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为树形图添加标签并为群集着色

[英]Add labels to dendrogram and color the clusters

 原文  2016-04-16 18:30:56  0   1    r/ ggplot2/ igraph/ dendrogram/ ggdendro

问题描述

I create a dendrogram using ggdendro in this way: 我用这种方式使用ggdendro创建一个树状图： 

library(igraph)
library(RColorBrewer)
library(GGally)
library(ggplot2)
library(plotrix)
library(extrafont)
library(ggdendro)

# load dataset
net <- read.graph("./dataset/lesmiserables.gml", format = c("gml"))
deg <- igraph::degree(net, mode = "all")

# find communities
girvNew <- cluster_edge_betweenness(net)

girvNew_sizesComm <- sizes(girvNew)
girvNew_numComm <- length(girvNew_sizesComm)

# colors
colorsRainbow <- rainbow(max(membership(girvNew)), alpha = 0.6)

x11()
plot(net,
     vertex.size = plotrix::rescale(deg, c(5, 16)),
     vertex.color = colorsRainbow[membership(girvNew)],
     vertex.frame.color = NA,
     vertex.label = NA,
     vertex.size = 10,
     edge.color = "#d8d8d8",
     layout = layout.fruchterman.reingold,
     main = "Detected communities")

# find dendrogram
girvNew_den <- as.dendrogram(girvNew)

#convert cluster object to use with ggplot
girvNew_dendrogram <- dendro_data(girvNew_den, type = "rectangle") 

x11()
ggdendrogram(girvNew_dendrogram, 
             rotate = TRUE, 
             labels = TRUE,
             segments = TRUE,
             leaf_labels = TRUE, 
             theme_dendro = FALSE)

And I get: 我得到： 在此输入图像描述 在此输入图像描述

My network is: 我的网络是： 

> dput(net)
structure(list(77, FALSE, c(1, 2, 3, 3, 4, 5, 6, 7, 8, 9, 11, 
11, 11, 11, 12, 13, 14, 15, 17, 18, 18, 19, 19, 19, 20, 20, 20, 
20, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 
23, 23, 23, 23, 23, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 
27, 27, 27, 28, 28, 29, 29, 29, 30, 31, 31, 31, 31, 32, 33, 33, 
34, 34, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 37, 37, 38, 38, 
38, 38, 38, 38, 39, 40, 41, 41, 42, 42, 42, 43, 43, 43, 44, 44, 
45, 47, 48, 48, 48, 48, 49, 49, 50, 50, 51, 51, 51, 52, 52, 53, 
54, 54, 54, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 56, 56, 57, 
57, 57, 58, 58, 58, 58, 58, 59, 59, 59, 59, 60, 60, 60, 61, 61, 
61, 61, 61, 61, 62, 62, 62, 62, 62, 62, 62, 62, 63, 63, 63, 63, 
63, 63, 63, 63, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 65, 65, 
65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66, 66, 66, 
66, 67, 68, 68, 68, 68, 68, 68, 69, 69, 69, 69, 69, 69, 69, 70, 
70, 70, 70, 70, 70, 70, 70, 71, 71, 71, 71, 71, 71, 71, 71, 72, 
72, 72, 73, 74, 74, 75, 75, 75, 75, 75, 75, 75, 76, 76, 76, 76, 
76, 76, 76), c(0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 10, 3, 2, 0, 11, 
11, 11, 11, 16, 16, 17, 16, 17, 18, 16, 17, 18, 19, 16, 17, 18, 
19, 20, 16, 17, 18, 19, 20, 21, 16, 17, 18, 19, 20, 21, 22, 12, 
11, 23, 11, 24, 23, 11, 24, 11, 16, 25, 11, 23, 25, 24, 26, 11, 
27, 23, 27, 11, 23, 30, 11, 23, 27, 11, 11, 27, 11, 29, 11, 34, 
29, 34, 35, 11, 29, 34, 35, 36, 11, 29, 34, 35, 36, 37, 11, 29, 
25, 25, 24, 25, 41, 25, 24, 11, 26, 27, 28, 11, 28, 46, 47, 25, 
27, 11, 26, 11, 49, 24, 49, 26, 11, 51, 39, 51, 51, 49, 26, 51, 
49, 39, 54, 26, 11, 16, 25, 41, 48, 49, 55, 55, 41, 48, 55, 48, 
27, 57, 11, 58, 55, 48, 57, 48, 58, 59, 48, 58, 60, 59, 57, 55, 
55, 58, 59, 48, 57, 41, 61, 60, 59, 48, 62, 57, 58, 61, 60, 55, 
55, 62, 48, 63, 58, 61, 60, 59, 57, 11, 63, 64, 48, 62, 58, 61, 
60, 59, 57, 55, 64, 58, 59, 62, 65, 48, 63, 61, 60, 57, 25, 11, 
24, 27, 48, 41, 25, 68, 11, 24, 27, 48, 41, 25, 69, 68, 11, 24, 
27, 41, 58, 27, 69, 68, 70, 11, 48, 41, 25, 26, 27, 11, 48, 48, 
73, 69, 68, 25, 48, 41, 70, 71, 64, 65, 66, 63, 62, 48, 58), 
    c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 12, 11, 10, 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, 47, 46, 39, 40, 41, 42, 43, 44, 
    45, 49, 48, 52, 51, 50, 54, 55, 53, 56, 57, 58, 60, 59, 61, 
    62, 63, 66, 64, 65, 67, 69, 70, 71, 68, 72, 73, 74, 75, 76, 
    77, 79, 78, 82, 83, 80, 81, 87, 88, 84, 85, 86, 93, 94, 89, 
    90, 91, 92, 95, 96, 97, 98, 101, 100, 99, 102, 103, 104, 
    106, 105, 107, 108, 112, 110, 111, 109, 114, 113, 116, 115, 
    119, 118, 117, 121, 120, 122, 125, 124, 123, 131, 132, 133, 
    130, 128, 134, 135, 127, 126, 129, 136, 137, 139, 140, 138, 
    145, 143, 142, 141, 144, 148, 147, 149, 146, 150, 151, 152, 
    153, 158, 157, 154, 156, 155, 164, 162, 159, 163, 160, 161, 
    166, 165, 168, 174, 170, 171, 167, 173, 172, 169, 184, 177, 
    175, 183, 179, 182, 181, 180, 176, 178, 187, 194, 193, 189, 
    192, 191, 190, 188, 185, 186, 200, 196, 197, 203, 202, 198, 
    201, 195, 199, 204, 206, 207, 205, 208, 210, 209, 213, 214, 
    211, 215, 217, 216, 212, 221, 222, 218, 223, 224, 225, 220, 
    219, 230, 233, 226, 232, 231, 228, 227, 229, 236, 234, 235, 
    237, 238, 239, 242, 244, 243, 241, 240, 245, 246, 252, 253, 
    251, 250, 247, 248, 249), c(0, 1, 2, 4, 5, 6, 7, 8, 9, 13, 
    3, 12, 11, 10, 14, 15, 16, 17, 47, 49, 52, 54, 57, 62, 66, 
    69, 72, 73, 75, 77, 82, 87, 93, 102, 106, 112, 114, 119, 
    131, 145, 184, 206, 213, 221, 230, 236, 46, 18, 19, 21, 24, 
    28, 33, 39, 55, 132, 20, 22, 25, 29, 34, 40, 23, 26, 30, 
    35, 41, 27, 31, 36, 42, 32, 37, 43, 38, 44, 45, 48, 51, 58, 
    64, 67, 70, 50, 53, 60, 97, 101, 116, 207, 214, 222, 56, 
    59, 95, 96, 98, 100, 110, 133, 205, 211, 218, 233, 242, 61, 
    103, 113, 118, 125, 130, 234, 63, 65, 71, 74, 104, 111, 143, 
    208, 215, 223, 226, 235, 105, 107, 76, 79, 83, 88, 94, 68, 
    78, 80, 84, 89, 81, 85, 90, 86, 91, 92, 121, 128, 99, 134, 
    139, 164, 210, 217, 224, 232, 244, 108, 109, 135, 140, 142, 
    148, 150, 153, 162, 168, 177, 187, 200, 209, 216, 231, 237, 
    238, 243, 252, 115, 117, 124, 127, 136, 120, 122, 123, 126, 
    129, 137, 138, 141, 147, 158, 159, 174, 175, 194, 144, 149, 
    157, 163, 170, 183, 193, 204, 146, 151, 154, 160, 171, 179, 
    189, 196, 225, 253, 152, 156, 161, 167, 182, 192, 197, 155, 
    166, 173, 181, 191, 203, 165, 172, 180, 190, 202, 169, 176, 
    188, 198, 251, 178, 185, 201, 250, 186, 195, 247, 199, 248, 
    249, 212, 220, 228, 241, 219, 227, 240, 229, 245, 246, 239
    ), c(0, 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 10, 14, 15, 16, 17, 
    18, 18, 19, 21, 24, 28, 33, 39, 48, 50, 53, 57, 62, 64, 67, 
    68, 72, 73, 75, 77, 80, 84, 89, 95, 96, 97, 99, 102, 105, 
    107, 108, 108, 109, 113, 115, 117, 120, 122, 123, 126, 136, 
    138, 141, 146, 150, 153, 159, 167, 175, 185, 195, 204, 205, 
    211, 218, 226, 234, 237, 238, 240, 247, 254), c(0, 10, 10, 
    12, 13, 13, 13, 13, 13, 13, 13, 14, 46, 47, 47, 47, 47, 56, 
    62, 67, 71, 74, 76, 77, 83, 92, 105, 112, 124, 126, 131, 
    132, 132, 132, 132, 136, 139, 141, 142, 142, 144, 144, 153, 
    153, 153, 153, 153, 154, 155, 173, 178, 178, 182, 182, 182, 
    183, 192, 192, 200, 210, 217, 223, 228, 233, 237, 240, 242, 
    243, 243, 247, 250, 252, 253, 253, 254, 254, 254, 254), list(
        c(1, 0, 1), structure(list(), .Names = character(0)), 
        structure(list(id = c(0, 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), label = c("Myriel", 
        "Napoleon", "MlleBaptistine", "MmeMagloire", "CountessDeLo", 
        "Geborand", "Champtercier", "Cravatte", "Count", "OldMan", 
        "Labarre", "Valjean", "Marguerite", "MmeDeR", "Isabeau", 
        "Gervais", "Tholomyes", "Listolier", "Fameuil", "Blacheville", 
        "Favourite", "Dahlia", "Zephine", "Fantine", "MmeThenardier", 
        "Thenardier", "Cosette", "Javert", "Fauchelevent", "Bamatabois", 
        "Perpetue", "Simplice", "Scaufflaire", "Woman1", "Judge", 
        "Champmathieu", "Brevet", "Chenildieu", "Cochepaille", 
        "Pontmercy", "Boulatruelle", "Eponine", "Anzelma", "Woman2", 
        "MotherInnocent", "Gribier", "Jondrette", "MmeBurgon", 
        "Gavroche", "Gillenormand", "Magnon", "MlleGillenormand", 
        "MmePontmercy", "MlleVaubois", "LtGillenormand", "Marius", 
        "BaronessT", "Mabeuf", "Enjolras", "Combeferre", "Prouvaire", 
        "Feuilly", "Courfeyrac", "Bahorel", "Bossuet", "Joly", 
        "Grantaire", "MotherPlutarch", "Gueulemer", "Babet", 
        "Claquesous", "Montparnasse", "Toussaint", "Child1", 
        "Child2", "Brujon", "MmeHucheloup"), maincharacter = c(0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0)), .Names = c("id", "label", "maincharacter"
        )), structure(list(value = c(1, 8, 10, 6, 1, 1, 1, 1, 
        2, 1, 1, 3, 3, 5, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 3, 3, 
        3, 4, 3, 3, 3, 3, 5, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 4, 
        4, 4, 2, 9, 2, 7, 13, 1, 12, 4, 31, 1, 1, 17, 5, 5, 1, 
        1, 8, 1, 1, 1, 2, 1, 2, 3, 2, 1, 1, 2, 1, 3, 2, 3, 3, 
        2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 
        2, 3, 2, 2, 1, 3, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 3, 2, 
        1, 1, 9, 2, 2, 1, 1, 1, 2, 1, 1, 6, 12, 1, 1, 21, 19, 
        1, 2, 5, 4, 1, 1, 1, 1, 1, 7, 7, 6, 1, 4, 15, 5, 6, 2, 
        1, 4, 2, 2, 6, 2, 5, 1, 1, 9, 17, 13, 7, 2, 1, 6, 3, 
        5, 5, 6, 2, 4, 3, 2, 1, 5, 12, 5, 4, 10, 6, 2, 9, 1, 
        1, 5, 7, 3, 5, 5, 5, 2, 5, 1, 2, 3, 3, 1, 2, 2, 1, 1, 
        1, 1, 3, 5, 1, 1, 1, 1, 1, 6, 6, 1, 1, 2, 1, 1, 4, 4, 
        4, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 
        2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), .Names = "value")), 
    <environment>), class = "igraph")

I wish there were names of network nodes on the vertical axis and the cluster lines are the same color of the communities calculated with cluster_edge_betweenness function. 我希望垂直轴上有网络节点的名称，并且簇线与使用cluster_edge_betweenness函数计算的社区的颜色相同。

It really doesn't matter if they are colorful: 它们是否丰富多彩并不重要：

  • the nodes (the labels) or 节点（标签）或
  • the lines of clusters or 簇或线
  • the background of each cluster. 每个集群的背景。

The important thing is that I can distinguish clusters based on the colors as I can do in the first plot (with round nodes). 重要的是，我可以根据颜色区分聚类，就像我在第一个图中所做的那样（使用圆形节点）。

How can I do? 我能怎么做？ I searched the internet but I couldn't solve. 我搜索了互联网，但我无法解决。 Thank you 谢谢

I tried to follow both of these two roads: 我试图遵循这两条道路：

  1. following the jlhoward response in this post 关注这篇文章中的jlhoward响应
  2. use dendextend package 使用dendextend包

I had problems from the beginning for both. 我从一开始就遇到了两个问题。

(1) In the response, df is a data frame, while I have a list ( net ), and I don't know how to correctly convert my network in a data frame like the one in the example. （1）在响应中， df是一个数据帧，而我有一个列表（ net ），我不知道如何在数据框中正确转换我的网络，如示例中的那个。 Anyway if I try (just to play) to do rownames(df) <- V(net)$label I get 无论如何，如果我尝试（只是为了玩）做rownames(df) <- V(net)$label我得到

Error in row.names<-.data.frame ( *tmp* , value = value) : length 'row.names' unacceptable row.names<-.data.frame错误row.names<-.data.frame （ *tmp* ，value = value）：length'row.names'不可接受

Quite right. 非常正确。 But the plotted result is this: 但绘制的结果如下： 在此输入图像描述

It don't use the label of the net network nodes but those of df network. 它不使用的标签net网络节点，但那些df网络。 It seems to me very strange. 在我看来非常奇怪。

(2) This is what I've done: （2）这就是我所做的： 

library(igraph)
library(GGally)
library(ggplot2)
library(ggdendro)
library(dendextend)
library(dendextendRcpp)
library(zoo)

# load dataset
net <- read.graph("./dataset/lesmiserables.gml", format = c("gml"))
deg <- igraph::degree(net, mode = "all")

comm <- fastgreedy.community(net)

comm_sizesComm <- sizes(comm) 
comm_numComm <- length(comm_sizesComm)
comm_modularity <- modularity(comm)

# plot communities
colorsRainbow <- rainbow(max(membership(comm)), alpha = 0.6)
pdf(file = "./output/prova_comm2.pdf")
plot(net,
     vertex.size = plotrix::rescale(deg, c(5, 16)),
     vertex.color = colorsRainbow[membership(comm)],
     vertex.frame.color = NA,
     vertex.label.color = "black",
     vertex.label.cex = 0.5,
     vertex.size = 10,
     edge.color = "#d8d8d8",
     layout = layout.fruchterman.reingold,
     edge.curved = FALSE)
dev.off()

# create dendrogram
dend <- as.dendrogram(comm)

cut <- comm_numComm

# change labels
dend <- dend %>% set("labels", V(net)$label) # change label
dend <- dend %>% set("labels_col", "black") # change color 
dend <- dend %>% set("labels_cex", .5) # change size

# color label based on communities
colorsRainbow <- rainbow(max(membership(comm)), alpha = 1)
dend <- dend %>% set("labels_col", value = colorsRainbow, k = cut) 

# plot dendrogram
pdf(file = "./output/prova_dend2.pdf")
plot(dend, 
     horiz = TRUE,
     main = "Dendrogram")
dev.off()

I succed to change the label and color them. 我成功地改变了标签并为它们着色。 But the colors don't match between the two graphs. 但两个图之间的颜色不匹配。 I don't understand if it's a problem of plot or the dendogram construction. 我不明白是否是情节或树状图构造的问题。

Explain: In the first plot (the one with the nodes represented by dots) I have that in blue community there are nodes {Perpetual, Fantine, Anzelma , Simplice, ...}. 解释：在第一个图（带有由点表示的节点的图）中，我在蓝色社区中有节点{Perpetual，Fantine， Anzelma ，Simplice，...}。 In the yellow community there are { Woman2 , Marius, Magnon, Cosette, ...}. 在黄色社区有{ Woman2 ，Marius，Magnon，珂赛特，......}。

If now I go to see the second plot (the dendrogram), I see that Woman2 and Anzelma nodes are in two different communities. 如果现在我去看第二个图（树形图），我看到Woman2和Anzelma节点在两个不同的社区。 It seems a huge problem and I don't know how to begin to solve. 这似乎是个大问题，我不知道如何开始解决。

(3) I tried also: （3）我也尝试过： 

ggd1 <- as.ggdend(dend)
ggplot(ggd1)

And the problem is the same of (2): communities do not match. 问题与（2）相同：社区不匹配。

I create this simple graph: 我创建了这个简单的图： 

> dput(net)
structure(list(16, FALSE, c(1, 2, 3, 2, 3, 3, 4, 5, 5, 7, 7, 
9, 8, 10, 9, 11, 11, 14, 15, 15, 15, 12, 14), c(0, 0, 0, 1, 2, 
1, 3, 3, 4, 3, 6, 6, 7, 8, 8, 8, 10, 11, 14, 13, 12, 3, 1), c(0, 
1, 3, 2, 5, 4, 6, 7, 8, 9, 10, 12, 11, 14, 13, 15, 16, 21, 22, 
17, 20, 19, 18), c(0, 1, 2, 3, 5, 22, 4, 6, 7, 9, 21, 8, 10, 
11, 12, 14, 13, 15, 16, 17, 20, 19, 18), c(0, 0, 1, 3, 6, 7, 
9, 9, 11, 12, 14, 15, 17, 18, 18, 20, 23), c(0, 3, 6, 7, 11, 
12, 12, 14, 15, 18, 18, 19, 20, 21, 22, 23, 23), list(c(1, 0, 
1), structure(list(), .Names = character(0)), structure(list(
    id = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 
    15), label = c("A", "B", "C", "D", "E", "F", "G", "H", "I", 
    "L", "M", "N", "O", "P", "Q", "R")), .Names = c("id", "label"
)), list()), <environment>), class = "igraph")

在此输入图像描述

and then I find communities and dendrogram: 然后我找到社区和树状图： 

# calculate degree
deg <- igraph::degree(net, mode = "all")

# find communities
com <- cluster_edge_betweenness(net) 
## or use 
# com <- fastgreedy.community(net)

# details on communities
com_sizesCom <- sizes(com) 
com_numCom <- length(com_sizesCom)

# create dendrogram
dend <- as.dendrogram(com)
labels(dend) <- V(net)$label[order.dendrogram(dend)]

k <- max(membership(com))
colorsRainbow <- rainbow(k, alpha = 1)

set.seed(23420)
colorsRainbow <- rainbow(max(membership(com)))
colorsRainbow <- sample(colorsRainbow)
dend <- dend %>% set("labels_col", value = colorsRainbow, k = k)
dend <- dend %>% set("branches_k_color", value = colorsRainbow, k = k)
dend <- dend %>% set("labels_cex", 1)

I get: 我明白了： 在此输入图像描述 在此输入图像描述

So, colors are not correct. 所以，颜色不正确。

Entire updated code. 整个更新的代码。 Now blue and green are switched. 现在蓝色和绿色都被切换了。 

 library(igraph)
 library(dendextend)
 library(colorspace)

 net <- upgrade_graph(net)
 # dput(net)

 # calculate degree
 deg <- igraph::degree(net, mode = "all")

 # plot network
 x11()
 plot(net,
      vertex.size = plotrix::rescale(deg, c(8, 22)),
      vertex.color = "tomato",
      vertex.frame.color = NA,
      vertex.label.color = "black",
      vertex.size = 10,
      edge.color = "#d8d8d8",
      layout = layout.fruchterman.reingold,
      edge.curved = FALSE)

 # find communities
 com <- cluster_edge_betweenness(net) 

 # details on communities
 com_sizesCom <- sizes(com) 
 com_numCom <- length(com_sizesCom)
 print(com_numCom)

 # plot communities
 colorsRainbow <- rainbow(max(membership(com)), alpha = 0.6)
 x11()
 par(mar = c(1, 1, 1, 1))
 plot(net,
      vertex.size = plotrix::rescale(deg, c(8, 22)),
      vertex.color = colorsRainbow[membership(com)],
      vertex.frame.color = NA,
      edge.color = "#d8d8d8",
      vertex.label.color = "black",
      layout = layout.fruchterman.reingold,
      edge.curved = FALSE)

 # create dendrogram
 dend <- as.dendrogram(com)
 labels(dend) <- V(net)$label[order.dendrogram(dend)]

 k <- max(membership(com))
 colorsRainbow <- rainbow(k, alpha = 1)

 colorsRainbow <- rev(unique(colorsRainbow[membership(com)[order.dendrogram(dend)]]))
 dend <- dend %>% set("labels_col", value = colorsRainbow, k = k)
 dend <- dend %>% set("branches_k_color", value = colorsRainbow, k = k)
 dend <- dend %>% set("labels_cex", 1)

 # plot dendrogram
 x11()
 par(mar = c(3, 1, 1, 5))
 plot(dend, horiz = T)

1 个解决方案

解决方案1
 0  2016-04-17 13:45:43

I am not sure I got the labels order correctly (as I am not sure how they are extracted from the network), but here is a try of a reproducible example: 我不确定我是否正确地获得了标签顺序（因为我不确定它们是如何从网络中提取出来的），但这里是一个可重现的例子： 

library(igraph)

net <- structure(list(77, FALSE, c(1, 2, 3, 3, 4, 5, 6, 7, 8, 9, 11, 
11, 11, 11, 12, 13, 14, 15, 17, 18, 18, 19, 19, 19, 20, 20, 20, 
20, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 
23, 23, 23, 23, 23, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 
27, 27, 27, 28, 28, 29, 29, 29, 30, 31, 31, 31, 31, 32, 33, 33, 
34, 34, 35, 35, 35, 36, 36, 36, 36, 37, 37, 37, 37, 37, 38, 38, 
38, 38, 38, 38, 39, 40, 41, 41, 42, 42, 42, 43, 43, 43, 44, 44, 
45, 47, 48, 48, 48, 48, 49, 49, 50, 50, 51, 51, 51, 52, 52, 53, 
54, 54, 54, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 56, 56, 57, 
57, 57, 58, 58, 58, 58, 58, 59, 59, 59, 59, 60, 60, 60, 61, 61, 
61, 61, 61, 61, 62, 62, 62, 62, 62, 62, 62, 62, 63, 63, 63, 63, 
63, 63, 63, 63, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 65, 65, 
65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66, 66, 66, 
66, 67, 68, 68, 68, 68, 68, 68, 69, 69, 69, 69, 69, 69, 69, 70, 
70, 70, 70, 70, 70, 70, 70, 71, 71, 71, 71, 71, 71, 71, 71, 72, 
72, 72, 73, 74, 74, 75, 75, 75, 75, 75, 75, 75, 76, 76, 76, 76, 
76, 76, 76), c(0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 10, 3, 2, 0, 11, 
11, 11, 11, 16, 16, 17, 16, 17, 18, 16, 17, 18, 19, 16, 17, 18, 
19, 20, 16, 17, 18, 19, 20, 21, 16, 17, 18, 19, 20, 21, 22, 12, 
11, 23, 11, 24, 23, 11, 24, 11, 16, 25, 11, 23, 25, 24, 26, 11, 
27, 23, 27, 11, 23, 30, 11, 23, 27, 11, 11, 27, 11, 29, 11, 34, 
29, 34, 35, 11, 29, 34, 35, 36, 11, 29, 34, 35, 36, 37, 11, 29, 
25, 25, 24, 25, 41, 25, 24, 11, 26, 27, 28, 11, 28, 46, 47, 25, 
27, 11, 26, 11, 49, 24, 49, 26, 11, 51, 39, 51, 51, 49, 26, 51, 
49, 39, 54, 26, 11, 16, 25, 41, 48, 49, 55, 55, 41, 48, 55, 48, 
27, 57, 11, 58, 55, 48, 57, 48, 58, 59, 48, 58, 60, 59, 57, 55, 
55, 58, 59, 48, 57, 41, 61, 60, 59, 48, 62, 57, 58, 61, 60, 55, 
55, 62, 48, 63, 58, 61, 60, 59, 57, 11, 63, 64, 48, 62, 58, 61, 
60, 59, 57, 55, 64, 58, 59, 62, 65, 48, 63, 61, 60, 57, 25, 11, 
24, 27, 48, 41, 25, 68, 11, 24, 27, 48, 41, 25, 69, 68, 11, 24, 
27, 41, 58, 27, 69, 68, 70, 11, 48, 41, 25, 26, 27, 11, 48, 48, 
73, 69, 68, 25, 48, 41, 70, 71, 64, 65, 66, 63, 62, 48, 58), 
    c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 12, 11, 10, 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, 47, 46, 39, 40, 41, 42, 43, 44, 
    45, 49, 48, 52, 51, 50, 54, 55, 53, 56, 57, 58, 60, 59, 61, 
    62, 63, 66, 64, 65, 67, 69, 70, 71, 68, 72, 73, 74, 75, 76, 
    77, 79, 78, 82, 83, 80, 81, 87, 88, 84, 85, 86, 93, 94, 89, 
    90, 91, 92, 95, 96, 97, 98, 101, 100, 99, 102, 103, 104, 
    106, 105, 107, 108, 112, 110, 111, 109, 114, 113, 116, 115, 
    119, 118, 117, 121, 120, 122, 125, 124, 123, 131, 132, 133, 
    130, 128, 134, 135, 127, 126, 129, 136, 137, 139, 140, 138, 
    145, 143, 142, 141, 144, 148, 147, 149, 146, 150, 151, 152, 
    153, 158, 157, 154, 156, 155, 164, 162, 159, 163, 160, 161, 
    166, 165, 168, 174, 170, 171, 167, 173, 172, 169, 184, 177, 
    175, 183, 179, 182, 181, 180, 176, 178, 187, 194, 193, 189, 
    192, 191, 190, 188, 185, 186, 200, 196, 197, 203, 202, 198, 
    201, 195, 199, 204, 206, 207, 205, 208, 210, 209, 213, 214, 
    211, 215, 217, 216, 212, 221, 222, 218, 223, 224, 225, 220, 
    219, 230, 233, 226, 232, 231, 228, 227, 229, 236, 234, 235, 
    237, 238, 239, 242, 244, 243, 241, 240, 245, 246, 252, 253, 
    251, 250, 247, 248, 249), c(0, 1, 2, 4, 5, 6, 7, 8, 9, 13, 
    3, 12, 11, 10, 14, 15, 16, 17, 47, 49, 52, 54, 57, 62, 66, 
    69, 72, 73, 75, 77, 82, 87, 93, 102, 106, 112, 114, 119, 
    131, 145, 184, 206, 213, 221, 230, 236, 46, 18, 19, 21, 24, 
    28, 33, 39, 55, 132, 20, 22, 25, 29, 34, 40, 23, 26, 30, 
    35, 41, 27, 31, 36, 42, 32, 37, 43, 38, 44, 45, 48, 51, 58, 
    64, 67, 70, 50, 53, 60, 97, 101, 116, 207, 214, 222, 56, 
    59, 95, 96, 98, 100, 110, 133, 205, 211, 218, 233, 242, 61, 
    103, 113, 118, 125, 130, 234, 63, 65, 71, 74, 104, 111, 143, 
    208, 215, 223, 226, 235, 105, 107, 76, 79, 83, 88, 94, 68, 
    78, 80, 84, 89, 81, 85, 90, 86, 91, 92, 121, 128, 99, 134, 
    139, 164, 210, 217, 224, 232, 244, 108, 109, 135, 140, 142, 
    148, 150, 153, 162, 168, 177, 187, 200, 209, 216, 231, 237, 
    238, 243, 252, 115, 117, 124, 127, 136, 120, 122, 123, 126, 
    129, 137, 138, 141, 147, 158, 159, 174, 175, 194, 144, 149, 
    157, 163, 170, 183, 193, 204, 146, 151, 154, 160, 171, 179, 
    189, 196, 225, 253, 152, 156, 161, 167, 182, 192, 197, 155, 
    166, 173, 181, 191, 203, 165, 172, 180, 190, 202, 169, 176, 
    188, 198, 251, 178, 185, 201, 250, 186, 195, 247, 199, 248, 
    249, 212, 220, 228, 241, 219, 227, 240, 229, 245, 246, 239
    ), c(0, 0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 10, 14, 15, 16, 17, 
    18, 18, 19, 21, 24, 28, 33, 39, 48, 50, 53, 57, 62, 64, 67, 
    68, 72, 73, 75, 77, 80, 84, 89, 95, 96, 97, 99, 102, 105, 
    107, 108, 108, 109, 113, 115, 117, 120, 122, 123, 126, 136, 
    138, 141, 146, 150, 153, 159, 167, 175, 185, 195, 204, 205, 
    211, 218, 226, 234, 237, 238, 240, 247, 254), c(0, 10, 10, 
    12, 13, 13, 13, 13, 13, 13, 13, 14, 46, 47, 47, 47, 47, 56, 
    62, 67, 71, 74, 76, 77, 83, 92, 105, 112, 124, 126, 131, 
    132, 132, 132, 132, 136, 139, 141, 142, 142, 144, 144, 153, 
    153, 153, 153, 153, 154, 155, 173, 178, 178, 182, 182, 182, 
    183, 192, 192, 200, 210, 217, 223, 228, 233, 237, 240, 242, 
    243, 243, 247, 250, 252, 253, 253, 254, 254, 254, 254), list(
        c(1, 0, 1), structure(list(), .Names = character(0)), 
        structure(list(id = c(0, 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), label = c("Myriel", 
        "Napoleon", "MlleBaptistine", "MmeMagloire", "CountessDeLo", 
        "Geborand", "Champtercier", "Cravatte", "Count", "OldMan", 
        "Labarre", "Valjean", "Marguerite", "MmeDeR", "Isabeau", 
        "Gervais", "Tholomyes", "Listolier", "Fameuil", "Blacheville", 
        "Favourite", "Dahlia", "Zephine", "Fantine", "MmeThenardier", 
        "Thenardier", "Cosette", "Javert", "Fauchelevent", "Bamatabois", 
        "Perpetue", "Simplice", "Scaufflaire", "Woman1", "Judge", 
        "Champmathieu", "Brevet", "Chenildieu", "Cochepaille", 
        "Pontmercy", "Boulatruelle", "Eponine", "Anzelma", "Woman2", 
        "MotherInnocent", "Gribier", "Jondrette", "MmeBurgon", 
        "Gavroche", "Gillenormand", "Magnon", "MlleGillenormand", 
        "MmePontmercy", "MlleVaubois", "LtGillenormand", "Marius", 
        "BaronessT", "Mabeuf", "Enjolras", "Combeferre", "Prouvaire", 
        "Feuilly", "Courfeyrac", "Bahorel", "Bossuet", "Joly", 
        "Grantaire", "MotherPlutarch", "Gueulemer", "Babet", 
        "Claquesous", "Montparnasse", "Toussaint", "Child1", 
        "Child2", "Brujon", "MmeHucheloup"), maincharacter = c(0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0)), .Names = c("id", "label", "maincharacter"
        )), structure(list(value = c(1, 8, 10, 6, 1, 1, 1, 1, 
        2, 1, 1, 3, 3, 5, 1, 1, 1, 1, 4, 4, 4, 4, 4, 4, 3, 3, 
        3, 4, 3, 3, 3, 3, 5, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 4, 
        4, 4, 2, 9, 2, 7, 13, 1, 12, 4, 31, 1, 1, 17, 5, 5, 1, 
        1, 8, 1, 1, 1, 2, 1, 2, 3, 2, 1, 1, 2, 1, 3, 2, 3, 3, 
        2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 1, 
        2, 3, 2, 2, 1, 3, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 3, 2, 
        1, 1, 9, 2, 2, 1, 1, 1, 2, 1, 1, 6, 12, 1, 1, 21, 19, 
        1, 2, 5, 4, 1, 1, 1, 1, 1, 7, 7, 6, 1, 4, 15, 5, 6, 2, 
        1, 4, 2, 2, 6, 2, 5, 1, 1, 9, 17, 13, 7, 2, 1, 6, 3, 
        5, 5, 6, 2, 4, 3, 2, 1, 5, 12, 5, 4, 10, 6, 2, 9, 1, 
        1, 5, 7, 3, 5, 5, 5, 2, 5, 1, 2, 3, 3, 1, 2, 2, 1, 1, 
        1, 1, 3, 5, 1, 1, 1, 1, 1, 6, 6, 1, 1, 2, 1, 1, 4, 4, 
        4, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 
        2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)), .Names = "value")), 
    "<environment>"), class = "igraph")

girvNew <- cluster_edge_betweenness(upgrade_graph(net))
dend <- as.dendrogram(girvNew )

library(dendextend)
# hopefully this is correct, but I'm not sure. It assumes that
# V(net)$label has the order of the original data
labels(dend) <- V(net)$label[order.dendrogram(dend)]

plot(dend)

library(colorspace)
k <- max(membership(girvNew))
colorsRainbow <- rainbow_hcl(k, alpha = 1) # [membership(girvNew)]

set.seed(23420)
colorsRainbow <- rainbow_hcl(max(membership(girvNew)))
colorsRainbow <- sample(colorsRainbow)
dend <- dend %>% set("labels_col", value = colorsRainbow, k = k) 
dend <- dend %>% set("branches_k_color", value = colorsRainbow, k = k) 

par(mar = c(3,1,1,5) )
plot(dend, horiz = T)

Output: 输出：

在此输入图像描述

And now with ggplot2: 现在用ggplot2： 

library(ggplot2)
ggplot(dend) # the same as: ggplot(as.ggdend(dend))
# more work is needed for fixing the margins etc.

在此输入图像描述

Updated code: 更新的代码： 

# dput(net)
net <- structure(list(16, FALSE, c(1, 2, 3, 2, 3, 3, 4, 5, 5, 7, 7, 
9, 8, 10, 9, 11, 11, 14, 15, 15, 15, 12, 14), c(0, 0, 0, 1, 2, 
1, 3, 3, 4, 3, 6, 6, 7, 8, 8, 8, 10, 11, 14, 13, 12, 3, 1), c(0, 
1, 3, 2, 5, 4, 6, 7, 8, 9, 10, 12, 11, 14, 13, 15, 16, 21, 22, 
17, 20, 19, 18), c(0, 1, 2, 3, 5, 22, 4, 6, 7, 9, 21, 8, 10, 
11, 12, 14, 13, 15, 16, 17, 20, 19, 18), c(0, 0, 1, 3, 6, 7, 
9, 9, 11, 12, 14, 15, 17, 18, 18, 20, 23), c(0, 3, 6, 7, 11, 
12, 12, 14, 15, 18, 18, 19, 20, 21, 22, 23, 23), list(c(1, 0, 
1), structure(list(), .Names = character(0)), structure(list(
    id = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 
    15), label = c("A", "B", "C", "D", "E", "F", "G", "H", "I", 
    "L", "M", "N", "O", "P", "Q", "R")), .Names = c("id", "label"
)), list()), "<environment>"), class = "igraph")


library(igraph)
net <- upgrade_graph(net)
# calculate degree
deg <- degree(net, mode = "all")

# find communities
com <- cluster_edge_betweenness(net) 
## or use 
# com <- fastgreedy.community(net)

# details on communities
com_sizesCom <- sizes(com) 
com_numCom <- length(com_sizesCom)


# create dendrogram
dend <- as.dendrogram(com)
plot(dend)
library(dendextend)
labels(dend) <- V(net)$label[order.dendrogram(dend)]
plot(dend)

k <- max(membership(com))
colorsRainbow <- rainbow(k, alpha = 1)

# colorsRainbow <- sample(colorsRainbow)
# dend <- rotate(dend, order(membership(comm)))
colorsRainbow <- rev(unique(colorsRainbow[membership(comm)[order.dendrogram(dend)]]))
dend <- dend %>% set("labels_col", value = colorsRainbow, k = k)
dend <- dend %>% set("branches_k_col", value = colorsRainbow, k = k)
dend <- dend %>% set("labels_cex", 1)
plot(dend)

在此输入图像描述

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相关问题 将树状图中的标签聚类 - Clusters labels in dendrogram Dendextend：关于如何根据定义的组为树状图标签着色 - Dendextend: Regarding how to color a dendrogram’s labels according to defined groups 如何根据定义的组为树形图的标签着色？ （在R中） - How to color a dendrogram's labels according to defined groups? (in R) 旋转 ggplot 树状图的标签 - Rotate labels for ggplot dendrogram R中树状图中的标签 - Labels in dendrogram in R R中带有标签的水平树状图 - Horizontal Dendrogram with Labels in R 右侧带有标签的树状图 - Dendrogram with labels on the right side R 中带有标签的水平树状图 - horizontal dendrogram in R with labels 无根树状图中的抖动标签 - Jitter Labels in unrooted dendrogram 基于外部标签的颜色树状图分支向上到根，直到标签匹配 - Color dendrogram branches based on external labels uptowards the root until the label matches
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