网络图简介

网络图表示着一群事物之间的关系，如果两个事物有关系，则对应结点之间有边。

实战

官方实例

所需的包

library(tidyverse)
library(viridis)
library(patchwork)
library(hrbrthemes)
# install.packages("ggraph")
library(ggraph)
library(igraph)
# install.packages("networkD3")
library(networkD3)

数据含义

这个数据表示如果两个人至少在同一出版物上列出过一次，则通过链接将他们联系起来。

数据读取

# Load researcher data
dataUU <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/13_AdjacencyUndirectedUnweighted.csv", header=TRUE)

这个数据的特点就是：

• 第一列是结点的名字；
• 如果两个结点右边，则对应矩阵里面的值为1，反之为NA。

数据处理

这个数据处理都是通用的，只要将我们的数据转换成dataUU的格式即可。

# Transform the adjacency matrix in a long format
connect <- dataUU %>% gather(key="to", value="value", -1) %>%na.omit()# Number of connection per person
c( as.character(connect$from), as.character(connect$to)) %>%as.tibble() %>%group_by(value) %>%summarize(n=n()) -> coauth
colnames(coauth) <- c("name", "n")# NetworkD3 format
graph=simpleNetwork(connect)

画图

# Plot
simpleNetwork(connect,     Source = 1,                 # column number of sourceTarget = 2,                 # column number of targetheight = 880,               # height of frame area in pixelswidth = 1980,linkDistance = 10,         # distance between node. Increase this value to have more space between nodescharge = -4,              # numeric value indicating either the strength of the node repulsion (negative value) or attraction (positive value)fontSize = 5,              # size of the node namesfontFamily = "serif",       # font og node nameslinkColour = "#666",        # colour of edges, MUST be a common colour for the whole graphnodeColour = "#69b3a2",     # colour of nodes, MUST be a common colour for the whole graphopacity = 0.9,              # opacity of nodes. 0=transparent. 1=no transparencyzoom = T                    # Can you zoom on the figure?
)

自己的例子

数据

名字	张三	李四	王五	赵六	冯七	蒋八
张三	1		1	1		1
李四		1		1
王五	1		1		1	1
赵六	1	1		1	1
冯七			1	1	1
蒋八	1		1			1
数据读取和处理都和前面一样。

# Libraries
library(tidyverse)
library(viridis)
library(patchwork)
library(hrbrthemes)
# install.packages("ggraph")
library(ggraph)
library(igraph)
# install.packages("networkD3")
library(networkD3)
# dataUU <- read.csv("D:/每日一图/关系网 - 副本.CSV")
dataUU <- read.csv("D:/每日一图/关系网.CSV")# Transform the adjacency matrix in a long format
connect <- dataUU %>% gather(key="to", value="value", -1) %>%na.omit()# Number of connection per person
c( as.character(connect$from), as.character(connect$to)) %>%as.tibble() %>%group_by(value) %>%summarize(n=n()) -> coauth
colnames(coauth) <- c("name", "n")# NetworkD3 format
graph=simpleNetwork(connect)

画图

画图的代码稍微改了一下linkDistance和fontSize参数，分别把节点间距离变长，字体改一下

# Plot
simpleNetwork(connect,     Source = 1,                 # column number of sourceTarget = 2,                 # column number of target# height = 80,               # height of frame area in pixels# width = 180,linkDistance = 38,         # distance between node. Increase this value to have more space between nodescharge = -4,              # numeric value indicating either the strength of the node repulsion (negative value) or attraction (positive value)fontSize = 5,              # size of the node namesfontFamily = "serif",       # font og node nameslinkColour = "#666",        # colour of edges, MUST be a common colour for the whole graphnodeColour = "#69b3a2",     # colour of nodes, MUST be a common colour for the whole graphopacity = 0.9,              # opacity of nodes. 0=transparent. 1=no transparencyzoom = T                    # Can you zoom on the figure?
)

最后得到下图

后记

网络在生信里面占据了很大作用，之后构建的网络可以通过这个方法可视化。

平常的数据，第一列一般不是结点名称，结点名称一般在行名上，这个时候我们可以用下面的代码进行处理一下，就变成我们想要的形式了

dataUU <- data.frame(rownames(dataUU),dataUU)