Dear community
I am using ggraph to plot a network analysis. See part 2 in the working
example.
Besides different colors for different groups of nodes:
--> geom_node_point(aes(size = V(network)$hub_score*200, color=
as.factor(V(network)$community)))
I additionally want to consider different colors for different edge groups
The grouping is defined in the edge_list$relationship: negative relationship
= red and positive relationship = darkblue. The code is working in the way
that the groups are separated by two colors. However, the code uses not the
assigned colors. Does anyone have any idea how to adapt the code?
--> geom_edge_arc(curvature=0.3, aes(width=(E(network)$weight/10),
color=c("darkblue", "red")[as.factor(edge_list$relationship)], alpha=0.5)) +
Kind regards
Sibylle
Working example
library(circlize)
library(ggplot2)
library(igraph)
library(tidyverse)
library(RColorBrewer)
library(stringi)
library(scico)
library(plotly)
library(ggraph)
edges_table_Test.csv
Names target weight relationship
B.B A.A 4 pos
C.C A.A 5 pos
D.D A.A 5 neg
E.E A.A 5 neg
F.F A.A 1 pos
C.C B.B 5 pos
E.E B.B 1 pos
F.F B.B 2 pos
A.A C.C 5 pos
B.B C.C 1 pos
D.D C.C 5 pos
E.E C.C 5 pos
F.F C.C 3 pos
A.A D.D 5 neg
B.B D.D 1 neg
C.C D.D 5 neg
E.E D.D 5 neg
F.F D.D 4 neg
A.A E.E 5 neg
B.B E.E 1 neg
C.C E.E 4 neg
D.D E.E 5 neg
F.F E.E 5 pos
A.A F.F 5 pos
B.B F.F 1 pos
C.C F.F 2 pos
D.D F.F 3 pos
E.E F.F 4 pos
F.F F.F 5 pos
edge_list<-read.csv("edges_table_Test.csv")
network <- graph_from_data_frame(aes_collapsed, directed= FALSE,
vertices = details)
temp<-cluster_optimal(network)
temp<-cbind(membership=temp$membership, Names=temp$name) aes_collapsed <-
aes_collapsed %>%
merge(temp, by="Names")
network <- network %>%
set_edge_attr(name = "type", value = factor(aes_collapsed$Names,
ordered =
is.ordered(V(network)$name))) %>%
set_edge_attr(name = "membership", value = aes_collapsed$membership) %>%
set_edge_attr(name = "color",
value = c(viridis::viridis(5))
[match(E(.)$type, c(factor(V(.)$name)))]) %>%
set_vertex_attr(name = "trans_v_net", value = c(transitivity(., type =
"local"))) %>%
set_vertex_attr(name = "hub_score", value = c(hub_score(.)$vector)) %>%
set_vertex_attr(name = "color",
value = c(viridis::viridis((5)))
[match(V(.)$name, c(factor(V(.)$name)))]) %>%
set_vertex_attr(name= "community", value=cluster_optimal(.)$membership)
clrs<-scico(3, palette = "batlow")
### part 1: network plot
par(bg="black")
network %>% plot(
vertex.color=clrs[V(.)$community],
vertex.size=V(.)$hub_score*5,
vertex.frame.color=V(.)$color,
vertex.label.color="white",
vertex.label.cex=0.5,
vertex.label.family="Helvetica",
vertex.label.font=1,
edge.curved=0.5,
edge.width= network,
layout=layout_with_mds(.))
### part 2: ggraph
tiff("figures/AES_network_bymembership.tiff", width=1000, height=700,
res=120) network %>%
ggraph(., layout = "auto")+
geom_edge_arc(curvature=0.3, aes(width=(E(network)$weight/10),
color=c("darkblue", "red")[as.factor(edge_list$relationship)], alpha=0.5)) +
geom_node_point(aes(size = V(network)$hub_score*200, color=
as.factor(V(network)$community))) +
geom_node_text(aes(label = V(network)$name), size=5, color="white",
repel=T)+
scale_color_scico_d(palette = "batlow")+
scale_edge_width(range = c(0.2,4))+
scale_size(range = c(0.5,20)) +
#scale_edge_color_manual(values = c(scico(21, palette="batlow")))+
theme(plot.background = element_rect(fill = "black"),
legend.position = "right",
panel.background = element_rect(fill = "black"))
dev.off()
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