dendro_data: Extract cluster data from a model into a list of data frames.

In ggdendro: Create Dendrograms and Tree Diagrams Using 'ggplot2'

Extract cluster data from a model into a list of data frames.

Description

This function provides a generic mechanism to extract relevant plotting data, typically line segments and labels, from a variety of cluster models.

Extract line segment and label data from stats::dendrogram() or stats::hclust() object. The resulting object is a list of data frames containing line segment data and label data.

Usage

dendro_data(model, ...)

## Default S3 method:
dendro_data(model, ...)

## S3 method for class 'dendrogram'
dendro_data(model, type = c("rectangle", "triangle"), ...)

## S3 method for class 'hclust'
dendro_data(model, type = c("rectangle", "triangle"), ...)

## S3 method for class 'twins'
dendro_data(model, type = c("rectangle", "triangle"), ...)

Arguments

model	object of type stats::hclust(), stats::dendrogram() or tree::tree()
...	ignored
type	The type of plot, indicating the shape of the dendrogram. "rectangle" will draw rectangular lines, while "triangle" will draw triangular lines.

Details

For stats::dendrogram() and tree::tree() models, extracts line segment data and labels.

Value

a list of data frames that contain the data appropriate to each cluster model

A list with components:

segments	Line segment data
labels	Label data

See Also

There are several implementations for specific cluster algorithms:

• dendro_data.hclust()

• dendro_data.dendrogram()

• dendro_data.tree()

• dendro_data.rpart()

To extract the data for line segments, labels or leaf labels use:

• segment(): the line segment data

• label(): the text for each end segment

• leaf_label(): the leaf labels of a tree diagram

ggdendrogram()

Other dendro_data methods: dendro_data.rpart(), dendro_data.tree(), dendrogram_data(), rpart_labels()

Other dendrogram/hclust functions: dendrogram_data()

Examples

require(ggplot2)

### Demonstrate dendro_data.dendrogram

model <- hclust(dist(USArrests), "ave")
dendro <- as.dendrogram(model)

# Rectangular lines
ddata <- dendro_data(dendro, type = "rectangle")
ggplot(segment(ddata)) +
  geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
  coord_flip() +
  scale_y_reverse(expand = c(0.2, 0)) +
  theme_dendro()

# Triangular lines
ddata <- dendro_data(dendro, type = "triangle")
ggplot(segment(ddata)) +
  geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
  theme_dendro()

# Demonstrate dendro_data.hclust

require(ggplot2)
hc <- hclust(dist(USArrests), "ave")

# Rectangular lines
hcdata <- dendro_data(hc, type = "rectangle")
ggplot(segment(hcdata)) +
  geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
  coord_flip() +
  scale_y_reverse(expand = c(0.2, 0)) +
  theme_dendro()

# Triangular lines
hcdata <- dendro_data(hc, type = "triangle")
ggplot(segment(hcdata)) +
  geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
  theme_dendro()
### Demonstrate the twins of agnes and diana, from package cluster

if (require(cluster)) {
  model <- agnes(votes.repub, metric = "manhattan", stand = TRUE)
  dg <- as.dendrogram(model)
  ggdendrogram(dg)
}


if (require(cluster)) {
  model <- diana(votes.repub, metric = "manhattan", stand = TRUE)
  dg <- as.dendrogram(model)
  ggdendrogram(dg)
}

ggdendro documentation built on March 18, 2022, 5:17 p.m.