循环打包以可视化 R 中的层次结构数据

在本文中，我们将讨论使用循环打包可视化处理分层数据。要使用 R 编程语言准备循环包装，我们将使用ggraph包并准备一个气泡来显示层次结构。

循环打包以可视化 R 中的层次结构数据

准备分层数据

在这里，我们将准备分层数据进行演示。为此，我们将使用耀斑数据集。

R

# Libraries
library(ggraph) # to prepare visualisation
library(igraph) # for network analysis
library(tidyverse) # for data handling
library(viridis) # for generating the color map
 
# data for hierarchical structure
edges = flare$edges
head(edges)

R

vertices = flare$vertices
head(vertices)

R

# preparing the graph
mygraph <- graph_from_data_frame( edges,
                                  vertices = vertices )
mygraph

R

# plot the graph using ggraph
ggraph(mygraph, # graph data
       layout = 'circlepack',
        
       # size of bubbles based on
       # the size parameter in vertices data
       weight = size) +
  geom_node_circle(aes(fill = as.factor(depth),
                       color = as.factor(depth) )) +
  # define the color of each different labels
  scale_color_manual( values=c("0" = "green", "1" = "red",
                               "2" = "red",
                               "3" = "red", "4"="red") ) +
  scale_fill_manual(values = c("0" = "green", "1" = viridis(4)[1],
                               "2" = viridis(4)[2], "3" = viridis(4)[3],
                               "4" = viridis(4)[4])) +
  theme_void()

输出：

为层次结构创建另一个数据框

R

vertices = flare$vertices
head(vertices)

输出：

使用数据框准备图表：

R

# preparing the graph
mygraph <- graph_from_data_frame( edges,
                                  vertices = vertices )
mygraph

输出：

IGRAPH 6e05b59 DN-- 252 251 --  
+ attr: name (v/c), size (v/n), shortName (v/c) 
+ edges from 6e05b59 (vertex names): 
[1] flare.analytics.cluster->flare.analytics.cluster.AgglomerativeCluster 
[2] flare.analytics.cluster->flare.analytics.cluster.CommunityStructure   
[3] flare.analytics.cluster->flare.analytics.cluster.HierarchicalCluster  
[4] flare.analytics.cluster->flare.analytics.cluster.MergeEdge            
[5] flare.analytics.graph  ->flare.analytics.graph.BetweennessCentrality  
[6] flare.analytics.graph  ->flare.analytics.graph.LinkDistance           
[7] flare.analytics.graph  ->flare.analytics.graph.MaxFlowMinCut          
[8] flare.analytics.graph  ->flare.analytics.graph.ShortestPaths          
+ ... omitted several edges

可视化循环层次结构

在这里，我们将可视化具有层次结构的数据框。

R

# plot the graph using ggraph
ggraph(mygraph, # graph data
       layout = 'circlepack',
        
       # size of bubbles based on
       # the size parameter in vertices data
       weight = size) +
  geom_node_circle(aes(fill = as.factor(depth),
                       color = as.factor(depth) )) +
  # define the color of each different labels
  scale_color_manual( values=c("0" = "green", "1" = "red",
                               "2" = "red",
                               "3" = "red", "4"="red") ) +
  scale_fill_manual(values = c("0" = "green", "1" = viridis(4)[1],
                               "2" = viridis(4)[2], "3" = viridis(4)[3],
                               "4" = viridis(4)[4])) +
  theme_void()

输出：