小编sta*_*555的帖子

我在 R 中模拟了一些图网络数据（~10,000 个观察值），并尝试使用 R 中的 visNetwork 库将其可视化。 然而，数据非常混乱，很难进行可视化分析（我明白在现实生活中，网络数据是旨在使用图查询语言进行分析）。

就目前而言，我可以做些什么来改进我创建的图网络的可视化（这样我就可以探索一些相互叠加的链接和节点）？

可以使用诸如“networkD3”和“diagrammeR”之类的库来更好地可视化该网络吗？

我在下面附上了我的可重现代码：

library(igraph)
library(dplyr)
library(visNetwork)

#create file from which to sample from
x5 <- sample(1:10000, 10000, replace=T)
#convert to data frame
x5 = as.data.frame(x5)

#create first file (take a random sample from the created file)
a = sample_n(x5, 9000)
#create second file (take a random sample from the created file)
b = sample_n(x5, 9000)

#combine
c = cbind(a,b)
#create dataframe
c = data.frame(c)
#rename column names
colnames(c) <- c("a","b")

graph …

我有如下所示的数据：https : //imgur.com/a/1hOsFpF

第一个数据集是标准格式数据集，其中包含人员及其财务属性的列表。

第二个数据集包含这些人之间的“关系”——他们互相支付了多少，以及他们彼此欠了多少。

我有兴趣了解更多关于网络和基于图的聚类 - 但我试图更好地了解什么类型的情况需要基于网络的聚类，即我不想在不需要的地方使用图聚类（避免“方钉圆孔"类型情况）。

使用 R，首先我创建了一些假数据：

library(corrr)
 library(dplyr) 
library(igraph) 
library(visNetwork)
 library(stats)

# create first data set

Personal_Information <- data.frame(

"name" = c("John", "Jack", "Jason", "Jim", "Julian", "Jack", "Jake", "Joseph"),

"age" = c("41","33","24","66","21","66","29", "50"),

"salary" = c("50000","20000","18000","66000","77000","0","55000","40000"),

"debt" = c("10000","5000","4000","0","20000","5000","0","1000"

)


Personal_Information$age = as.numeric(Personal_Information$age)
Personal_Information$salary = as.numeric(Personal_Information$salary)
Personal_Information$debt = as.numeric(Personal_Information$debt)
create second data set
Relationship_Information <-data.frame(

"name_a" = c("John","John","John","Jack","Jack","Jack","Jason","Jason","Jim","Jim","Jim","Julian","Jake","Joseph","Joseph"),
"name_b" = c("Jack", "Jason", "Joseph", "John", "Julian","Jim","Jim", "Joseph", "Jack", "Julian", "John", "Joseph", "John", "Jim", "John"),
"how_much_they_owe_each_other" = …

我正在使用 R。使用教程，我能够创建一个统计模型并为一些输出生成可视化图：

#load libraries
library(survival)

library(dplyr)

library(ranger)

library(data.table)

library(ggplot2)

#use the built in "lung" data set
#remove missing values (dataset is called "a")

a <- na.omit(lung)

#create id variable

a$ID <- seq_along(a[,1])

#create test set with only the first 3 rows

new <- a[1:3,]

#create a training set by removing first three rows

a <- a[-c(1:3),]



#fit survival model (random survival forest)

r_fit <- ranger(Surv(time,status) ~ age + sex + ph.ecog + ph.karno + pat.karno + meal.cal + …

使用 plotly 库，我在 R 中绘制了以下图：

library(dplyr)
library(ggplot2)
library(plotly)

set.seed(123)
df <- data.frame(var1 = rnorm(1000,10,10),
                   var2 = rnorm(1000,5,5))

df <- df %>% mutate(var3 = ifelse(var1 <= 5 & var2 <= 5, "a", ifelse(var1 <= 10 & var2 <= 10, "b", "c"))) 


plot = df %>%
  ggplot() + geom_point(aes(x=var1, y= var2, color= var3))


ggplotly(plot)

这是一个简单的散点图 - 生成两个随机变量，然后点的颜色由某些标准决定（例如，如果 var1 和 var2 在某些范围之间）。

从这里，我还可以总结统计数据：

df$var3 = as.factor(df$var3)
summary = df %>%
    group_by(var3) %>%
    summarize(Mean_var1 = mean(var1), Mean_var2 = mean(var2), count=n())

# A tibble: 3 …

我在 R 中使用 ggplot2 库。

假设我有一个看起来像这样的图表：

library(ggplot2)

ggplot(work) + geom_line(aes(x = var1, y = var2, group = 1)) +
               theme(axis.text.x = element_text(angle = 90)) +
               ggtitle("sample graph")

有没有办法直接在这个图中添加第二行？

例如

ggplot(work) + geom_line(aes(x = var1, y = var2, group = 1)) +
               geom_line(aes(x = var1, y = mean(var2), group = 1)) +
               theme(axis.text.x = element_text(angle = 90)) +
               ggtitle("Sample graph")

谢谢