How to make neural edges more dynamic in neural plot? - r

Question

I have data which represents transit between UK cities.
Transit: if there is a transit between these two cities = 1, otherwise
=0
ave.pas: average number of passengers
.
library(plotly)
library(ggraph)
library(tidyverse)
library(tidygraph)
library(igraph)
library(edgebundleR)
df2 <- data.frame (City1 = c("London", "London", "London", "London" ,"Liverpool","Liverpool","Liverpool" , "Manchester", "Manchester", "Bristol"),
City2 = c("Liverpool", "Manchester", "Bristol","Derby", "Manchester", "Bristol","Derby","Bristol","Derby","Derby"),
Transit = c(1,0,1,1,1,1,1,1,0,1),
ave.pas = c(10,0,11,24,40,45,12,34,0,29))
df:
City1 City2 Transit ave.pas
1 London Liverpool 1 10
2 London Manchester 0 0
3 London Bristol 1 11
4 London Derby 1 24
5 Liverpool Manchester 1 40
6 Liverpool Bristol 1 45
7 Liverpool Derby 1 12
8 Manchester Bristol 1 34
9 Manchester Derby 0 0
10 Bristol Derby 1 29
Now I plot circular network:
df <- subset(df2, Transit== 1, select = c("City1","City2"))
edgebundle(graph.data.frame(df),directed=F,tension=0.1,fontsize = 10)
My goal is to set the size or colour's intensitvity of edges based on the corresponding value in 'ave.pas' variable from the dataset
linked links: link1 link2 link3 link4
(Plot must be made using edgebundle() function)

Answer 1

The intensity of the edges in the linked plots appears to be a function of the number of edges joining the vertices. We can make the number of edges equal to the number of passengers, but the problem here is that after a few lines are plotted on top of each other, the intensity stops increasing. It is therefore good for showing the difference between, say, 1 and 3 edges, but the difference between 10 and 30 is much less obvious. As a compromise, we can make the number of edges approximately proportional to the number of passengers. One way to do this is to create the graph from an adjacency matrix:
cities <- unique(c(df2$City1, df$City2))
m <- matrix(0, nrow = length(cities), ncol = length(cities),
dimnames = list(cities, cities))
for(i in seq(nrow(df2))) m[df2[i, 1], df2[i, 2]] <- df2[i, 4]
m <- m/min(m[m > 0])
edgebundle(graph_from_adjacency_matrix(m))