问题描述
我想绘制我的图表。我提供了一个样本数据集。 我使用simple去除循环,但它完全影响了数据的结构。当我在不使用简化的情况下绘制数据时,我有正确的顶点和边缘着色,但存在循环。
我使用了simple 来移除循环和它后面的颜色,这是错误的,因为它的每个节点和边都应该与我在代码中定义的颜色相同。
有谁知道如何去除绘图中的循环以不影响数据结构?
情节 1:正确着色但有循环
情节 2:使用简化后的错误着色
代码:
X user.screen_name child parent in_reply_to_screen_name vaccine_type label
1 0 TweeetLorraine 1.392218e+18 1.392218e+18 1 AstraZeneca 0
2 1 phldenault 1.393259e+18 1.392218e+18 TweeetLorraine AstraZeneca 2
41 40 ElizabethDuncan 1.392297e+18 1.392218e+18 TweeetLorraine AstraZeneca 2
42 41 7Rose75 1.392294e+18 1.392218e+18 TweeetLorraine AstraZeneca 1
43 42 wh0careswh0 1.392336e+18 1.392294e+18 7Rose75 AstraZeneca 0
44 43 T_ProudVeteran 1.392330e+18 1.392294e+18 7Rose75 AstraZeneca 2
45 44 TweeetLorraine 1.392294e+18 1.392294e+18 7Rose75 AstraZeneca 2
46 45 norlaine 1.392288e+18 1.392218e+18 TweeetLorraine AstraZeneca 2
47 46 elham95264575 1.393212e+18 1.392288e+18 norlaine AstraZeneca 1
48 47 soyfreemike 1.392387e+18 1.392288e+18 norlaine AstraZeneca 0
49 48 KMTCarr 1.392288e+18 1.392218e+18 TweeetLorraine AstraZeneca 2
50 49 angela_petta 1.392283e+18 1.392218e+18 TweeetLorraine AstraZeneca 2
51 50 lhoneyimhome 1.392272e+18 1.392218e+18 TweeetLorraine AstraZeneca 2
net1 <- graph_from_data_frame(df %>% select("child","parent"))
rel = get.adjacency(graph,sparse = FALSE)
graph = simplify(net1,remove.loops=TRUE)
graph
summary(graph)
vertex_attr(graph,"label") <- df$label
#Set edge attribute:
edge_attr(graph,"label") <- df$label
E(graph)$color[E(graph)$label == 2] <- '#B3DE69' #green
E(graph)$color[E(graph)$label == 1] <- '#80B1D3' #yellow
E(graph)$color[E(graph)$label == 0] <- '#FB8072'#purple
V(graph)$color[V(graph)$label == 2] <- '#B3DE69'
V(graph)$color[V(graph)$label == 1] <- '#80B1D3'
V(graph)$color[V(graph)$label == 0] <- '#FB8072'
g<-c('#B3DE69','#80B1D3','#FB8072')
plot(graph,layout=layout.fruchterman.reingold,vertex.frame.color=NA,vertex.label.color="black",edge.label = NA,vertex.size=3,usecurve=TRUE,edge.lwd=0.02,vertex.dist=10,vertex.label.dist=2,vertex.label.cex=0.9,pad=0.9,alpha=80,edge.arrow.size=.1)
legend("bottomleft",legend= c("Positive","Neutral","Negative"),col=g,pch=19,pt.cex=1.5,bty="n",title="Label category")
title(main="Visualization ",cex.main=1)
解决方法
通常在数据框中操作数据比在 igraph 属性中更容易。我建议在将其转换为图形之前准备好数据框中的所有内容。然后 simplify
将按预期完成它的工作,您可以根据需要绘制或分析您的图表。如果 simplify
为 remove.multiple
,要保留 TRUE
中的边属性,您需要定义 edge.attr.comb
参数。下面我使用了 dplyr::first
,这意味着我们在组合多条边时选择第一个值。
编辑:在 simplify
中使用 OP 数据并保留边缘属性
library(igraph)
library(dplyr)
library(magrittr)
library(tibble)
library(rlang)
library(readr)
df <- read_tsv('so_user142_data.tsv',col_types = cols())
color_map <- c(
'0' = '#B3DE69',# green
'1' = '#80B1D3',# blue
'2' = '#FB8072' # salmon
)
df %<>%
mutate(label = recode(label,!!!color_map)) %>%
rename(color = label) %>%
select(
child = user.screen_name,parent = in_reply_to_screen_name,color
)
vertex_colors <-
bind_rows(
df %>% select(name = child,color),df %>% select(name = parent,color)
) %>%
group_by(name) %>%
summarize_all(first) %>%
ungroup
g <-
df %>%
graph_from_data_frame(vertices = vertex_colors) %>%
simplify(edge.attr.comb = first)
png('so_user142_graph.png',800,800)
plot(
g,layout = layout.fruchterman.reingold,vertex.frame.color = NA,vertex.label.color = 'black',vertex.size = 7,edge.curved = TRUE,edge.lwd = 0.4,vertex.dist = 10,vertex.label.dist = 1.2,vertex.label.cex = 1.2,pad = 0.9,alpha = 80,edge.arrow.size = 1.
)
dev.off()