平时对于网络图的绘制，一般我们都会在R中生成边和点列表后导入到Cytoscape和Gephi等的本地工具软件当中，而R语言中也自带有不少优秀的包也可精美的可视化我们的数据，所有函数也比较简单，有时间的不妨学习一下~~
加载R包
## 安装加载数据
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/navdata")
install.packages(
c("tidyverse", "igraph", "tidygraph", "ggraph")
)
library(pacman)
p_load("navdata","tidyverse", "igraph", "tidygraph", "ggraph")
- navdata:需要用到此包中的示例数据集phone.call
- tidyverse：数据进行操作转换
- igraph，tidygraph和ggraph：用于网络可视化
查看示例数据
文件包含三列数据：来源地、目的地、及call数量
library("navdata")
data("phone.call")
head(phone.call, 3)
## # A tibble: 3 x 3
## source destination n.call
##
## 1 France Germany 9
## 2 Belgium France 4
## 3 France Spain 3
我们都知道可视化网络，需要准备两个数据文件：
- 点列表：包含点标签和其它的点属性
- 边列表：包含点与点之间的关系，及其它属性
接下来基于此数据集准备点和边列表文件
点列表
- 去重source 和 destination列，然后将向量合并一列命名为label，添加索引值
# 来源地去重
sources <- phone.call %>%
distinct(source) %>%
rename(label = source)
#目的地去重
destinations <- phone.call %>%
distinct(destination) %>%
rename(label = destination)
## 合并数据并添加一列索引
nodes <- full_join(sources, destinations, by = "label")
nodes <- nodes %>%
mutate(id = 1:nrow(nodes)) %>%
select(id, everything())
head(nodes, 3)
## # A tibble: 3 x 2
## id label
##
## 1 1 France
## 2 2 Belgium
## 3 3 Germany
创建边列表
- 其实原始数据已具备边列表的格式，重命名 n.call为weight，然后通过left_join函数将点列表中id数据和边列表中进行合并
# Rename the n.call column to weight
phone.call <- phone.call %>%
rename(weight = n.call)
# (a) Join nodes id for source column
edges <- phone.call %>%
left_join(nodes, by = c("source" = "label")) %>%
rename(from = id)
# (b) Join nodes id for destination column
edges <- edges %>%
left_join(nodes, by = c("destination" = "label")) %>%
rename(to = id)
# (c) Select/keep only the columns from and to
edges <- select(edges, from, to, weight)
head(edges, 3)
## # A tibble: 3 x 3
## from to weight
##
## 1 1 3 9
## 2 2 1 4
## 3 1 8 3
可视化网络
至此，简单的边和点列表已经准备好了，我们接下来通过几个R包来可视化下
igraph
这是R中绘制网络图的一个基本R包，这里主要用到graph_from_data_fram()函数。更多关于此包绘图的细节可参考这个帖子Network Analysis and Visualization with R and igraph (kateto.net)，介绍的十分详细。
library(igraph)
net.igraph <- graph_from_data_frame(
d = edges, vertices = nodes,
directed = TRUE
)
## d 边列表，vertices 点列表，directer：有向或者无向
## network
set.seed(123)
plot(net.igraph, edge.arrow.size = 0.2,
layout = layout_with_graphopt)
image-20220308151413493
tidygraph和ggraph
这两个R包目前相对比较流行，可以对network数据进行操作和可视化
- 首先用tidygraph创建networ对象
library(tidygraph)
net.tidy <- tbl_graph(
nodes = nodes, edges = edges, directed = TRUE
)
- 使用ggraph可视化网络
library(ggraph)
ggraph(net.tidy, layout = "graphopt") +
geom_node_point(col = 'gold',size = 2) + # 点信息
geom_edge_link(aes(width = weight), alpha = 0.8) + # 边信息
scale_edge_width(range = c(0.2, 2)) + # 控制粗细
geom_node_text(aes(label = label), repel = TRUE) + # 增加节点的标签，reple避免节点重叠
labs(edge_width = "phone.call") + # 图例标签
theme_graph()
image-20220308152100483
更多图形样式
- 弧形图
# Arc diagram
ggraph(net.tidy, layout = "linear") +
geom_edge_arc(aes(width = weight), alpha = 0.8) +
scale_edge_width(range = c(0.2, 2)) +
geom_node_text(aes(label = label), repel = TRUE) +
labs(edge_width = "Number of calls") +
theme_graph()+
theme(legend.position = "top")
linear
- 弦图
# Coord diagram, circular
ggraph(net.tidy, layout = "linear", circular = TRUE) +
geom_edge_arc(aes(width = weight), alpha = 0.8) +
scale_edge_width(range = c(0.2, 2)) +
geom_node_text(aes(label = label), repel = TRUE) +
labs(edge_width = "Number of calls") +
theme_graph()+
theme(legend.position = "top")
image
- treemap、dendrogram等等
Treemap
dendrogram
ggraph包还有更多好玩的样式，详情参考：https://www.data-imaginist.com/2017/ggraph-introduction-layouts/, 根据个人的数据去选择最合适的展现形式即可。
visNetwor和network3D
这两款R包都是基于浏览器的JavaScript可视化库，用于交互式的展示图形，这里我们还用到刚才的示例数据来展示。
加载数据
library(tidyverse)
library("navdata")
data("phone.call2")
nodes <- phone.call2$nodes
edges <- phone.call2$edges
## 刚才数据集是phone.call，phone.call2可以直接提取点和边列表
##注意：该包中节点和边缘列表中的ID应该是从0开始的数字值。我们直接减1 即可
nodes_d3 <- mutate(nodes, id = id - 1)
edges_d3 <- mutate(edges, from = from - 1, to = to - 1)
network3D
常用来快速创建交互式桑基图、和其它种类的网络图等，核心的函数即forceNetwork()
library(networkD3)
forceNetwork(
Links = edges_d3, Nodes = nodes_d3,bounded = T,
Source = "from", Target = "to", # so the network is directed.
NodeID = "label", Group = "id", Value = "weight",
width = 700, height=500,legend = T,
colourScale = JS("d3.scaleOrdinal(d3.schemeCategory10);"), ## 颜色模板
opacity = 1, fontSize = 18, zoom = TRUE, opacityNoHover = 1 ## 显示标签信息
)
## 如果想自定义颜色新修改，直接括号中改为16进制颜色即可
colourScale = JS("d3.scaleOrdinal([`#383867`, `#584c77`, `#e1eff7`,`#dd2c29`]);"),
image-20220308160944163
制作桑基图
sankeyNetwork(
Links = edges_d3, Nodes = nodes_d3,
Source = "from", Target = "to",
NodeID = "label", Value = "weight",
fontSize = 16, unit = "Letter(s)")
image-20220308161022262
visNetwork
该包绘制更加方便，
library(visNetwork)
visNetwork(nodes, edges) %>%
visLayout(randomSeed = 12)
image-20220308161618459
我们也可以为网络中的边添加方向,用到 layout_with_fr 方式
visNetwork(nodes, edges) %>%
visIgraphLayout(layout = "layout_with_fr") %>%
visEdges(arrows = "middle") %>%
visLayout(randomSeed = 1234)
image-20220308161805871
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