R语言绘制树形图--四个分支用四个颜色进行区分

library(factoextra)
USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "Set1") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "Set2") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "Pastel1") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "Pastel2") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "Paired") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "Dark2") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "Spectral") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "RdYlGn") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "RdYlBu") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "RdGy") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "RdBu") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "PuOr") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "PRGn") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "PiYG") # Visualize and cut 
# into 4 groups

USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "BrBG") # Visualize and cut 
# into 4 groups

参考文献：

https://blog.csdn.net/m0_38127487/article/details/125502889

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