R 手动设置图例名称

我有一个数据框，在该数据框下我进行了适当处理，以创建一个具有以下内容的群集散点图：

library(tidyverse)  # data manipulation
library(cluster)    # clustering algorithms
library(factoextra) # clustering algorithms & visualization
library(plotly)
df <- USArrests
df <- na.omit(df)

df <- scale(df)
distance <- get_dist(df)

k2 <- kmeans(df, centers = 2, nstart = 25)
df %>%
  as_tibble() %>%
  mutate(cluster = k2$cluster,
         state = row.names(USArrests))
p2<-fviz_cluster(k2, data = df, geom="point")
#+ scale_fill_discrete(name = "Cluster", labels = c("1", "2", "3","4"))
p2
ggplotly(p2)

library（tidyverse）#数据操作
库（群集）#群集算法
库（factoextra）#聚类算法与可视化
图书馆（绘本）
df我得到十字架的最简单方法是重命名对象内的标签

p2<-fviz_cluster(k2, data = df, geom="point")

p3 <- ggplotly(p2)

p3[["x"]][["data"]][[2]][["name"]] <- "2"
p3

p2在
p2中是否有输入错误？有趣的是，这怎么可能与4个簇（中心=4）而不是2个簇一起工作？我到处用4替换2，这又错了。很抱歉，这更多的是一个修复方案，而不是解决方案。[他们似乎也有类似的问题。如果我理解他们的话，请纠正它，因为
fviz_cluster（）
-产生多个刻度，而
ggplolty（）
无法处理它们。[这似乎很相似，他建议直接使用
plotly
构建int。嗯，我明白了。问题是，我无法直接使用plotly找到相同的绘图。如果可以使用4个群集，您的回答会很好。我使用了您的示例数据和代码。或者您具体的意思是什么？可能是这样。'p3[“x”][“data”]][[1] ][[“text”]]”在这里，您可以看到悬停文本中包含的内容，您可以将其替换为州名称。
# Example
library(tidyverse)  # data manipulation
library(cluster)    # clustering algorithms
library(factoextra) # clustering algorithms & visualization
library(plotly)
df <- USArrests
df <- na.omit(df)

df <- scale(df)
distance <- get_dist(df)

# added center variable for number of centers in kmeans
# this will also be used to select elemnets from ggplot or ggplotly later

centers=4
k2 <- kmeans(df, centers = centers, nstart = 25)
df %>%
  as_tibble() %>%
  mutate(cluster = k2$cluster,
         state = row.names(USArrests))

p2<-fviz_cluster(k2, data = df, geom="point")

p2
p3 <- ggplotly(p2)

# Solution
# First Problem: Changing legend labels 
# Because the transition from ggplot to ggplotly
#   messes up multiple scales like here (color and shape)
# Why it looks like intended when only changing the point layer, 
#   I don't know

for (i in 1:centers) {
  p3[["x"]][["data"]][[i]][["name"]] <- i
}

# Second Problem: interactive points
# ggplot saves the data in one list and ggplotly splits the data 
#    depending on layer and cluster
# for the labels it is enough to change the point layers 
#    (the first x depending on num. of centers)
# to add more inforamtion to labels 
#   manipulate the variable names_states with html
for (i in 1:centers) {
  name_states <- p2[["data"]]%>%
    filter(cluster==i)%>%
    select(name)

  p3[["x"]][["data"]][[i]][["text"]] <- as.vector(name_states$name)
}

# Changing order of layers because polygon-layer is on top and 
#    makes it impossible to hover over points beneeth
p3[["x"]][["data"]] <- p3[["x"]][["data"]][(centers*3):1]

# Now you can hover over every point and can see the state name
p3