Re: [R] geom_edge & color
Dear Kommo
Many thanks for the valuable solution.
Sibylle
-Original Message-
From: R-help On Behalf Of Kimmo Elo
Sent: Friday, March 22, 2024 12:10 PM
To: r-help@r-project.org
Subject: Re: [R] geom_edge & color
Hi,
this seems to work (assuming that your problem was the setting of
colours...):
--- snip ---
network %>%
ggraph(., layout = "auto") +
# This produces an error...
# geom_edge_arc(curvature=0.3, aes(width=(E(network)$weight/10),
color=c("darkblue", "red")[as.factor(edge_list$relationship)], alpha=0.5))
+
# ... this works :-)
geom_edge_arc(curvature=0.3, aes(width=E(network)$weight/10,
color=edge_list$relationship), alpha=.5) +
scale_edge_color_manual(values=c("pos"="darkblue", "neg"="red")) +
# This does not work either...
# geom_node_point(aes(size = V(network)$hub_score*200, color=
as.factor(V(network)$community))) +
# ... so try this :)
geom_node_point(aes(size = V(network)$hub_score*200,
color=V(network)$Subcategory_type)) +
geom_node_text(aes(label = V(network)$name), size=3, color="white",
repel=T)+
scale_color_scico_d(palette = "batlow")+
scale_edge_width(range = c(0.2,4))+
scale_size(range = c(0.5,15)) +
theme(plot.background = element_rect(fill = "black"),
legend.position = "right",
panel.background = element_rect(fill = "black"))
--- snip ---
At least in my data created with your code, the object "network" does not have
an attribute "community". I use the existing "Subcategory_type"
instead, because I had no time to debug this problem :-)
I do not know whether this produces what you expect or want to visualise.
HTH,
Kimmo
pe, 2024-03-22 kello 08:59 +0100, sibylle.stoec...@gmx.ch kirjoitti:
> Dear community
>
>
>
> Find enclosed the full working example.
>
>
>
> Many thanks
>
> Sibylle
>
>
>
> Test_cat.csv
>
>
> Names
>
> Subcategory_type
>
> sources.cyto
>
> source
>
> Factor
>
>
> A.A
>
> material
>
> "A"
>
> A
>
> 1
>
>
> B.B
>
> material
>
> "B"
>
> B
>
> 1
>
>
> C.C
>
> regulation
>
> "C"
>
> C
>
> 1
>
>
> D.D
>
> regulation
>
> "D"
>
> D
>
> 1
>
>
> E.E
>
> habitat
>
> "E"
>
> E
>
> 1
>
>
> F.F
>
> cultural
>
> "F"
>
> F
>
> 1
>
>
>
> Test_adjac.csv
>
>
> A.A
>
> B.B
>
> C.C
>
> D.D
>
> E.E
>
> F.F
>
>
> A.A
>
> 0
>
> 0
>
> 5
>
> 5
>
> 5
>
> 5
>
>
> B.B
>
> 4
>
> 0
>
> 1
>
> 1
>
> 1
>
> 1
>
>
> C.C
>
> 5
>
> 5
>
> 0
>
> 5
>
> 4
>
> 2
>
>
> D.D
>
> 5
>
> 0
>
> 5
>
> 0
>
> 5
>
> 3
>
>
> E.E
>
> 5
>
> 1
>
> 5
>
> 5
>
> 0
>
> 4
>
>
> F.F
>
> 1
>
> 2
>
> 3
>
> 4
>
> 5
>
> 5
>
>
>
>
>
> 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
>
> pos
>
>
> F.F
>
> A.A
>
> 1
>
> pos
>
>
> C.C
>
> B.B
>
> 5
>
> pos
>
>
> E.E
>
> B.B
>
> 1
>
> pos
>
>
> F.F
>
> B.B
>
> 2
>
> neg
>
>
> 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
>
> pos
>
>
> C.C
>
> D.D
>
> 5
>
> pos
>
>
> E.E
>
> D.D
>
> 5
>
> pos
>
>
> F.F
>
> D.D
>
> 4
>
> pos
>
>
> A.A
>
> E.E
>
> 5
>
> pos
>
>
> B.B
>
> E.E
>
> 1
>
> pos
>
>
> C.C
>
> E.E
>
> 4
>
> pos
>
>
&
Re: [R] geom_edge & color
.B
>
> F.F
>
> 1
>
> neg
>
>
> C.C
>
> F.F
>
> 2
>
> pos
>
>
> D.D
>
> F.F
>
> 3
>
> pos
>
>
> E.E
>
> F.F
>
> 4
>
> pos
>
>
> F.F
>
> F.F
>
> 5
>
> pos
>
>
>
>
>
>
>
> #upload librairies
>
> library(circlize)
>
> library(ggplot2)
>
> library(igraph)
>
> library(tidyverse)
>
> library(RColorBrewer)
>
> library(stringi)
>
> library(scico)
>
> library(plotly)
>
> library(ggraph)
>
>
>
> #upload
>
> aes<-read.csv("Test_adjac.csv", row.names = 1)
>
> details<-read.csv("Test_cat.csv")
>
>
>
>
>
> # adjacency table
>
> aes_collapsed<-aes %>%
>
> rownames_to_column(var='Names') %>%
>
> tidyr::gather(target, weight, 1:ncol(aes)+1) %>%
>
> dplyr::filter(weight != 0) %>%
>
> mutate(weight = ifelse(weight == "-1", 0, weight)) # here 0 = negative
> values
>
>
>
> write.csv(aes_collapsed, "edges_table-Test.csv", row.names = F)
>
> edge_list<-read.csv("edges_table-Test.csv")
>
>
>
>
>
> #create network and add some necessary attributes (vertices) for the plot
>
>
>
> network <- graph_from_data_frame(aes_collapsed, directed= FALSE,
>
> vertices = details)
>
>
>
> ### network and vertex with 'subcategory_type'
>
>
>
> 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(21))
>
> [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(.)$vector)) %>%
>
> set_vertex_attr(name = "color",
>
> value = c(viridis::viridis((21)))
>
> [match(V(.)$name, c(factor(V(.)$name)))]) %>%
>
> set_vertex_attr(name= "community",
> value=cluster_optimal(.)$Subcategory_type)
>
>
>
> clrs<-scico(3, palette = "batlow")
>
>
>
> windowsFonts(Helvetica = windowsFont("Helvetica"))
>
>
>
> par(bg="black")
>
> network %>% plot(
>
> vertex.color=clrs[V(.)$community],
>
> vertex.size=V(.)$hub_score*20,
>
> vertex.frame.color=V(.)$color,
>
> vertex.label.color="white",
>
> vertex.label.cex=0.4,
>
> vertex.label.family="Helvetica",
>
> vertex.label.font=0.75,
>
> edge.curved=0.5,
>
> edge.width= E(.)$weight,
>
> edge.color = ifelse(edge_list$relationship == "pos", "blue", "red"),
>
> layout=layout_with_mds(.))
>
>
>
> tiff("figures/Test_network_bysubcatecory.tiff", width=1000, height=900,
> res=120)
>
> network %>%
>
> ggraph(., layout = "auto")+
>
> geom_edge_arc(curvature=0.3, aes(width=(E(network)$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=3, color="white",
> repel=T)+
>
> scale_color_scico_d(palette = "batlow")+
>
> scale_edge_width(range = c(0.2,4))+
>
> scale_size(range = c(0.5,15)) +
>
> theme(plot.background = element_rect(fill = "black"),
>
> legend.position = "right",
>
> panel.background = element_rect(fill = "black"))
>
> dev.off()
>
>
>
> -Original Message-
> From: R-help On Behalf Of Kimmo Elo
> Sent: Thursday, March 21, 2024 10:51 AM
> To: r-help@r-project.org
> Subject: Re: [R] geom_edge & color
>
>
>
> Dear Sibylle,
>
>
&
Re: [R] geom_edge & color
Dear community
Find enclosed the full working example.
Many thanks
Sibylle
Test_cat.csv
Names
Subcategory_type
sources.cyto
source
Factor
A.A
material
"A"
A
1
B.B
material
"B"
B
1
C.C
regulation
"C"
C
1
D.D
regulation
"D"
D
1
E.E
habitat
"E"
E
1
F.F
cultural
"F"
F
1
Test_adjac.csv
A.A
B.B
C.C
D.D
E.E
F.F
A.A
0
0
5
5
5
5
B.B
4
0
1
1
1
1
C.C
5
5
0
5
4
2
D.D
5
0
5
0
5
3
E.E
5
1
5
5
0
4
F.F
1
2
3
4
5
5
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
pos
F.F
A.A
1
pos
C.C
B.B
5
pos
E.E
B.B
1
pos
F.F
B.B
2
neg
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
pos
C.C
D.D
5
pos
E.E
D.D
5
pos
F.F
D.D
4
pos
A.A
E.E
5
pos
B.B
E.E
1
pos
C.C
E.E
4
pos
D.D
E.E
5
pos
F.F
E.E
5
pos
A.A
F.F
5
pos
B.B
F.F
1
neg
C.C
F.F
2
pos
D.D
F.F
3
pos
E.E
F.F
4
pos
F.F
F.F
5
pos
#upload librairies
library(circlize)
library(ggplot2)
library(igraph)
library(tidyverse)
library(RColorBrewer)
library(stringi)
library(scico)
library(plotly)
library(ggraph)
#upload
aes<-read.csv("Test_adjac.csv", row.names = 1)
details<-read.csv("Test_cat.csv")
# adjacency table
aes_collapsed<-aes %>%
rownames_to_column(var='Names') %>%
tidyr::gather(target, weight, 1:ncol(aes)+1) %>%
dplyr::filter(weight != 0) %>%
mutate(weight = ifelse(weight == "-1", 0, weight)) # here 0 = negative values
write.csv(aes_collapsed, "edges_table-Test.csv", row.names = F)
edge_list<-read.csv("edges_table-Test.csv")
#create network and add some necessary attributes (vertices) for the plot
network <- graph_from_data_frame(aes_collapsed, directed= FALSE,
vertices = details)
### network and vertex with 'subcategory_type'
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$membership) %>%
set_edge_attr(name = "color",
value = c(viridis::viridis(21))
[match(E(.)$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((21)))
[match(V(.)$name, c(factor(V(.)$name)))]) %>%
set_vertex_attr(name= "community", value=cluster_optimal(.)$Subcategory_type)
clrs<-scico(3, palette = "batlow")
windowsFonts(Helvetica = windowsFont("Helvetica"))
par(bg="black")
network %>% plot(
vertex.color=clrs[V(.)$community],
vertex.size=V(.)$hub_score*20,
vertex.frame.color=V(.)$color,
vertex.label.color="white",
vertex.label.cex=0.4,
vertex.label.family="Helvetica",
vertex.label.font=0.75,
edge.curved=0.5,
edge.width= E(.)$weight,
edge.color = ifelse(edge_list$relationship == "pos", "blue", "red"),
layout=layout_with_mds(.))
tiff("figures/Test_network_bysubcatecory.tiff", width=1000, height=900, 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)$name), size=3, color="white", repel=T)+
scale_color_scico_d(palette = "batlow")+
scale_edge_width(range = c(0.2,4))+
scale_size(range = c(0.5,15)) +
theme(plot.background = element_rect(fill = "black"),
legend.position = "right",
panel.background = element_rect(fill = "black"))
dev.off()
-Original Message-
From: R-help On Behalf Of Kimmo Elo
Sent: Thursday, March 21, 2024 10:51 AM
To: r-help@r-project.org
Subject: Re: [R] geom_edge & color
Dear Sibylle,
your example is not working! E.g. no data for "aes_
Re: [R] geom_edge & color
Dear Sibylle,
your example is not working! E.g. no data for "aes_collapsed".
Best,
Kimmo
ke, 2024-03-20 kello 19:28 +0100, SIBYLLE STÖCKLI via R-help kirjoitti:
> Dear community
>
> I am using ggraph to plot a network analysis. See part 2 in the working
> example.
> Besides different colors for different groups of nodes:
> --> geom_node_point(aes(size = V(network)$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)$name), size=5, color="white",
> repel=T)+
> scale_color_scico_d(palette = "batlow")+
> scale_edge_width(range = c(0.2,4))+
> scale_size(range = c(0.5,20)) +
> #scale_edge_color_manual(values = c(scico(21, palette="batlow")))+
> theme(plot.background = element_rect(fill = "black"),
> legend.position = "right",
> panel.background = element_rect(fill = "black"))
> dev.off()
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
__
[R] geom_edge & color
Dear community
I am using ggraph to plot a network analysis. See part 2 in the working
example.
Besides different colors for different groups of nodes:
--> geom_node_point(aes(size = V(network)$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 5pos
B.B C.C 1pos
D.D C.C 5 pos
E.E C.C 5 pos
F.F C.C 3 pos
A.A D.D 5neg
B.B D.D 1neg
C.C D.D 5neg
E.E D.D 5neg
F.F D.D 4 neg
A.A E.E 5 neg
B.B E.E 1neg
C.C E.E 4neg
D.D E.E 5neg
F.F E.E 5 pos
A.A F.F 5pos
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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