colored_dots(
  colors,
  dend,
  rowLabels = NULL,
  cex.rowLabels = 0.9,
  add = TRUE,
  y_scale,
  y_shift,
  text_shift = 1,
  sort_by_labels_order = TRUE,
  horiz = FALSE,
  dot_size = 1,
  ...
)

rows_picking <- c(1:5, 25:30)
dend <- (iris[rows_picking, -5] * 10) %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
odd_numbers <- rows_picking %% 2
cols <- c("red", "white")[odd_numbers + 1]
plot(dend)
colored_dots(cols, dend)
# Example of adjusting postion of dots
plot(dend)
colored_dots(cols, dend,
  y_shift = -1,
  rowLabels = "Odd\n numbers"
)



rows_picking <- c(1:5, 25:30)
dend <- (iris[rows_picking, -5] * 10) %>%
  dist() %>%
  hclust() %>%
  as.dendrogram()
odd_numbers <- rows_picking %% 2
# For leaves that shouldn't have dots, make them the same color as the background,
# or set the alpha value to fully transparant
cols <- c("black", "white")[odd_numbers + 1]
# scale is off
plot(dend)
colored_dots(cols, dend)
# move and scale a bit
plot(dend)
colored_dots(cols, dend,
  y_shift = -1,
  rowLabels = "Odd\n numbers"
)
# Now let's cut the tree and add that info to the plot:
k2 <- cutree(dend, k = 2)
cols2 <- c("#1b9e77", "#d95f02")[k2]

par(mar = c(5, 6, 1, 1))
plot(dend)
colored_dots(cbind(cols2, cols), dend,
  rowLabels = c("2 clusters", "Even numbers")
)

# The same, but with an horizontal plot!
par(mar = c(6, 2, 2, 4))
plot(dend, horiz = TRUE)
colored_dots(cbind(cols2, cols), dend,
  rowLabels = c("2 clusters", "Even numbers"),
  horiz = TRUE
)

# ==============================
# ==============================

## mtcars example

# Create the dend:
dend <- as.dendrogram(hclust(dist(mtcars)))

# Get all company names
comp_names <- unlist(lapply(rownames(mtcars), function(x) strsplit(x, " ")[[1]][[1]]))
# Get the top three occurring companies
top_three <- sort(table(comp_names), decreasing = TRUE)[1:3]
# Match the top three companies to where they are found in the dendrogram labels
top_three <- sapply(names(top_three), function(x) grepl(x, labels(dend)))
top_three <- as.data.frame(top_three)
# "top_three" is now a data frame of the top three companies as columns.
# Each column represents a vector (rows) which is the length of labels(dend).
# The vector has values TRUE and FALSE, for whether the company name matched
# labels(dend)[i]

# Colorblind friendly vector of HEX colors
colorblind_friendly <- c("#1b9e77", "#d95f02", "#7570b3")

# If we run the for-loop on "top_three" we will turn the vectors into a character-type too early,
# so make a copy to "colored_dataframe" which we will work on
colored_dataframe <- top_three

for (i in 1:3) {
  # This replaces TRUE values with a color from our vector of colors
  colored_dataframe[top_three[, i], i] <- colorblind_friendly[[i]]
  # This replaces FALSE values with black HEX, but fully transparent (invisible on plot)
  colored_dataframe[!top_three[, i], i] <- "#00000000"
}

# Color branches and labels by "cutting" the dendrogram at an arbitrary height
dend <- color_branches(dend, h = 170)
dend <- color_labels(dend, h = 170)

### plots
par(mar = c(12, 4, 1, 1))
plot(dend)
colored_dots(colored_dataframe, dend,
  rowLabels = colnames(colored_dataframe), horiz = FALSE, sort_by_labels_order = FALSE
)
# Show a dotted line where tree was "cut"
abline(h = 170, lty = 3)

# horiz version:
par(mar = c(4, 1, 1, 12))
plot(dend, horiz = TRUE)
colored_dots(colored_dataframe, dend,
  rowLabels = colnames(colored_dataframe), horiz = TRUE, sort_by_labels_order = FALSE
)
# Show a dotted line where the tree was "cut"
abline(v = 170, lty = 3)