", "rev1426", "rev1410", "rev4469", "rev4470", "rev4472",
"rev4474", "rev4476", "rev4479", "rev4480", "rev1789", "rev1812",
"rev1815", "rev1799", "rev4559", "rev4560", "rev3810", "rev1453",
"rev1440", "rev1441", "rev3560", "rev3302", "rev3295", "rev0008",
"rev1940", "rev3840", "rev1050", "rev4710", "rev570", "rev230",
"rev3360", "rev3508", "rev4656", "rev9999"), row.names = c(34367L,
48646L, 48715L, 48717L, 48722L, 48724L, 48743L, 48744L, 48781L,
48783L), class = "data.frame")

One options is select_if with grepl, as grepl returns a logical vector and you can include multiple patterns you want to match on via |.
A simple case where you want to remove two species would look like:
select_if(df, !grepl("3781|3751", names(df)) )
Aggregate all of your species into the right form for grepl via paste with collapse, and then use this within grepl.
select_if(df, !grepl(paste(species, collapse = "|"), names(df)) )

Is it possible to do in the loop ? Clustering/heatmaps

I would like to run the whole code in the loop because there will be much more tables than one as in the example. I don't have much experience in doing loops so I'd be so grateful for the help.
That's the code which I would like to run in the loop:
cluster1 <- tbl_cluster[tbl_cluster$fit.cluster == 1,]
cluster1 <- cluster1[,-25]
cluster1 <- as.matrix(cluster1[,2:24])
mode(cluster1)<-"numeric"
heatmap.2(cluster1,
Colv = NA,
scale = "none",
dendrogram = "row",
col=rainbow(256, s = 1, v = 1, start = 0, end = 2/6, alpha = 1),
density.info = "none",
trace = "none",
key = FALSE,
cexCol=0.5,
cexRow=1)
mtext(paste("Cluster 1",""), side=3, line=-1, cex=2, col="black")
mtext("Size in kDa", side=1, line=3.5, cex=1.3, col="black")
I would like to create a pdf file and keep all of those heatmaps in the same pdf, just each heatmap on new page.
In total there is 50 clusters: cluster1, cluster2, cluster3 and so on...
If I have to do it manually and I would write next code like that:
cluster2 <- tbl_cluster[tbl_cluster$fit.cluster == 2,]
cluster2 <- cluster1[,-25]
cluster2 <- as.matrix(cluster2[,2:24])
mode(cluster2)<-"numeric"
heatmap.2(cluster2,
Colv = NA,
scale = "none",
dendrogram = "row",
col=rainbow(256, s = 1, v = 1, start = 0, end = 2/6, alpha = 1),
density.info = "none",
trace = "none",
key = FALSE,
cexCol=0.5,
cexRow=1)
mtext(paste("Cluster 2",""), side=3, line=-1, cex=2, col="black")
mtext("Size in kDa", side=1, line=3.5, cex=1.3, col="black")
Cluster1:
> dput(cluster1)
structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.16752067, 0, 0, 0, 0, 0, 0,
0, 1, 0.663889907, 0.44201215, 1, 0.955735729, 0.746154794, 0.805581104,
1, 0.535148598, 1, 0.750189517, 0.531915145, 0.904554192, 0.580052296,
0.584708494, 0.50381622, 1, 0.689930171, 1, 0.492607902, 0.592845546,
0.784022307, 0.713841276, 0.735311924, 0.485931998, 0.555872624,
0.637681873, 1, 0.608332643, 0.471510245, 0.742100525, 1, 0.725036951,
0.636103642, 0.442522885, 0.781087935, 0.63953839, 1, 1, 0.46976386,
1, 1, 1, 0.75983123, 1, 0.86259757, 1, 1, 0.7087314, 1, 1, 1,
0.50654411, 1, 0.69987908, 1, 1, 1, 1, 1, 1, 1, 1, 0.74120003,
1, 1, 1, 0.73758981, 1, 1, 1, 1, 0.64615661, 0.83194495, 0.80859968,
0.51178152, 0.33254192, 0.44084187, 0.32018398, 0.60794852, 0.55977902,
0.53444753, 0.75018952, 0.76595757, 0.7087314, 0.63280451, 0.58470849,
0.31688716, 0.50654411, 0.68993017, 0.45147891, 0.6658817, 0.60364123,
0.49194389, 0.55370463, 0.48847424, 0.44031246, 0.55587262, 0.60699513,
0.58036173, 0.54545093, 0.44738082, 0.51232832, 0.5531822, 0.35779526,
0.45525701, 0.75091808, 0.72445096, 0, 0.065540432, 0, 0, 0.498637407,
0.099666444, 0.267858247, 0.160618801, 0.036112045, 0.10206614,
0, 0.053537577, 0.292234638, 0.615220442, 0.526821051, 0.245979954,
0.209517328, 0, 0, 0.174034334, 0.145034071, 0.240228383, 0.167734427,
0, 0.243614373, 0.358657498, 0.565901472, 0.134088752, 0.204292959,
0, 0.370633918, 0.153763271, 0, 0.136791753, 0.100906512, 0,
0, 0, 0, 0, 0, 0.029860988, 0.192836939, 0.01099398, 0, 0, 0,
0.044629467, 0.219880408, 0, 0, 0.165085571, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.208989308, 0.355930559, 0.054875021, 0, 0.278744359,
0.094030066, 0, 0.202269046, 0, 0, 0, 0, 0, 0, 0, 0, 0.31009524,
0, 0, 0, 0, 0, 0, 0, 0, 0.16273216, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.23948029, 0, 0, 0, 0, 0, 0, 0.38456462, 0, 0, 0, 0, 0, 0,
0, 0, 0.31366311, 0, 0, 0, 0, 0, 0, 0, 0, 0.16376326, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.14738849, 0, 0, 0, 0, 0, 0, 0.13396394,
0, 0, 0, 0, 0, 0, 0, 0, 0.28847861, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.08922821, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), .Dim = c(36L, 23L), .Dimnames = list(c("at1g02140.1",
"at1g06130.2", "at1g08110.3", "at1g09830.1", "at1g11930.2", "at1g32470.1",
"at1g47128.1", "at1g62380.1", "at1g66430.1", "at1g75270.1", "at2g25070.1",
"at2g37660.1", "at2g42590.1", "at2g47450.1", "at3g01520.1", "at3g02780.1",
"at3g07470.1", "at3g07720.1", "at3g15840.4", "at3g19010.1", "at3g21750.1",
"at3g29185.1", "at4g00490.1", "at4g16770.1", "at4g29590.1", "at4g37000.1",
"at4g39260.1", "at4g39970.1", "at5g04430.1", "at5g12210.2", "at5g24400.1",
"at5g36160.1", "at5g59420.1", "at5g63980.1", "at5g66530.2", "at5g67150.1"
), c("X20", "X52.5", "X81", "X110", "X140.5", "X189", "X222.5",
"X278", "X340", "X397", "X453.5", "X529", "X580", "X630.5", "X683.5",
"X735.5", "X784", "X832", "X882.5", "X926.5", "X973", "X1108",
"X1200")))
And that's how cluster2 looks like before removing one column and set it as a matrix:
> dput(cluster2)
structure(list(X10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), X20 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), X52.5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), X81 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X110 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X140.5 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X189 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X222.5 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X278 = c(0,
0, 0, 0, 0, 0, 0.21749847, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.28402318, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), X340 = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1), X397 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.44653495, 0, 0, 0, 0.45794332, 0.17105234, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.43856849, 0, 0, 0, 0, 0, 0.2103673,
0, 0, 0, 0, 0.3364729, 0, 0, 0, 0, 0, 0, 0, 0, 0.13032743, 0,
0, 0, 0, 0.27423546, 0, 0, 0, 0.28894321, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X453.5 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03595661, 0, 0, 0, 0, 0.17105234,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.08335904,
0, 0, 0, 0, 0.3364729, 0, 0, 0, 0, 0, 0, 0, 0, 0.13032743, 0,
0, 0, 0, 0, 0, 0, 0, 0.03243995, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), X529 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.02518973, 0, 0, 0, 0, 0.118128928, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.004797445,
0, 0, 0, 0, 0.291561006, 0, 0, 0, 0, 0, 0, 0, 0, 0.04657268,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), X580 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.037388639, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.203744779,
0, 0, 0, 0, 0, 0, 0, 0, 0.09800002, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X630.5 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04831449, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), X683.5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), X735.5 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), X784 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), X832 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), X882.5 = c(0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X926.5 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X973 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X1108 = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), X1200 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), fit.cluster = c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L)), .Names = c("X10", "X20", "X52.5", "X81",
"X110", "X140.5", "X189", "X222.5", "X278", "X340", "X397", "X453.5",
"X529", "X580", "X630.5", "X683.5", "X735.5", "X784", "X832",
"X882.5", "X926.5", "X973", "X1108", "X1200", "fit.cluster"), row.names = c("at1g02880.3",
"at1g03210.1", "at1g03900.1", "at1g11090.1", "at1g11360.4", "at1g18210.2",
"at1g21600.2", "at1g26520.1", "at1g29260.1", "at1g32550.1", "at1g34760.2",
"at1g44790.1", "at1g56430.1", "at1g60000.1", "at1g62480.1", "at1g71790.1",
"at1g73530.1", "at1g75690.1", "at1g77122.1", "at1g79790.2", "at2g02390.1",
"at2g02390.3", "at2g02500.1", "at2g04845.1", "at2g18850.2", "at2g19770.1",
"at2g20270.1", "at2g22570.2", "at2g23610.1", "at2g25950.1", "at2g26060.1",
"at2g29730.1", "at2g30410.2", "at2g31670.1", "at2g32520.1", "at2g33740.1",
"at2g35410.1", "at2g41760.1", "at2g43100.1", "at2g48120.2", "at3g04780.1",
"at3g08010.1", "at3g10620.1", "at3g11210.1", "at3g15190.1", "at3g16450.3",
"at3g16990.1", "at3g17790.1", "at3g18420.1", "at3g19810.1", "at3g21760.1",
"at3g21790.1", "at3g23580.1", "at3g23600.2", "at3g25640.1", "at3g26450.1",
"at3g26900.3", "at3g27060.1", "at3g48700.1", "at3g50440.1", "at3g54900.1",
"at3g55040.1", "at3g56310.1", "at3g58470.1", "at3g60910.1", "at3g63250.2",
"at4g09040.2", "at4g14710.2", "at4g16690.1", "at4g26840.1", "at4g28030.2",
"at4g30550.1", "at4g31310.1", "at4g32930.1", "at4g33950.1", "at4g34090.1",
"at4g37020.1", "at4g37150.1", "at4g37470.1", "at4g38225.2", "at4g38800.1",
"at5g04740.1", "at5g08170.1", "at5g11330.1", "at5g11950.2", "at5g14910.1",
"at5g16440.1", "at5g19680.1", "at5g28830.1", "at5g32450.1", "at5g38480.1",
"at5g40280.1", "at5g41310.1", "at5g42980.1", "at5g43070.1", "at5g44600.1",
"at5g48480.1", "at5g53045.1", "at5g58240.2", "at5g58490.1", "at5g58590.1",
"at5g61500.1", "at5g62180.1"), class = "data.frame")
I can't dput whole tbl_cluster because it's too big...

You can achieve this with a simple for loop. The dynamic variable naming may require use of assign.
k=dim(tbl_cluster)
for(i in 1:k){
cluster <- tbl_cluster[tbl_cluster$fit.cluster == i,]
cluster <- cluster1[,-25]
cluster <- as.matrix(cluster[,2:24])
mode(cluster)<-"numeric"
heatmap.2(cluster,
Colv = NA,
scale = "none",
dendrogram = "row",
col=rainbow(256, s = 1, v = 1, start = 0, end = 2/6, alpha = 1),
density.info = "none",
trace = "none",
key = FALSE,
cexCol=0.5,
cexRow=1)
mtext(paste("Cluster ",i,"_"), side=3, line=-1, cex=2, col="black")
mtext("Size in kDa", side=1, line=3.5, cex=1.3, col="black")
## at this point you can dump the cluster object
}

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