fviz_dend: Enhanced Visualization of Dendrogram
In factoextra: Extract and Visualize the Results of Multivariate Data Analyses

Description Usage Arguments Value Examples

Draws easily beautiful dendrograms using either R base plot or ggplot2. Provides also an option for drawing a circular dendrogram and phylogenic trees.

fviz_dend(
  x,
  k = NULL,
  h = NULL,
  k_colors = NULL,
  palette = NULL,
  show_labels = TRUE,
  color_labels_by_k = TRUE,
  label_cols = NULL,
  labels_track_height = NULL,
  repel = FALSE,
  lwd = 0.7,
  type = c("rectangle", "circular", "phylogenic"),
  phylo_layout = "layout.auto",
  rect = FALSE,
  rect_border = "gray",
  rect_lty = 2,
  rect_fill = FALSE,
  lower_rect,
  horiz = FALSE,
  cex = 0.8,
  main = "Cluster Dendrogram",
  xlab = "",
  ylab = "Height",
  sub = NULL,
  ggtheme = theme_classic(),
  ...
)

`x`	an object of class dendrogram, hclust, agnes, diana, hcut, hkmeans or HCPC (FactoMineR).
`k`	the number of groups for cutting the tree.
`h`	a numeric value. Cut the dendrogram by cutting at height h. (k overrides h)
`k_colors, palette`	a vector containing colors to be used for the groups. It should contains k number of colors. Allowed values include also "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".
`show_labels`	a logical value. If TRUE, leaf labels are shown. Default value is TRUE.
`color_labels_by_k`	logical value. If TRUE, labels are colored automatically by group when k != NULL.
`label_cols`	a vector containing the colors for labels.
`labels_track_height`	a positive numeric value for adjusting the room for the labels. Used only when type = "rectangle".
`repel`	logical value. Use repel = TRUE to avoid label overplotting when type = "phylogenic".
`lwd`	a numeric value specifying branches and rectangle line width.
`type`	type of plot. Allowed values are one of "rectangle", "triangle", "circular", "phylogenic".
`phylo_layout`	the layout to be used for phylogenic trees. Default value is "layout.auto". Allowed values include: `layout.auto`, `layout_with_drl`, `layout_as_tree`, `layout.gem`, `layout.mds` and `layout_with_lgl`.
`rect`	logical value specifying whether to add a rectangle around groups. Used only when k != NULL.
`rect_border, rect_lty`	border color and line type for rectangles.
`rect_fill`	a logical value. If TRUE, fill the rectangle.
`lower_rect`	a value of how low should the lower part of the rectangle around clusters. Ignored when rect = FALSE.
`horiz`	a logical value. If TRUE, an horizontal dendrogram is drawn.
`cex`	size of labels
`main, xlab, ylab`	main and axis titles
`sub`	Plot subtitle. If NULL, the method used hierarchical clustering is shown. To remove the subtitle use sub = "".
`ggtheme`	function, ggplot2 theme name. Default value is theme_classic(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....
`...`	other arguments to be passed to the function plot.dendrogram()

an object of class fviz_dend which is a ggplot with the attributes "dendrogram" accessible using attr(x, "dendrogram"), where x is the result of fviz_dend().

# Load and scale the data
data(USArrests)
df <- scale(USArrests)

# Hierarchical clustering
res.hc <- hclust(dist(df))

# Default plot
fviz_dend(res.hc)

# Cut the tree
fviz_dend(res.hc, cex = 0.5, k = 4, color_labels_by_k = TRUE)

# Don't color labels, add rectangles
fviz_dend(res.hc, cex = 0.5, k = 4, 
 color_labels_by_k = FALSE, rect = TRUE)
 
# Change the color of tree using black color for all groups
# Change rectangle border colors
fviz_dend(res.hc, rect = TRUE, k_colors ="black",
rect_border = 2:5, rect_lty = 1)

# Customized color for groups
fviz_dend(res.hc, k = 4, 
 k_colors = c("#1B9E77", "#D95F02", "#7570B3", "#E7298A"))
 
 
 # Color labels using k-means clusters
 km.clust <- kmeans(df, 4)$cluster
 fviz_dend(res.hc, k = 4, 
   k_colors = c("blue", "green3", "red", "black"),
   label_cols =  km.clust[res.hc$order], cex = 0.6)