Convert rowname char(X1, X2, ... Xn) to num(1,2,...n)

Convert rowname char(X1, X2, ... Xn) to num(1,2,...n) - r

I've created a new data frame and the rownames got named like char(X1, X2, X3, ... Xn).
In order to merge the new data frame with an old one I need them to be num(1,2,3,...,n).
# Create DB with Topics
df_test <- data.frame(doc_topic_distr)
setDT(df_test, keep.rownames = "doc_id")
I've tried to df_test$doc_id <- as.integer(gsub('[a-zA-Z]', '', df$doc_id))them afterwards, but that's not working. :/
Any clues for this one?
/e:Here we go:
> df_test$doc_id <- gsub('[a-zA-Z]', '', df$doc_id)
Error in df$doc_id : object of type 'closure' is not subsettable
>
> dput(head(doc_topic_distr))
structure(c(0, 0, 0, 0, 0, 0.037037037037037, 0, 0.08, 0, 0,
0, 0, 0.25, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.32,
0, 0, 0.875, 0.407407407407407, 0, 0.16, 0, 0.166666666666667,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.04, 0, 0, 0, 0.0740740740740741, 0, 0.12,
0, 0, 0, 0.037037037037037, 0, 0.04, 0, 0, 0, 0, 0, 0.08, 0,
0, 0, 0.0740740740740741, 0.25, 0, 0, 0, 0.0625, 0.037037037037037,
0, 0, 0, 0.333333333333333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.222222222222222, 0, 0, 0, 0, 0, 0.037037037037037, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0625, 0, 0, 0, 0, 0, 0, 0.037037037037037,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.037037037037037, 0, 0, 0,
0, 0, 0, 0, 0.16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(6L,
31L), .Dimnames = list(c("0", "1", "2", "3", "4", "5"), NULL))
´´´
Many thanks in advance!

Solved it like this:
df <- data.frame(doc_topic_distr)
df <- cbind(doc_id = rownames(df), df)
df$doc_id <- as.numeric(as.character(df$doc_id))

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.

Error in xp %*% W : non-conformable arguments

I am new to R. I am trying to estimate Moran's I result. I have spatial points data over different locations.
I am following this Q&A (2nd answer). When I am running--- patterns <- as.character(interaction(xp > 0, W%*%yp > 0)), then it is showing this error--- Error in xp %*% W : non-conformable arguments. Maybe I am doing something wrong.
Please I appreciate it if someone could help me :)
W <-
structure(c(0, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.2, 0, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 0.2,
0, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 0.2, 0.2, 0, 0.2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 0.2, 0, 0.2, 0.2, 0.2, 0.2, 0.2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0.2, 0.2, 0.2, 0.2, 0, 0.2, 0.2, 0.2, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(77L, 77L), .Dimnames = list(
c("1", "2", "3", "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", "64", "65", "66", "67", "68", "69", "70", "71",
"72", "73", "74", "75", "76", "77"), c("1", "2", "3", "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", "64", "65",
"66", "67", "68", "69", "70", "71", "72", "73", "74", "75",
"76", "77")))
yp <-
structure(c(-0.0983101073646552, -0.0983101073646552, -0.0983101073646552,
-0.0983101073646552, -0.0983101073646552, -0.0983101073646552,
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0.391921068769459, 0.391921068769459, 0.391921068769459, 0.391921068769459,
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0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978), .Dim = c(468L, 1L), "`scaled:center`" = -0.0965273999166667, "`scaled:scale`" = 0.906349890889938)

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.

Match row names and column names of one matrix to another to do element by element calculations

I have two matrices and I need to do a row name and column name match to conduct element by element calculations. The first calculation is phij/pij and the second is ((phij-pij)^2)/pij
The long matrix phij has row names separated by a dash e.g. Aaa-Baa. The column names have no dash. I need to match the part of the row name after the dash i.e. Baa and a column name in the phij matrix to the row name and column name of the smaller matrix pij.
I tried using a for loop but it's not matching the actual row names and column names but instead looks up positions in the sequence.
LR<-phij
ChiSq<-phij
ROWS <- data.frame(ROW0=rownames(phij),
ROW1=substr(rownames(phij),regexpr("-", rownames(phij))+1,nchar(rownames(phij))))
COLNAMES <- c(colnames(phij))
for(rowN in 1:length(ROWS$ROW0)){
for(colN in COLNAMES){
LR[ROWS$ROW0[rowN],colN]<-LR[ROWS$ROW0[rowN],colN]/pij[ROWS$ROW1[rowN],colN]
ChiSq[ROWS$ROW0[rowN],colN]<-((ChiSq[ROWS$ROW0[rowN],colN]-pij[ROWS$ROW1[rowN],colN])^2)/pij[ROWS$ROW1[rowN],colN]
}
}
Data:
phij:
structure(c(0.111111111111111, 0.2, 0, 0, 0, 0.25, 0, 0.666666666666667,
0.166666666666667, 0, 0, 0.666666666666667, 0.5, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0.333333333333333, 0, 1,
0, 0.166666666666667, 0, 0.571428571428571, 0, 0, 0, 0.4, 0.272727272727273,
0, 0, 0, 0, 0, 0, 0, 0, 0.222222222222222, 0.6, 0, 0, 0.25, 0,
0, 0.333333333333333, 0.333333333333333, 0, 0, 0.333333333333333,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 0, 0, 0, 0, 0, 0.2, 0.666666666666667,
0, 0, 0, 0.166666666666667, 0, 0.142857142857143, 1, 0, 0, 0.2,
0.272727272727273, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.222222222222222, 0.2, 0, 0, 0, 0,
0, 0, 0.166666666666667, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0, 0.333333333333333, 0, 0.142857142857143,
0, 0, 0, 0.2, 0.181818181818182, 0, 0, 0, 0, 0, 0, 0, 0, 0.444444444444444,
0, 0, 0, 0.75, 0.5, 0, 0, 0.333333333333333, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0, 1, 1, 0, 0.2, 0, 0, 0, 1, 0.333333333333333,
0, 0.142857142857143, 0, 0, 1, 0, 0.272727272727273, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), class = "table", .Dim = c(49L,
7L), .Dimnames = list(i = c("A-A", "A-Aa", "A-Aaa", "A-B", "A-Ba",
"A-Baa", "A-Caa", "Aa-A", "Aa-Aa", "Aa-Aaa", "Aa-B", "Aa-Ba",
"Aa-Baa", "Aa-Caa", "Aaa-A", "Aaa-Aa", "Aaa-Aaa", "Aaa-B", "Aaa-Ba",
"Aaa-Baa", "Aaa-Caa", "B-A", "B-Aa", "B-Aaa", "B-B", "B-Ba",
"B-Baa", "B-Caa", "Ba-A", "Ba-Aa", "Ba-Aaa", "Ba-B", "Ba-Ba",
"Ba-Baa", "Ba-Caa", "Baa-A", "Baa-Aa", "Baa-Aaa", "Baa-B", "Baa-Ba",
"Baa-Baa", "Baa-Caa", "Caa-A", "Caa-Aa", "Caa-Aaa", "Caa-B",
"Caa-Ba", "Caa-Baa", "Caa-Caa"), j = c("A", "Aa", "Aaa", "B",
"Ba", "Baa", "Caa")))
pij:
structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0.608695652173913, 0.323529411764706,
0.129032258064516, 0.176470588235294, 0.125, 0, 0, 0.173913043478261,
0.323529411764706, 0.258064516129032, 0.294117647058824, 0.25,
0, 0, 0.0869565217391304, 0.235294117647059, 0.419354838709677,
0.352941176470588, 0.25, 0, 0, 0.130434782608696, 0.117647058823529,
0.161290322580645, 0.117647058823529, 0.25, 0, 0, 0, 0, 0.032258064516129,
0.0588235294117647, 0.125, 0, 0, 0, 0, 0, 0, 0, 0), class = "table", .Dim = c(7L,
7L), .Dimnames = list(i = c("Aaa", "Aa", "A", "Baa", "Ba", "B",
"Caa"), j = c("Aaa", "Aa", "A", "Baa", "Ba", "B", "Caa")))

You can create a new matrix of pij which is of same dimension as phij and then perform the calculations that you want.
new_pij <- pij[sub('-.*', '', rownames(phij)), colnames(phij)]
You can then do :
phij/new_pij
and
((phij-new_pij)^2)/new_pij

Find the smallest distance between the profiles

I would like to find the smallest distance between the profiles stored in a data frame. I am interested especially in one row in comparison to the rest of the rows stored in the data frame.
That's a data frame:
structure(list(`10` = 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), `34` = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 393090, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6718400,
0, 311350, 0), `59` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2164949.7,
4834137.6, 0, 0, 0, 1187816.7, 0, 0, 0, 0, 0, 0, 1340912.5, 0
), `84` = c(0, 0, 0, 0, 0, 0, 0, 0, 8607100, 0, 0, 17586713.2,
22629743.6, 0, 0, 0, 2808791.7, 0, 0, 4026222.5, 0, 0, 0, 1981900,
0), `110` = c(2296000, 0, 0, 0, 0, 2140221.7, 0, 0, 5809230.6,
0, 0, 37134898.5, 3861828.7, 2553100, 0, 12075845.8, 0, 0, 1272950,
8695273, 0, 0, 2657180, 2710080, 0), `134` = c(0, 0, 0, 1176150,
0, 1329596.7, 1471000, 0, 6511934, 6511934, 0, 18709227.3, 0,
1041211.2, 0, 6544176.9, 0, 0, 2412651.7, 7724956.9, 2878418.3,
0, 8620131.7, 2386972.8, 0), `165` = c(0, 1226610, 0, 1345098.7,
2083771.9, 0, 1808231.4, 0, 0, 10742997.7, 0, 13060798.9, 0,
538340, 538340, 2791649.5, 0, 0, 6217622, 1316097.1, 4716931.8,
0, 6615816.9, 1510532, 0), `199` = c(0, 1571525, 0, 1903038.3,
1676700, 0, 888832.2, 0, 0, 9084418.6, 0, 11189460.1, 0, 0, 1807662.5,
2564275, 0, 0, 18080359.7, 0, 0, 0, 2397710.2, 1717949.2, 0),
`234` = c(0, 1314900, 2482696, 1325684, 0, 0, 0, 0, 0, 7321432.7,
0, 9843409.2, 0, 0, 1073341.7, 2762775, 0, 0, 9335312.8,
0, 0, 0, 1950788.2, 1509100, 0), `257` = c(0, 1568700, 14604298.7,
940162.2, 0, 0, 0, 0, 0, 4779505.9, 0, 9691692.4, 0, 0, 735290,
2650165, 0, 2311383.7, 5193383.4, 0, 0, 0, 1341998.7, 1225325.6,
0), `362` = c(0, 0, 4190740.5, 288800, 0, 0, 0, 0, 0, 4846634.8,
0, 9574498.7, 0, 0, 0, 1425600, 0, 8339312.1, 3877892.5,
0, 0, 0, 1752866.7, 0, 0), `433` = c(0, 0, 773280, 0, 0,
0, 0, 0, 0, 3926582.8, 3926582.8, 5962586.5, 0, 0, 0, 1041400,
0, 1972909.3, 1895439.4, 0, 0, 0, 963891.2, 0, 1109800),
`506` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9332272, 0, 0, 0,
0, 0, 0, 2219100, 0, 0, 0, 0, 0, 0, 0), `581` = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 4371537.1, 0, 0, 0, 0, 0, 0, 2428800,
0, 0, 0, 0, 0, 0, 0), `652` = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1689871.4, 0, 0, 0, 0, 0, 0, 988399.7, 0, 0, 0, 0, 0,
0, 0), `733` = c(0, 0, 0, 0, 0, 0, 0, 1250100, 0, 0, 1754205.3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `818` = c(0, 0,
0, 0, 0, 0, 0, 517340, 0, 0, 1149227.6, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), `896` = c(0, 0, 0, 0, 0, 0, 0, 579846.7,
0, 0, 985931.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
`972` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 858255.5, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `1039` = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 848993.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0)), .Names = c("10", "34", "59", "84", "110", "134",
"165", "199", "234", "257", "362", "433", "506", "581", "652",
"733", "818", "896", "972", "1039"), row.names = c("Mark_1",
"Mark_2", "Alex_1", "Katrin_1", "Georg_1", "Martin_1",
"Tim_1", "Tom_1", "Mike_1", "Mike_2", "Mike_3",
"Hare_1", "Dea_1", "Monty_1", "Monty_2", "Niko_1",
"Lee_1", "Marq_1", "Otto_1", "Priaq_1", "Surkta_1",
"Norsa_1", "Norsa_2", "Quer_1", "Quer_2"), class = "data.frame")
So the row named Katrin_1 is the one which is interesting for me. I would like to find which rows have the smallest euclidean distance to Katrin_1. Let say 3-5 rows.

Let's get rid of Katrin_1 column with df[!rownames(df) %in% "Katrin_1", ], subtract df["Katrin_1", ] from each of the remaining rows with sweep, find Euclidean distances by squaring the resulting matrix element-wise and using rowSums, use which.min to get the final result:
names(which.min(rowSums(sweep(df[!rownames(df) %in% "Katrin_1", ], 2, as.numeric(df["Katrin_1", ]), `-`)^2)))
# [1] "Mark_2"
This should be much more efficient than using dist as dist would compute all possible distances, while we need need only a few.

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Convert rowname char(X1, X2, ... Xn) to num(1,2,...n) - r

Solved it like this: df <- data.frame(doc_topic_distr) df <- cbind(doc_id = rownames(df), df) df$doc_id <- as.numeric(as.character(df$doc_id))

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

Match row names and column names of one matrix to another to do element by element calculations

Find the smallest distance between the profiles

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