40. 网络图绘制
清除当前环境中的变量
rm(list=ls())
设置工作目录
setwd("C:/Users/Dell/Desktop/R_Plots/40network/")
使用igraph包绘制网络图
# 安装并加载所需的R包
#install.packages("igraph")
library(igraph)
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
# 使用邻接矩阵数据绘制网络图
# 构建示例数据
set.seed(10)
data <- matrix(sample(0:2, 25, replace=TRUE), nrow=5)
colnames(data) = rownames(data) = LETTERS[1:5]
# 查看示例数据
head(data)
## A B C D E
## A 2 2 1 2 2
## B 0 2 2 1 1
## C 1 2 1 2 1
## D 2 2 1 1 0
## E 1 2 0 2 0
# build the graph object
# 使用graph_from_adjacency_matrix()函数构建网络图对象
network <- graph_from_adjacency_matrix(data)
# 查看对象
network
## IGRAPH 8f56656 DN-- 5 33 --
## + attr: name (v/c)
## + edges from 8f56656 (vertex names):
## [1] A->A A->A A->B A->B A->C A->D A->D A->E A->E B->B B->B B->C B->C B->D
## [15] B->E C->A C->B C->B C->C C->D C->D C->E D->A D->A D->B D->B D->C D->D
## [29] E->A E->B E->B E->D E->D
# plot it
# 绘制基础网络图，默认得到无权重有方向的网路图
plot(network)
image.png
# 使用关联矩阵数据绘制网络图
# 构建示例数据
set.seed(1)
data <- matrix(sample(0:2, 15, replace=TRUE), nrow=3)
colnames(data) <- letters[1:5]
rownames(data) <- LETTERS[1:3]
# 查看示例数据
head(data)
## a b c d e
## A 0 1 2 2 0
## B 2 0 1 2 0
## C 0 2 1 0 1
# create the network object
# 使用graph_from_incidence_matrix()函数构建网络图对像
network <- graph_from_incidence_matrix(data,directed = T)
network
## IGRAPH 8f5ca19 DN-B 8 18 --
## + attr: type (v/l), name (v/c)
## + edges from 8f5ca19 (vertex names):
## [1] A->b b->A A->c c->A A->d d->A B->a a->B B->c c->B B->d d->B C->b b->C
## [15] C->c c->C C->e e->C
# plot it
plot(network)
image.png
# 使用边列表数据绘制网络图
# 构建示例数据
links <- data.frame(
source=c("A","A", "A", "A", "A","F", "B"),
target=c("B","B", "C", "D", "F","A","E")
)
links
## source target
## 1 A B
## 2 A B
## 3 A C
## 4 A D
## 5 A F
## 6 F A
## 7 B E
# create the network object
# 使用graph_from_data_frame()函数构建网络图对像
network <- graph_from_data_frame(d=links, directed=F)
network
## IGRAPH 8f63302 UN-- 6 7 --
## + attr: name (v/c)
## + edges from 8f63302 (vertex names):
## [1] A--B A--B A--C A--D A--F A--F B--E
# plot it
plot(network)
image.png
# 自定义一些参数美化网络图
# Create data
set.seed(1)
data <- matrix(sample(0:1, 100, replace=TRUE, prob=c(0.8,0.2)), nc=10)
head(data)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 0 0 1 0 1 0 1 0 0 0
## [2,] 0 0 0 0 0 1 0 1 0 0
## [3,] 0 0 0 0 0 0 0 0 0 0
## [4,] 1 0 0 0 0 0 0 0 0 1
## [5,] 0 0 0 1 0 0 0 0 0 0
## [6,] 1 0 0 0 0 0 0 1 0 0
# 构建网络图对象，设置mode='undirected'参数构建无方向的网络图
network <- graph_from_adjacency_matrix(data, mode='undirected', diag=T)
network
## IGRAPH 8f69bc6 U--- 10 15 --
## + edges from 8f69bc6:
## [1] 1-- 3 1-- 4 1-- 5 1-- 6 1-- 7 2-- 6 2-- 8 3-- 9 4-- 5 4--10 6-- 8
## [12] 7-- 8 7--10 8--10 9--10
# Default network
plot(network)
image.png
# 使用vertex.参数设置节点的大小，形状和颜色等
plot(network,
vertex.color = rgb(0.8,0.2,0.2,0.9), # Node color
vertex.frame.color = "Forestgreen", # Node border color
vertex.shape=c("circle","square"), # One of “none”, “circle”, “square”, “csquare”, “rectangle” “crectangle”, “vrectangle”, “pie”, “raster”, or “sphere”
vertex.size=c(15:24), # Size of the node (default is 15)
vertex.size2=NA, # The second size of the node (e.g. for a rectangle)
)
image.png
# 使用vertex.label.参数添加标签，并设置标签字体，颜色和大小等
plot(network,
vertex.label=LETTERS[1:10], # Character vector used to label the nodes
vertex.label.color=c("red","blue"),
vertex.label.family="Times", # Font family of the label (e.g.“Times”, “Helvetica”)
vertex.label.font=c(1,2,3,4), # Font: 1 plain, 2 bold, 3, italic, 4 bold italic, 5 symbol
vertex.label.cex=c(0.5,1,1.5), # Font size (multiplication factor, device-dependent)
vertex.label.dist=0, # Distance between the label and the vertex
vertex.label.degree=0 , # The position of the label in relation to the vertex (use pi)
)
image.png
# 使用edge.参数设置边的大小，颜色和箭头等
plot(network,
edge.color=rep(c("red","pink"),5), # Edge color
edge.width=seq(1,10), # Edge width, defaults to 1
edge.arrow.size=1, # Arrow size, defaults to 1
edge.arrow.width=1, # Arrow width, defaults to 1
edge.lty=c("solid") # Line type, could be 0 or “blank”, 1 or “solid”, 2 or “dashed”, 3 or “dotted”, 4 or “dotdash”, 5 or “longdash”, 6 or “twodash”
#edge.curved=c(rep(0,5), rep(1,5)) # Edge curvature, range 0-1 (FALSE sets it to 0, TRUE to 0.5)
)
image.png
# 使用layout参数设置网络图的展示类型
# Create data
data <- matrix(sample(0:1, 400, replace=TRUE, prob=c(0.8,0.2)), nrow=20)
head(data)
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 0 0 0 0 0 0 1 0 0 0 0 1
## [2,] 0 0 0 0 0 0 1 1 0 0 1 0 0
## [3,] 0 0 0 0 0 0 1 0 1 1 0 1 0
## [4,] 0 0 1 0 1 0 0 0 0 0 0 1 1
## [5,] 0 0 0 0 0 0 1 0 0 1 0 0 0
## [6,] 0 0 0 1 0 0 1 1 0 0 0 0 0
## [,14] [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 0 0 0 0 0 0 0
## [2,] 0 1 1 1 0 0 0
## [3,] 0 0 0 0 0 0 0
## [4,] 1 0 0 1 0 0 0
## [5,] 0 0 0 1 1 1 0
## [6,] 0 0 0 0 1 0 0
# 构建网络图对象
network <- graph_from_adjacency_matrix(data , mode='undirected', diag=F )
network
## IGRAPH 8f7dd1e U--- 20 69 --
## + edges from 8f7dd1e:
## [1] 1-- 8 1--13 1--14 1--19 2-- 7 2-- 8 2--11 2--14 2--15 2--16
## [11] 2--17 3-- 4 3-- 7 3-- 8 3-- 9 3--10 3--12 3--17 3--18 4-- 5
## [21] 4-- 6 4-- 9 4--10 4--12 4--13 4--14 4--16 4--17 4--18 5-- 7
## [31] 5-- 9 5--10 5--13 5--17 5--18 5--19 6-- 7 6-- 8 6--15 6--18
## [41] 7-- 9 7--11 7--12 7--18 7--19 7--20 8-- 9 8--11 8--16 8--18
## [51] 8--19 8--20 9--11 9--12 9--14 9--18 10--12 10--15 10--18 10--19
## [61] 11--14 12--14 12--18 13--14 13--17 14--16 14--20 17--19 18--19
# 绘制不同展示类型的网络图
plot(network, layout=layout.sphere, main="sphere")
image.png
plot(network, layout=layout.circle, main="circle")
image.png
plot(network, layout=layout.random, main="random")
image.png
plot(network, layout=layout.fruchterman.reingold, main="fruchterman.reingold")
image.png
# 添加节点的属性特征
# create data:
links <- data.frame(
source=c("A","A", "A", "A", "A","J", "B", "B", "C", "C", "D","I"),
target=c("B","B", "C", "D", "J","A","E", "F", "G", "H", "I","I"),
importance=(sample(1:4, 12, replace=T))
)
# 构建节点属性表
nodes <- data.frame(
name=LETTERS[1:10],
carac=c( rep("young",3),rep("adult",2), rep("old",5))
)
# 查看数据
head(links)
## source target importance
## 1 A B 4
## 2 A B 3
## 3 A C 3
## 4 A D 1
## 5 A J 3
## 6 J A 3
head(nodes)
## name carac
## 1 A young
## 2 B young
## 3 C young
## 4 D adult
## 5 E adult
## 6 F old
# Turn it into igraph object
# 构建网络图对象，vertices参数指定节点属性
network <- graph_from_data_frame(d=links, vertices=nodes, directed=F)
# 查看网络图对象
network
## IGRAPH 8f96792 UN-- 10 12 --
## + attr: name (v/c), carac (v/c), importance (e/n)
## + edges from 8f96792 (vertex names):
## [1] A--B A--B A--C A--D A--J A--J B--E B--F C--G C--H D--I I--I
# 自定义颜色
# Make a palette of 3 colors
library(RColorBrewer)
coul <- brewer.pal(3, "Set1")
# Create a vector of color
my_color <- coul[as.numeric(as.factor(V(network)$carac))]
my_color
## [1] "#4DAF4A" "#4DAF4A" "#4DAF4A" "#E41A1C" "#E41A1C" "#377EB8" "#377EB8"
## [8] "#377EB8" "#377EB8" "#377EB8"
# 绘值带节点属性的网络图
plot(network, vertex.color=my_color)
# 添加图例
legend("bottomleft", legend=levels(as.factor(V(network)$carac)),
col = coul , bty = "n", pch=20 , pt.cex = 3,
cex = 1.5, text.col=coul , horiz = FALSE,
inset = c(0.1, 0.1))
image.png
# 添加边的属性特征
plot(network, vertex.color=my_color,
edge.width=E(network)$importance*2 )
# 添加图例
legend("bottomleft", legend=levels(as.factor(V(network)$carac)),
col = coul , bty = "n", pch=20 , pt.cex = 3,
cex = 1.5, text.col=coul , horiz = FALSE,
inset = c(0.1, 0.1))
image.png
使用ggraph包绘制网络图
library(ggraph)
## Warning: package 'ggraph' was built under R version 3.6.3
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.6.3
library(igraph)
library(tidyverse)
## -- Attaching packages -------------------------------- tidyverse 1.2.1 --
## √ tibble 2.1.3 √ purrr 0.3.2
## √ tidyr 1.1.2 √ dplyr 1.0.2
## √ readr 1.3.1 √ stringr 1.4.0
## √ tibble 2.1.3 √ forcats 0.4.0
## Warning: package 'tidyr' was built under R version 3.6.3
## Warning: package 'dplyr' was built under R version 3.6.3
## -- Conflicts ----------------------------------- tidyverse_conflicts() --
## x dplyr::as_data_frame() masks tibble::as_data_frame(), igraph::as_data_frame()
## x purrr::compose() masks igraph::compose()
## x tidyr::crossing() masks igraph::crossing()
## x dplyr::filter() masks stats::filter()
## x dplyr::groups() masks igraph::groups()
## x dplyr::lag() masks stats::lag()
## x purrr::simplify() masks igraph::simplify()
# 构建示例数据
# create a data frame giving the hierarchical structure of your individuals
set.seed(1234)
d1 <- data.frame(from="origin", to=paste("group", seq(1,10), sep=""))
d2 <- data.frame(from=rep(d1$to, each=10), to=paste("subgroup", seq(1,100), sep="_"))
hierarchy <- rbind(d1, d2)
# create a dataframe with connection between leaves (individuals)
all_leaves <- paste("subgroup", seq(1,100), sep="_")
connect <- rbind(
data.frame( from=sample(all_leaves, 100, replace=T) , to=sample(all_leaves, 100, replace=T)),
data.frame( from=sample(head(all_leaves), 30, replace=T) , to=sample( tail(all_leaves), 30, replace=T)),
data.frame( from=sample(all_leaves[25:30], 30, replace=T) , to=sample( all_leaves[55:60], 30, replace=T)),
data.frame( from=sample(all_leaves[75:80], 30, replace=T) , to=sample( all_leaves[55:60], 30, replace=T)) )
connect$value <- runif(nrow(connect))
# create a vertices data.frame. One line per object of our hierarchy
vertices <- data.frame(
name = unique(c(as.character(hierarchy$from), as.character(hierarchy$to))) ,
value = runif(111)
)
# Let's add a column with the group of each name. It will be useful later to color points
vertices$group <- hierarchy$from[ match( vertices$name, hierarchy$to ) ]
# 查看示例数据
head(hierarchy)
## from to
## 1 origin group1
## 2 origin group2
## 3 origin group3
## 4 origin group4
## 5 origin group5
## 6 origin group6
head(connect)
## from to value
## 1 subgroup_28 subgroup_85 0.9553637
## 2 subgroup_80 subgroup_85 0.3994100
## 3 subgroup_22 subgroup_32 0.9770535
## 4 subgroup_9 subgroup_77 0.5117742
## 5 subgroup_5 subgroup_17 0.4671612
## 6 subgroup_38 subgroup_66 0.7238355
head(vertices)
## name value group
## 1 origin 0.04931545 <NA>
## 2 group1 0.30197021 origin
## 3 group2 0.51204261 origin
## 4 group3 0.71036062 origin
## 5 group4 0.28465309 origin
## 6 group5 0.46494880 origin
# Create a graph object
# 构建网络图对象，vertices参数指定节点属性
mygraph <- graph_from_data_frame( hierarchy, vertices=vertices )
mygraph
## IGRAPH 909f7f2 DN-- 111 110 --
## + attr: name (v/c), value (v/n), group (v/c)
## + edges from 909f7f2 (vertex names):
## [1] origin->group1 origin->group2 origin->group3
## [4] origin->group4 origin->group5 origin->group6
## [7] origin->group7 origin->group8 origin->group9
## [10] origin->group10 group1->subgroup_1 group1->subgroup_2
## [13] group1->subgroup_3 group1->subgroup_4 group1->subgroup_5
## [16] group1->subgroup_6 group1->subgroup_7 group1->subgroup_8
## [19] group1->subgroup_9 group1->subgroup_10 group2->subgroup_11
## [22] group2->subgroup_12 group2->subgroup_13 group2->subgroup_14
## + ... omitted several edges
# The connection object must refer to the ids of the leaves:
from <- match( connect$from, vertices$name)
to <- match( connect$to, vertices$name)
# Basic graph
# 绘制基础网络图
ggraph(mygraph, layout = 'dendrogram', circular = TRUE) +
# 添加边连接线
geom_conn_bundle(data = get_con(from = from, to = to),
alpha=0.6, colour="skyblue", tension = .7) +
# 设置节点
geom_node_point(aes(filter = leaf, x = x*1.05, y=y*1.05)) +
theme_void()
image.png
# 更改边连接线的颜色
p <- ggraph(mygraph, layout = 'dendrogram', circular = TRUE) +
geom_node_point(aes(filter = leaf, x = x*1.05, y=y*1.05)) +
theme_void()
# Use the 'value' column of the connection data frame for the color:
p + geom_conn_bundle(data = get_con(from = from, to = to),
aes(colour=value, alpha=value))
image.png
# In this case you can change the color palette
p + geom_conn_bundle(data = get_con(from = from, to = to),
aes(colour=value)) +
scale_edge_color_continuous(low="white", high="red")
image.png
p + geom_conn_bundle(data = get_con(from = from, to = to),
aes(colour=value)) +
scale_edge_colour_distiller(palette = "BuPu")
image.png
# Color depends of the index: the from and the to are different
p + geom_conn_bundle(data = get_con(from = from, to = to),
width=1, alpha=0.2, aes(colour=..index..)) +
scale_edge_colour_distiller(palette = "RdPu") +
theme(legend.position = "none")
image.png
# 更改节点的颜色，大小等信息
# Basic usual argument
p <- ggraph(mygraph, layout = 'dendrogram', circular = TRUE) +
geom_conn_bundle(data = get_con(from = from, to = to),
width=1, alpha=0.2, aes(colour=..index..)) +
scale_edge_colour_distiller(palette = "RdPu") +
theme_void() +
theme(legend.position = "none")
# just a blue uniform color. Note that the x*1.05 allows to make a space between the points and the connection ends
p + geom_node_point(aes(filter = leaf, x = x*1.05, y=y*1.05),
colour="skyblue", alpha=0.3, size=3)
image.png
# It is good to color the points following their group appartenance
library(RColorBrewer)
p + geom_node_point(aes(filter = leaf, x = x*1.05, y=y*1.05,
colour=group), size=3) +
scale_colour_manual(values= rep( brewer.pal(9,"Paired"), 30))
image.png
# And you can adjust the size to whatever variable quite easily!
p + geom_node_point(aes(filter = leaf, x = x*1.05, y=y*1.05,
colour=group, size=value, alpha=0.2)) +
scale_colour_manual(values= rep( brewer.pal(9,"Paired"), 30)) +
scale_size_continuous( range = c(0.1,10) )
image.png
使用networkD3包绘制动态网络图
# 安装并加载所需的R包
#install.packages("networkD3")
library(networkD3)
## Warning: package 'networkD3' was built under R version 3.6.3
# 使用simpleNetwork()函数绘制简单交互网络图
# 构建示例数据
Source <- c("A", "A", "A", "A", "B", "B", "C", "C", "D")
Target <- c("B", "C", "D", "J", "E", "F", "G", "H", "I")
NetworkData <- data.frame(Source, Target)
NetworkData
## Source Target
## 1 A B
## 2 A C
## 3 A D
## 4 A J
## 5 B E
## 6 B F
## 7 C G
## 8 C H
## 9 D I
# Create graph
simpleNetwork(NetworkData)
image.png
# 使用forceNetwork()函数绘制交互网络图
# Load data
data(MisLinks)
data(MisNodes)
# 查看内置数据集
head(MisLinks)
## source target value
## 1 1 0 1
## 2 2 0 8
## 3 3 0 10
## 4 3 2 6
## 5 4 0 1
## 6 5 0 1
head(MisNodes)
## name group size
## 1 Myriel 1 15
## 2 Napoleon 1 20
## 3 Mlle.Baptistine 1 23
## 4 Mme.Magloire 1 30
## 5 CountessdeLo 1 11
## 6 Geborand 1 9
# Create graph
forceNetwork(Links = MisLinks, Nodes = MisNodes,
Source = "source", Target = "target", Value = "value",
NodeID = "name", Group = "group",
opacity = 0.4, zoom = TRUE)
image.png
# Create graph with legend and varying node radius
forceNetwork(Links = MisLinks, Nodes = MisNodes,
Source = "source", Target = "target", Value = "value",
NodeID = "name", Nodesize = "size", Group = "group",
radiusCalculation = "Math.sqrt(d.nodesize)+6",
opacity = 0.4, legend = TRUE)
image.png
# Create graph directed arrows
forceNetwork(Links = MisLinks, Nodes = MisNodes,
Source = "source", Target = "target", Value = "value",
NodeID = "name", Group = "group",
opacity = 0.4, arrows = TRUE)
image.png
# 使用chordNetwork()函数绘制和弦交互式网络图
# 构建示例数据
hairColourData <- matrix(c(11975, 1951, 8010, 1013,
5871, 10048, 16145, 990,
8916, 2060, 8090, 940,
2868, 6171, 8045, 6907),
nrow = 4)
head(hairColourData)
## [,1] [,2] [,3] [,4]
## [1,] 11975 5871 8916 2868
## [2,] 1951 10048 2060 6171
## [3,] 8010 16145 8090 8045
## [4,] 1013 990 940 6907
# Create graph
chordNetwork(Data = hairColourData,
width = 500,
height = 500,
colourScale = c("#000000", "#FFDD89", "#957244", "#F26223"),
labels = c("red", "brown", "blond", "gray"))
image.png
# 使用dendroNetwork()函数绘制层次聚类交互式网络图
# 使用hclust()函数进行层次聚类
hc <- hclust(dist(USArrests), "ave")
hc
##
## Call:
## hclust(d = dist(USArrests), method = "ave")
##
## Cluster method : average
## Distance : euclidean
## Number of objects: 50
# Create graph
dendroNetwork(hc, height = 800)
image.png
# 设置标签颜色
dendroNetwork(hc, textColour = c("red", "green", "orange")[cutree(hc, 3)],
height = 800)
image.png
# 使用radialNetwork()函数绘制放射状交互式网络图
# 构建示例数据
CanadaPC <- list(name = "Canada", children = list(list(name = "Newfoundland",
children = list(list(name = "St. John's"))),
list(name = "PEI",
children = list(list(name = "Charlottetown"))),
list(name = "Nova Scotia",
children = list(list(name = "Halifax"))),
list(name = "New Brunswick",
children = list(list(name = "Fredericton"))),
list(name = "Quebec",
children = list(list(name = "Montreal"),
list(name = "Quebec City"))),
list(name = "Ontario",
children = list(list(name = "Toronto"),
list(name = "Ottawa"))),
list(name = "Manitoba",
children = list(list(name = "Winnipeg"))),
list(name = "Saskatchewan",
children = list(list(name = "Regina"))),
list(name = "Nunavuet",
children = list(list(name = "Iqaluit"))),
list(name = "NWT",
children = list(list(name = "Yellowknife"))),
list(name = "Alberta",
children = list(list(name = "Edmonton"))),
list(name = "British Columbia",
children = list(list(name = "Victoria"),
list(name = "Vancouver"))),
list(name = "Yukon",
children = list(list(name = "Whitehorse")))
))
CanadaPC
$name
[1] "Canada"
$children
$children[[1]]
$children[[1]]$name
[1] "Newfoundland"
$children[[1]]$children
$children[[1]]$children[[1]]
$children[[1]]$children[[1]]$name
[1] "St. John's"
$children[[2]]
$children[[2]]$name
[1] "PEI"
$children[[2]]$children
$children[[2]]$children[[1]]
$children[[2]]$children[[1]]$name
[1] "Charlottetown"
$children[[3]]
$children[[3]]$name
[1] "Nova Scotia"
$children[[3]]$children
$children[[3]]$children[[1]]
$children[[3]]$children[[1]]$name
[1] "Halifax"
$children[[4]]
$children[[4]]$name
[1] "New Brunswick"
$children[[4]]$children
$children[[4]]$children[[1]]
$children[[4]]$children[[1]]$name
[1] "Fredericton"
$children[[5]]
$children[[5]]$name
[1] "Quebec"
$children[[5]]$children
$children[[5]]$children[[1]]
$children[[5]]$children[[1]]$name
[1] "Montreal"
$children[[5]]$children[[2]]
$children[[5]]$children[[2]]$name
[1] "Quebec City"
$children[[6]]
$children[[6]]$name
[1] "Ontario"
$children[[6]]$children
$children[[6]]$children[[1]]
$children[[6]]$children[[1]]$name
[1] "Toronto"
$children[[6]]$children[[2]]
$children[[6]]$children[[2]]$name
[1] "Ottawa"
$children[[7]]
$children[[7]]$name
[1] "Manitoba"
$children[[7]]$children
$children[[7]]$children[[1]]
$children[[7]]$children[[1]]$name
[1] "Winnipeg"
$children[[8]]
$children[[8]]$name
[1] "Saskatchewan"
$children[[8]]$children
$children[[8]]$children[[1]]
$children[[8]]$children[[1]]$name
[1] "Regina"
$children[[9]]
$children[[9]]$name
[1] "Nunavuet"
$children[[9]]$children
$children[[9]]$children[[1]]
$children[[9]]$children[[1]]$name
[1] "Iqaluit"
$children[[10]]
$children[[10]]$name
[1] "NWT"
$children[[10]]$children
$children[[10]]$children[[1]]
$children[[10]]$children[[1]]$name
[1] "Yellowknife"
$children[[11]]
$children[[11]]$name
[1] "Alberta"
$children[[11]]$children
$children[[11]]$children[[1]]
$children[[11]]$children[[1]]$name
[1] "Edmonton"
$children[[12]]
$children[[12]]$name
[1] "British Columbia"
$children[[12]]$children
$children[[12]]$children[[1]]
$children[[12]]$children[[1]]$name
[1] "Victoria"
$children[[12]]$children[[2]]
$children[[12]]$children[[2]]$name
[1] "Vancouver"
$children[[13]]
$children[[13]]$name
[1] "Yukon"
$children[[13]]$children
$children[[13]]$children[[1]]
$children[[13]]$children[[1]]$name
[1] "Whitehorse"
# Create graph
radialNetwork(List = CanadaPC, fontSize = 10)
image.png
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936
[2] LC_CTYPE=Chinese (Simplified)_China.936
[3] LC_MONETARY=Chinese (Simplified)_China.936
[4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.936
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] networkD3_0.4 forcats_0.4.0 stringr_1.4.0
[4] dplyr_1.0.2 purrr_0.3.2 readr_1.3.1
[7] tidyr_1.1.2 tibble_2.1.3 tidyverse_1.2.1
[10] ggraph_2.0.3 ggplot2_3.3.2 RColorBrewer_1.1-2
[13] igraph_1.2.4.1
loaded via a namespace (and not attached):
[1] ggrepel_0.8.1 Rcpp_1.0.5 lubridate_1.7.4
[4] lattice_0.20-38 assertthat_0.2.1 digest_0.6.20
[7] ggforce_0.3.2 R6_2.4.0 cellranger_1.1.0
[10] backports_1.1.4 evaluate_0.14 httr_1.4.0
[13] pillar_1.4.2 rlang_0.4.7 readxl_1.3.1
[16] rstudioapi_0.10 Matrix_1.2-17 rmarkdown_1.13
[19] labeling_0.3 htmlwidgets_1.3 polyclip_1.10-0
[22] munsell_0.5.0 broom_0.5.2 xfun_0.8
[25] compiler_3.6.0 modelr_0.1.4 pkgconfig_2.0.2
[28] htmltools_0.3.6 tidyselect_1.1.0 gridExtra_2.3
[31] graphlayouts_0.7.0 viridisLite_0.3.0 crayon_1.3.4
[34] withr_2.1.2 MASS_7.3-51.4 grid_3.6.0
[37] nlme_3.1-139 jsonlite_1.6 gtable_0.3.0
[40] lifecycle_0.2.0 magrittr_1.5 scales_1.0.0
[43] cli_1.1.0 stringi_1.4.3 farver_1.1.0
[46] viridis_0.5.1 xml2_1.2.0 generics_0.0.2
[49] vctrs_0.3.2 tools_3.6.0 glue_1.4.2
[52] tweenr_1.0.1 hms_0.4.2 rsconnect_0.8.16
[55] yaml_2.2.0 colorspace_1.4-1 tidygraph_1.2.0
[58] rvest_0.3.4 knitr_1.23 haven_2.3.1
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