# Code to produce plots for the clusterability package

# Copyright (C) 2026  Zachariah Neville, Naomi Brownstein, Andreas Adolfsson, Margareta Ackerman

# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.

# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.


###### Initial Setup ######
library(clusterability)
data(normals1)
data(normals2)
data(normals3)

col1 <- rgb(63, 116, 255, maxColorValue = 255)
col2 <- rgb(225, 4, 43, maxColorValue = 255)
col3 <- rgb(63, 145, 70, maxColorValue = 255)

sym1 <- 21
sym2 <- 23
sym3 <- 24

n1 <- ifelse(normals1$cluster == 1, col1, ifelse(normals1$cluster == 2, col2, col3))
n2 <- ifelse(normals2$cluster == 1, col1, ifelse(normals2$cluster == 2, col2, col3))
n3 <- ifelse(normals3$cluster == 1, col1, ifelse(normals3$cluster == 2, col2, col3))

s1 <- ifelse(normals1$cluster == 1, sym1, ifelse(normals1$cluster == 2, sym2, sym3))
s2 <- ifelse(normals2$cluster == 1, sym1, ifelse(normals2$cluster == 2, sym2, sym3))
s3 <- ifelse(normals3$cluster == 1, sym1, ifelse(normals3$cluster == 2, sym2, sym3))


##### Normals 1 #####
dev.new(width = 300, height = 300, unit = "px") # new window
par(mar = c(5, 5, 2, 2)) # margins
par(xpd = T, mar = par()$mar + c(4.25, 0, 0.75, 0))
plot(normals1[, 1:2], col = "black", pch = s1, cex = 3, cex.main = 3, cex.axis = 1.75, cex.lab = 2.75, main = "normals1", pty = "s", bg = n1)
legend(x = -0.65, y = 0, legend = c("Cluster 1"), col = "black", pch = c(sym1), pt.bg = c(col1), cex = 2, pt.cex = 3, horiz = TRUE)


##### Normals 2 #####
dev.new(width = 300, height = 300, unit = "px")
par(mar = c(5, 5, 2, 2))
par(xpd = T, mar = par()$mar + c(4.25, 0, 0.75, 0))
plot(normals2[, 1:2], col = "black", pch = s2, cex = 3, cex.main = 3, cex.axis = 1.75, cex.lab = 2.75, main = "normals2", pty = "s", bg = n2)
legend(x = -4, y = -6.80, legend = c("Cluster 1", "Cluster 2"), col = "black", pch = c(sym1, sym2), pt.bg = c(col1, col2), cex = 2, pt.cex = 3, horiz = TRUE)


##### Normals 3 #####
dev.new(width = 300, height = 300, unit = "px")
par(mar = c(5, 5, 2, 2))
par(xpd = T, mar = par()$mar + c(4.25, 0, 0.75, 0))
plot(normals3[, 1:2], col = "black", pch = s3, cex = 3, cex.main = 3, cex.axis = 1.75, cex.lab = 2.75, main = "normals3", pty = "s", bg = n3)
legend(x = -2.5, y = -5.75, legend = c("Cluster 1", "Cluster 2", "Cluster 3"), col = "black", pch = c(sym1, sym2, sym3), pt.bg = c(col1, col2, col3), cex = 2, pt.cex = 3, horiz = TRUE)


##### Setup for Normals 4 and 5 #####
if (!suppressWarnings(require("plotly", character.only = TRUE))) {
  # require() issues a warning if the package isn't installed but displays it out of order. Suppress the warning to avoid confusion.
  print("The plotly package is required to generate example plots. Installing package now.")
  install.packages("plotly")
}

library(plotly)

data(normals4)
normals4$markers[which(normals4$cluster == 2)] <- "square"
normals4$markers[which(normals4$cluster == 1)] <- "circle"

data(normals5)
normals5$markers[which(normals5$cluster == 1)] <- "circle"
normals5$markers[which(normals5$cluster == 2)] <- "square"
normals5$markers[which(normals5$cluster == 3)] <- "diamond"

colors2 <- c(col1, col2)
colors3 <- c(col1, col2, col3)

scene <- list(
  xaxis = list(titlefont = list(size = 20), tickfont = list(size = 14)),
  yaxis = list(titlefont = list(size = 20), tickfont = list(size = 14)),
  zaxis = list(titlefont = list(size = 20), tickfont = list(size = 14)),
  camera = list(eye = list(x = 0.8, y = -2.2, z = 0.15))
)


normals4$clustername[which(normals4$cluster == 2)] <- "Cluster 2"
normals4$clustername[which(normals4$cluster == 1)] <- "Cluster 1"


normals5$clustername[which(normals5$cluster == 1)] <- "Cluster 1"
normals5$clustername[which(normals5$cluster == 2)] <- "Cluster 2"
normals5$clustername[which(normals5$cluster == 3)] <- "Cluster 3"


##### Normals 4 #####
plot_ly(data = normals4) %>%
  add_markers(
    x = ~x, y = ~y, z = ~z, type = "scatter3d", mode = "markers", stroke = I("black"), span = I(1.5),
    color = ~clustername, colors = colors2, symbol = ~ I(markers)
  ) %>%
  layout(
    title = list(text = "<b>normals4</b>", yanchor = "bottom", y = 0.80, font = list(size = 24)),
    scene = scene,
    legend = list(font = list(size = 20), orientation = "h", x = 0.2, y = 0.05, borderwidth = 1)
  )


##### Normals 5 #####
plot_ly(data = normals5) %>%
  add_markers(
    x = ~x, y = ~y, z = ~z, type = "scatter3d", mode = "markers", stroke = I("black"), span = I(1.5),
    color = ~clustername, colors = colors3, symbol = ~ I(markers)
  ) %>%
  layout(
    title = list(text = "<b>normals5</b>", yanchor = "bottom", y = 0.85, font = list(size = 24)), scene = scene,
    legend = list(font = list(size = 20), orientation = "h", x = 0.15, y = 0, borderwidth = 1)
  )


##### iris #####
data(iris)

iris <- iris[, -5]
par(mar = c(5, 5, 2, 2))

irispca <- clusterability:::perform_pca(iris, TRUE, TRUE)

dev.new(width = 300, height = 300, unit = "px")
hist(irispca, col = col2, main = "iris", xlab = "Score of First Principal Component", ylab = "Frequency", cex.main = 1.5, cex.axis = 1, cex.lab = 1.25)

irisdist <- as.vector(dist(iris))

dev.new(width = 300, height = 300, unit = "px")
hist(irisdist, col = col3, main = "iris", xlab = "Pairwise Distance", ylab = "Frequency", cex.main = 1.5, cex.axis = 1, cex.lab = 1.25)

irisspca <- clusterability:::perform_spca_elasticnet(as.matrix(iris), 0.01, 1e-6)

dev.new(width = 300, height = 300, unit = "px")
hist(irisspca, col = col1, main = "iris", xlab = "Score of First Sparse Principal Component", ylab = "Frequency", cex.main = 1.5, cex.axis = 1, cex.lab = 1.25)


##### cars #####
data(cars)

dev.new(width = 300, height = 300, unit = "px")
par(mar = c(5, 5, 2, 2))
plot(cars, col = "black", pch = sym1, cex = 1.5, cex.main = 1.5, cex.axis = 1, cex.lab = 1.25, main = "cars", pty = "s", bg = col1, xlab = "Speed", ylab = "Distance")

carspca <- clusterability:::perform_pca(cars, TRUE, TRUE)

dev.new(width = 300, height = 300, unit = "px")
hist(carspca, col = col1, main = "cars", xlab = "Score of First Principal Component", ylab = "Frequency", cex.main = 1.5, cex.axis = 1, cex.lab = 1.25)

carsdist <- as.vector(dist(cars))

dev.new(width = 300, height = 300, unit = "px")
hist(carsdist, col = col2, main = "cars", xlab = "Pairwise Distance", ylab = "Frequency", cex.main = 1.5, cex.axis = 1, cex.lab = 1.25)

carsspca <- clusterability:::perform_spca_elasticnet(as.matrix(cars), 0.01, 1e-6)

dev.new(width = 300, height = 300, unit = "px")
hist(carsspca, col = col3, main = "cars", xlab = "Score of First Sparse Principal Component", ylab = "Frequency", cex.main = 1.5, cex.axis = 1, cex.lab = 1.25)
