color_branches: Color tree's branches according to sub-clusters
In dendextend: Extending 'dendrogram' Functionality in R

color_branches

R Documentation

Color tree's branches according to sub-clusters

Description

This function is for dendrogram and hclust objects. This function colors both the terminal leaves of a dend's cluster and the edges leading to those leaves. The edgePar attribute of nodes will be augmented by a new list item col. The groups will be defined by a call to cutree using the k or h parameters.

If col is a color vector with a different length than the number of clusters (k) - then a recycled color vector will be used.

Usage

color_branches(
  dend,
  k = NULL,
  h = NULL,
  col,
  groupLabels = NULL,
  clusters,
  warn = dendextend_options("warn"),
  ...
)

Arguments

`dend`	A `dendrogram` or `hclust` tree object
`k`	number of groups (passed to `cutree`)
`h`	height at which to cut tree (passed to `cutree`)
`col`	Function or vector of Colors. By default it tries to use rainbow_hcl from the `colorspace` package. (with parameters c=90 and l=50). If `colorspace` is not available, It will fall back on the rainbow function.
`groupLabels`	If TRUE add numeric group label - see Details for options
`clusters`	an integer vector of clusters. This is passed to branches_attr_by_clusters. This HAS to be of the same length as the number of leaves. Items that belong to no cluster should get the value 0. The vector should be of the same order as that of the labels in the dendrogram. If you create the clusters from something like cutree you would first need to use order.dendrogram on it, before using it in the function.
`warn`	logical (default from dendextend_options("warn") is FALSE). Set if warning are to be issued, it is safer to keep this at TRUE, but for keeping the noise down, the default is FALSE.
`...`	ignored.

Details

If groupLabels=TRUE then numeric group labels will be added to each cluster. If a vector is supplied then these entries will be used as the group labels. If a function is supplied then it will be passed a numeric vector of groups (e.g. 1:5) and must return the formatted group labels.

If the labels of the dendrogram are NOT character (but, for example integers) - they are coerced into character. This step is essential for the proper operation of the function. A dendrogram labels might happen to be integers if they are based on an hclust performed on a dist of an object without rownames.

Value

a tree object of class dendrogram.

Author(s)

Tal Galili, extensively based on code by Gregory Jefferis

Source

This function is a derived work from the color_clusters function, with some ideas from the slice function - both are from the dendroextras package by jefferis.

It extends it by using cutree.dendrogram - allowing the function to work for trees that hclust can not handle (unbranched and non-ultrametric trees). Also, it allows REPEATED cluster color assignments to branches on to the same tree. Something which the original function was not able to handle.

Examples


## Not run: 
par(mfrow = c(1, 2))
dend <- USArrests %>%
  dist() %>%
  hclust(method = "ave") %>%
  as.dendrogram()
d1 <- color_branches(dend, k = 5, col = c(3, 1, 1, 4, 1))
plot(d1) # selective coloring of branches :)
d2 <- color_branches(dend, 5)
plot(d2)

par(mfrow = c(1, 2))
d1 <- color_branches(dend, 5, col = c(3, 1, 1, 4, 1), groupLabels = TRUE)
plot(d1) # selective coloring of branches :)
d2 <- color_branches(dend, 5, groupLabels = TRUE)
plot(d2)

par(mfrow = c(1, 3))
d5 <- color_branches(dend, 5)
plot(d5)
d5g <- color_branches(dend, 5, groupLabels = TRUE)
plot(d5g)
d5gr <- color_branches(dend, 5, groupLabels = as.roman)
plot(d5gr)

par(mfrow = c(1, 1))

# messy - but interesting:
dend_override <- color_branches(dend, 2, groupLabels = as.roman)
dend_override <- color_branches(dend_override, 4, groupLabels = as.roman)
dend_override <- color_branches(dend_override, 7, groupLabels = as.roman)
plot(dend_override)

d5 <- color_branches(dend = dend[[1]], k = 5)


library(dendextend)
data(iris, envir = environment())
d_iris <- dist(iris[, -5])
hc_iris <- hclust(d_iris)
dend_iris <- as.dendrogram(hc_iris)
dend_iris <- color_branches(dend_iris, k = 3)

library(colorspace)
labels_colors(dend_iris) <-
  rainbow_hcl(3)[sort_levels_values(
    as.numeric(iris[, 5])[order.dendrogram(dend_iris)]
  )]

plot(dend_iris,
  main = "Clustered Iris dataset",
  sub = "labels are colored based on the true cluster"
)



# cutree(dend_iris,k=3, order_clusters_as_data=FALSE,
#  try_cutree_hclust=FALSE)
# cutree(dend_iris,k=3, order_clusters_as_data=FALSE)

library(colorspace)

data(iris, envir = environment())
d_iris <- dist(iris[, -5])
hc_iris <- hclust(d_iris)
labels(hc_iris) # no labels, because "iris" has no row names
dend_iris <- as.dendrogram(hc_iris)
is.integer(labels(dend_iris)) # this could cause problems...

iris_species <- rev(levels(iris[, 5]))
dend_iris <- color_branches(dend_iris, k = 3, groupLabels = iris_species)
is.character(labels(dend_iris)) # labels are no longer "integer"

# have the labels match the real classification of the flowers:
labels_colors(dend_iris) <-
  rainbow_hcl(3)[sort_levels_values(
    as.numeric(iris[, 5])[order.dendrogram(dend_iris)]
  )]

# We'll add the flower type
labels(dend_iris) <- paste(as.character(iris[, 5])[order.dendrogram(dend_iris)],
  "(", labels(dend_iris), ")",
  sep = ""
)

dend_iris <- hang.dendrogram(dend_iris, hang_height = 0.1)

# reduce the size of the labels:
dend_iris <- assign_values_to_leaves_nodePar(dend_iris, 0.5, "lab.cex")

par(mar = c(3, 3, 3, 7))
plot(dend_iris,
  main = "Clustered Iris dataset
     (the labels give the true flower species)",
  horiz = TRUE, nodePar = list(cex = .007)
)
legend("topleft", legend = iris_species, fill = rainbow_hcl(3))
a <- dend_iris[[1]]
dend_iris1 <- color_branches(a, k = 3)
plot(dend_iris1)

# str(dendrapply(d2, unclass))
# unclass(d1)

c(1:5) %>% # take some data
  dist() %>% # calculate a distance matrix,
  # on it compute hierarchical clustering using the "average" method,
  hclust(method = "single") %>%
  as.dendrogram() %>%
  color_branches(k = 3) %>%
  plot() # nice, returns the tree as is...


# Example of the "clusters" parameter
par(mfrow = c(1, 2))
dend <- c(1:5) %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
dend %>%
  color_branches(k = 3) %>%
  plot()
dend %>%
  color_branches(clusters = c(1, 1, 2, 2, 3)) %>%
  plot()


# another example, based on the question here:
# https://stackoverflow.com/q/45432271/256662


library(cluster)
set.seed(999)
iris2 <- iris[sample(x = 1:150, size = 50, replace = F), ]
clust <- diana(iris2)
dend <- as.dendrogram(clust)

temp_col <- c("red", "blue", "green")[as.numeric(iris2$Species)]
temp_col <- temp_col[order.dendrogram(dend)]
temp_col <- factor(temp_col, unique(temp_col))

library(dendextend)
dend %>%
  color_branches(clusters = as.numeric(temp_col), col = levels(temp_col)) %>%
  set("labels_colors", as.character(temp_col)) %>%
  plot()

## End(Not run)

dendextend documentation built on Oct. 13, 2024, 1:06 a.m.