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Data-Visualization.R
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Data-Visualization.R
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# Radar Chart
# Normalize Values to be from 0 to 1
mean_features_norm <- cbind(mean_features[1], apply(mean_features[-1],2, function(x){(x-min(x)) / diff(range(x))}))
# First Batch of Musicians
library(radarchart)
library(tidyr)
sample1 <- mean_features[mean_features$Musician %in% c("TaylorSwift", "ArianaGrande", "KendrickLamar", "ShawnMendes", "Maroon5", "PostMalone", "Kygo", "TheChainsmokers", "Adele", "Drake"),]
mean_features_norm_1 <- cbind(sample1[1], apply(sample1[-1],2, function(x){(x-min(x)) / diff(range(x))}))
radarDF_1 <- gather(mean_features_norm_1, key=Attribute, value=Score, -Musician) %>%
spread(key=Musician, value=Score)
chartJSRadar(scores = radarDF_1, scaleStartValue = -1, maxScale =1, showToolTipLabel = TRUE)
# Second Batch of Musicians
sample2 <- mean_features[mean_features$Musician %in% c("JustinBieber", "Coldplay", "KanyeWest", "BrunoMars", "EdSheeran", "Eminem", "Beyonce", "Avicii", "Marshmello", "CalvinHarris"),]
mean_features_norm_2 <- cbind(sample2[1], apply(sample2[-1],2,function(x){(x-min(x)) / diff(range(x))}))
radarDF_2 <- gather(mean_features_norm_2, key=Attribute, value=Score, -Musician) %>%
spread(key=Musician, value=Score)
chartJSRadar(scores = radarDF_2, scaleStartValue = -1, maxScale = 1, showToolTipLabel = TRUE)
# Third Batch of Musicians
sample3 <- mean_features[mean_features$Musician %in% c("JustinTimberlake", "FrankSinatra", "CharliePuth", "MichaelBuble", "MartinGarrix", "EnriqueIglesias", "JohnMayer", "Future", "EltonJohn", "FiftyCent"),]
mean_features_norm_3 <- cbind(sample3[1], apply(sample3[-1],2,function(x){(x-min(x)) / diff(range(x))}))
radarDF_3 <- gather(mean_features_norm_3, key=Attribute, value=Score, -Musician) %>%
spread(key=Musician, value=Score)
chartJSRadar(scores = radarDF_3, scaleStartValue = -1, maxScale = 1, showToolTipLabel = TRUE)
# Fourth Batch of Musicians
sample4 <- mean_features[mean_features$Musician %in% c("Lorde", "LilWayne", "WizKhalifa", "FifthHarmony", "LanaDelRay", "NorahJones", "JamesArthur", "OneRepublic", "TheScript", "StevieWonder"),]
mean_features_norm_4 <- cbind(sample4[1], apply(sample4[-1],2,function(x){(x-min(x)) / diff(range(x))}))
radarDF_4 <- gather(mean_features_norm_4, key=Attribute, value=Score, -Musician) %>%
spread(key=Musician, value=Score)
chartJSRadar(scores = radarDF_4, scaleStartValue = -1, maxScale = 1, showToolTipLabel = TRUE)
# Fifth Batch of Musicians
sample5 <- mean_features[mean_features$Musician %in% c("JasonMraz", "JohnLegend", "Pentatonix", "AliciaKeys", "Usher", "SnoopDogg", "Macklemore", "ZaraLarsson", "JayZ", "Rihanna"),]
mean_features_norm_5 <- cbind(sample5[1], apply(sample5[-1],2,function(x){(x-min(x)) / diff(range(x))}))
radarDF_5 <- gather(mean_features_norm_5, key=Attribute, value=Score, -Musician) %>%
spread(key=Musician, value=Score)
chartJSRadar(scores = radarDF_5, scaleStartValue = -1, maxScale = 1, showToolTipLabel = TRUE)
# Cluster Analysis
# Rescale numeric variables
scaled.features <- scale(mean_features[-1])
rownames(scaled.features) <- mean_features$Musician
# K-Means Clustering
library(ggfortify)
library(ggthemes)
set.seed(5000)
k_means <- kmeans(scaled.features, 6)
kmeans_plot <- autoplot(k_means,
main = "K-means Clustering",
data = scaled.features,
loadings = TRUE,
loadings.colour = "#CC0000",
loadings.label.colour = "#CC0000",
loadings.label = TRUE,
loadings.label.size = 2.2,
loadings.label.repel = T,
label.size = 2.2,
label.repel = T) +
scale_fill_manual(values = c("#000066", "#9999CC", "#66CC99", "#FB7201", "#21CDFF", "#FF219C")) +
scale_color_manual(values = c("#000066", "#9999CC", "#66CC99", "#FB7201", "#21CDFF", "#FF219C")) +
theme(plot.title=element_text(size=18, face="bold"))
kmeans_plot
# Cluster Radar Chart
mean_features_norm_50 <- cbind(mean_features[1], apply(mean_features[-1],2,scale))
cluster_centers <- as.data.frame(k_means$centers)
cluster <- c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6")
cluster_centers <- cbind(cluster, cluster_centers)
radarDF_6 <- gather(cluster_centers, key=Attribute, value=Score, -cluster) %>%
spread(key=cluster, value=Score)
# Change the colours according to clusters
colMatrix = matrix(c(c(4,24,102), c(135,133,193), c(87,196,135), c(251,114,1), c(33,205,255), c(255,33,156)), nrow = 3)
# Chart
chartJSRadar(scores = radarDF_6, scaleStartValue = -4, maxScale =1.5, showToolTipLabel = TRUE, colMatrix = colMatrix)
# Analysis by Features
library(stringr)
# Converting cluster to vector
clusters <- as.vector(k_means$cluster)
clusters <- str_replace_all(clusters, "1", "Cluster 1")
clusters <- str_replace_all(clusters, "2", "Cluster 2")
clusters <- str_replace_all(clusters, "3", "Cluster 3")
clusters <- str_replace_all(clusters, "4", "Cluster 4")
clusters <- str_replace_all(clusters, "5", "Cluster 5")
clusters <- str_replace_all(clusters, "6", "Cluster 6")
mean_features_norm_50 <- cbind(mean_features_norm_50, cluster = clusters)
# Danceability feature
library(ggplot2)
danceability_subset <- mean_features_norm_50[,c("Musician","danceability", "cluster")]
danceability_subset <- danceability_subset[order(danceability_subset$danceability, decreasing = TRUE), ]
danceability_plot <- ggplot(danceability_subset,
aes(x = reorder(Musician, danceability),
y = danceability, label = danceability)) +
xlab("Musician") + ylab("Danceability") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Danceability Feature") + coord_flip()
danceability_plot
# Energy feature
energy_subset <- mean_features_norm_50[,c("Musician","energy", "cluster")]
energy_subset <- energy_subset[order(energy_subset$energy, decreasing = TRUE), ]
energy_plot <- ggplot(energy_subset,
aes(x = reorder(Musician, energy),
y = energy, label = energy)) +
xlab("Musician") + ylab("energy") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Energy Feature") + coord_flip()
energy_plot
# Loudness feature
loudness_subset <- mean_features_norm_50[,c("Musician","loudness", "cluster")]
loudness_subset <- loudness_subset[order(loudness_subset$loudness, decreasing = TRUE), ]
loudness_plot <- ggplot(loudness_subset,
aes(x = reorder(Musician, loudness),
y = loudness, label = loudness)) +
xlab("Musician") + ylab("loudness") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Loudness Feature") + coord_flip()
loudness_plot
# Speechiness feature
speechiness_subset <- mean_features_norm_50[,c("Musician","speechiness", "cluster")]
speechiness_subset <- speechiness_subset[order(speechiness_subset$speechiness, decreasing = TRUE), ]
speechiness_plot <- ggplot(speechiness_subset,
aes(x = reorder(Musician, speechiness),
y = speechiness, label = speechiness)) +
xlab("Musician") + ylab("speechiness") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Speechiness Feature") + coord_flip()
speechiness_plot
# Acousticness feature
acousticness_subset <- mean_features_norm_50[,c("Musician","acousticness", "cluster")]
acousticness_subset <- acousticness_subset[order(acousticness_subset$acousticness, decreasing = TRUE), ]
acousticness_plot <- ggplot(acousticness_subset,
aes(x = reorder(Musician, acousticness),
y = acousticness, label = acousticness)) +
xlab("Musician") + ylab("acousticness") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Acousticness Feature") + coord_flip()
acousticness_plot
# Instrumentalness feature
instrumentalness_subset <- mean_features_norm_50[,c("Musician","instrumentalness", "cluster")]
instrumentalness_subset <- instrumentalness_subset[order(instrumentalness_subset$instrumentalness, decreasing = TRUE), ]
instrumentalness_plot <- ggplot(instrumentalness_subset,
aes(x = reorder(Musician, instrumentalness),
y = instrumentalness, label = instrumentalness)) +
xlab("Musician") + ylab("instrumentalness") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Instrumentalness Feature") + coord_flip()
instrumentalness_plot
# Liveness feature
liveness_subset <- mean_features_norm_50[,c("Musician","liveness", "cluster")]
liveness_subset <- liveness_subset[order(liveness_subset$liveness, decreasing = TRUE), ]
liveness_plot <- ggplot(liveness_subset,
aes(x = reorder(Musician, liveness),
y = liveness, label = liveness)) +
xlab("Musician") + ylab("liveness") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Liveness Feature") + coord_flip()
liveness_plot
# Valence feature
valence_subset <- mean_features_norm_50[,c("Musician","valence", "cluster")]
valence_subset <- valence_subset[order(valence_subset$valence, decreasing = TRUE), ]
valence_plot <- ggplot(valence_subset,
aes(x = reorder(Musician, valence),
y = valence, label = valence)) +
xlab("Musician") + ylab("valence") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Valence Feature") + coord_flip()
valence_plot
# Tempo feature
tempo_subset <- mean_features_norm_50[,c("Musician","tempo", "cluster")]
tempo_subset <- tempo_subset[order(tempo_subset$tempo, decreasing = TRUE), ]
tempo_plot <- ggplot(tempo_subset,
aes(x = reorder(Musician, tempo),
y = tempo, label = tempo)) +
xlab("Musician") + ylab("tempo") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Tempo Feature") + coord_flip()
tempo_plot
# Duration feature
duration_subset <- mean_features_norm_50[,c("Musician","duration_ms", "cluster")]
duration_subset <- duration_subset[order(duration_subset$duration_ms, decreasing = TRUE), ]
duration_plot <- ggplot(duration_subset,
aes(x = reorder(Musician, duration_ms),
y = duration_ms, label = duration_ms)) +
xlab("Musician") + ylab("duration") +
geom_bar(stat = 'identity', aes(fill = cluster), width = .5) +
scale_fill_manual(name = "Cluster",
labels = c("Cluster 1", "Cluster 2", "Cluster 3", "Cluster 4", "Cluster 5", "Cluster 6"),
values = c("Cluster 1" = "#000066",
"Cluster 2" = "#9999CC",
"Cluster 3" = "#66CC99",
"Cluster 4" = "#FB7201",
"Cluster 5" = "#21CDFF",
"Cluster 6" = "#FF219C")) +
labs(title = "Duration Feature") + coord_flip()
duration_plot