内容简介：spotifyr包很棒，让我们来探索音乐的各个方面，如节奏、舞蹈性和化合价。在这篇文章中，我们将从相同点和不同点来探讨布鲁斯·斯普林斯汀的专辑。使用get_artist_audio_features()函数很容易获得数据。在这里，我们将从csv文件中加载它并查看。我们只需要做一点清洗和删除一些非录音室的专辑。
spotifyr包很棒，让我们来探索音乐的各个方面，如节奏、舞蹈性和化合价。在这篇文章中，我们将从相同点和不同点来探讨布鲁斯·斯普林斯汀的专辑。
# devtools::install_github('charlie86/spotifyr')
library(spotifyr)
library(tidyverse)
library(magrittr)
library(ggridges)
library(ggcorrplot)
library(viridisLite)
library(factoextra)
library(ggiraphExtra)
使用get_artist_audio_features()函数很容易获得数据。在这里，我们将从csv文件中加载它并查看。
# df <- get_artist_audio_features(artist = "bruce springsteen")
df <- read_csv("https://raw.github.com/peerchristensen/Springsteen_album_clusters/master/springsteen_albums.csv")
glimpse(df)
## Observations: 537 ## Variables: 31 ## $ artist_name <chr> "Bruce Springsteen", "Bruce Springsteen... ## $ artist_uri <chr> "3eqjTLE0HfPfh78zjh6TqT", "3eqjTLE0HfPf... ## $ album_uri <chr> "0PMasrHdpaoIRuHuhHp72O", "0PMasrHdpaoI... ## $ album_name <chr> "Born In The U.S.A.", "Born In The U.S.... ## $ album_img <chr> "https://i.scdn.co/image/d002b63ceb5658... ## $ album_type <chr> "album", "album", "album", "album", "al... ## $ is_collaboration <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALS... ## $ album_release_date <chr> "1984-06-04", "1984-06-04", "1984-06-04... ## $ album_release_year <date> 1984-06-04, 1984-06-04, 1984-06-04, 19... ## $ album_popularity <dbl> 76, 76, 76, 76, 76, 76, 76, 76, 76, 76,... ## $ track_name <chr> "Born in the U.S.A.", "Cover Me", "Darl... ## $ track_uri <chr> "0dOg1ySSI7NkpAe89Zo0b9", "4U7NhC2rQTAh... ## $ track_number <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ... ## $ disc_number <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... ## $ danceability <dbl> 0.398, 0.535, 0.536, 0.429, 0.544, 0.62... ## $ energy <dbl> 0.952, 0.884, 0.982, 0.949, 0.762, 0.44... ## $ key <chr> "E", "A", "G", "C", "A#", "C#", "F", "A... ## $ loudness <dbl> -6.042, -5.499, -4.674, -5.295, -7.289,... ## $ mode <chr> "major", "minor", "major", "major", "ma... ## $ speechiness <dbl> 0.0610, 0.0407, 0.0389, 0.0458, 0.0382,... ## $ acousticness <dbl> 0.000373, 0.001880, 0.014100, 0.084200,... ## $ instrumentalness <dbl> 7.75e-05, 1.26e-03, 3.67e-05, 0.00e+00,... ## $ liveness <dbl> 0.1000, 0.1400, 0.2740, 0.1540, 0.0740,... ## $ valence <dbl> 0.584, 0.796, 0.963, 0.967, 0.473, 0.86... ## $ tempo <dbl> 122.093, 120.555, 119.201, 184.286, 120... ## $ duration_ms <dbl> 278680, 205987, 288027, 192267, 215427,... ## $ time_signature <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, ... ## $ key_mode <chr> "E major", "A minor", "G major", "C maj... ## $ track_popularity <dbl> 72, 51, 45, 47, 49, 71, 50, 47, 53, 62,... ## $ track_preview_url <chr> "https://p.scdn.co/mp3-preview/3b6a5b91... ## $ track_open_spotify_url <chr> "https://open.spotify.com/track/0dOg1yS...
我们只需要做一点清洗和删除一些非录音室的专辑。
# some albums only have one song, some are alternate versions
remove_albums <- c("Greatest Hits",
"Hammersmith Odeon, London 75",
"The Essential Bruce Springsteen (Bonus Disc)",
"The Ties That Bind: The River Collection",
"Chapter and Verse",
"The Promise",
"Tracks")
df %<>%
filter(!album_name %in% remove_albums,
!grepl("live|Live",album_name)) %>%
mutate(album_name = str_to_title(album_name))
df$album_name <- gsub(":.*","",df$album_name)
df$album_name[grepl("Innocent",df$album_name)] <- "The Wild, The Innocent.."
df$album_name[grepl("Greetings",df$album_name)] <- "Greetings"
df$album_name[grepl("Darkness",df$album_name)] <- "Darkness"
让我们先来看看Springsteen歌曲中最常用的五个键。
df %>%
select(key_mode) %>%
group_by(key_mode) %>%
count() %>%
arrange(desc(n)) %>%
ungroup() %>%
top_n(5) %>%
mutate(ordered = row_number()) %>%
ggplot(aes(x = reorder(key_mode,desc(ordered)), y = n, fill = n)) +
geom_col() +
coord_flip() +
ggtitle("Five most common keys") +
scale_fill_viridis_c(option="B", direction = -1,guide=F) +
theme_minimal() +
labs(y = "n",x = "key")
正如我们所看到的，spotifyr从spotify API获取了许多有趣的数据。让我们先来看看每张专辑的舞蹈性。“天生就会跑步”的可舞性最低，而“爱的隧道”的可舞性最高。
df %>%
group_by(album_name) %>%
ggplot(aes(x = danceability,
y = reorder(album_name,desc(album_release_year)),
fill = reorder(album_name,desc(album_release_year)))) +
geom_density_ridges(colour = "snow") +
scale_fill_viridis_d(option = "B", begin = .05, direction = -1, guide = F) +
theme_minimal() +
ggtitle("Danceability") +
labs(y="album")
让我们把所有的特征放在同一个图中。
df %>%
gather(key = feature, value = measure,
danceability, energy, loudness, valence, tempo, acousticness) %>%
group_by(album_name) %>%
ggplot(aes(x = measure,
y = reorder(album_name,desc(album_release_year)),
fill = album_release_date)) +
geom_density_ridges(rel_min_height = 0.005, legend = F, alpha = .9, size = .2, colour = "snow") +
facet_wrap(~feature, scales = "free", ncol = 2) +
scale_fill_viridis_d(option ="B" ,begin = .05) +
theme_minimal() +
theme(axis.text.y = element_text(size = 7)) +
labs(y = "album name") +
ggtitle("Springsteen albums in six features",
subtitle = "Acousticness, danceability, energy, loudness, tempo and valence") +
guides(fill = FALSE)
将各个特征之间的相关性形象化也会很有趣。energy和loudness是正相关的，而acousticness和loudness是负相关的，这不足为奇。
sign_test <- df %>%
select(acousticness,danceability,energy,loudness,tempo,valence) %>%
cor_pmat()
df %>%
select(acousticness,danceability,energy,loudness,tempo,valence) %>%
cor() %>%
ggcorrplot(type = "lower",
p.mat = sign_test,
colors = c(inferno(5)[2], "snow", inferno(5)[4])) +
ggtitle("Correlations between features",
subtitle = "Non-significant correlations marked with X")
基于这些特性，我们还可以探索专辑在距离矩阵中的相似性。在这幅图中，橙色表示专辑之间的高度差异或很大的“距离”。
dfScale <- df %>%
select(album_name,acousticness,danceability,energy,loudness,tempo,valence) %>%
group_by(album_name) %>%
summarise(acousticness = mean(scale(acousticness)),
danceability = mean(scale(danceability)),
energy = mean(scale(energy)),
loudness = mean(scale(loudness)),
tempo = mean(scale(tempo)),
valence = mean(scale(valence))) %>%
data.frame()
row.names(dfScale) <- dfScale$album_name
dfScale %<>%
select(-album_name) %>%
data.frame()
df_dist <- get_dist(dfScale, stand = TRUE)
fviz_dist(df_dist,gradient = list(low = inferno(5)[2], mid = "white", high = inferno(5)[4])) +
theme_minimal() +
ggtitle("Distance matrix",
subtitle = "Similarity between albums based on all features") +
theme(axis.text.x = element_text(hjust = 1,angle = 45),
axis.title = element_blank())
为了获得更清晰的图像，我们可以使用ggiraphExtra包中的雷达图来探索专辑和特征之间的模式。
dfScale %>%
mutate(albums = row.names(dfScale)) %>%
ggRadar(aes(group = albums),
rescale = FALSE, legend.position = "none",
size = 1, interactive = FALSE, use.label = TRUE) +
facet_wrap(~albums) +
scale_y_discrete(breaks = NULL) +
theme(axis.text.x = element_text(size = 10)) +
theme_minimal() +
theme(legend.position = "none") +
scale_fill_viridis_d(option="B") +
scale_colour_viridis_d(option="B")
最后一步，我们将了解如何使用分层和k-means聚类根据各种特征对专辑进行分组。我们首先使用factoExtra包中的fviz_nbclust()函数来计算聚类的最优数量。注意，函数中包含不同的方法来计算聚类的数量。默认情况下使用“silhouette”方法。
fviz_nbclust(dfScale, hcut) +
ggtitle("Optimal Number of Clusters: H-Clustering")
df.hc <- hclust(dist(scale(dfScale)))
fviz_dend(df.hc, k = 3,
cex = .9,
k_colors = inferno(10)[c(4,7)],
color_labels_by_k = TRUE,
rect = TRUE) +
ggtitle("Hierachical Clustering")
fviz_nbclust(dfScale, kmeans) +
ggtitle("Optimal Number of Clusters: K-means Clustering")
set.seed(324789)
km.res <- kmeans(dfScale, 2, nstart = 25)
fviz_cluster(km.res, data = dfScale,
ellipse.type = "convex",
repel = T,
palette = inferno(10)[c(4,6,8)],
ggtheme = theme_minimal(),
main = "K-means Clustering")
作者：Peer Christensen 原文链接： https://peerchristensen.netlify.com/post/clustering-springsteen-albums-with-spotifyr/
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