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Giddy up, giddy it up

Wanna move into a fool’s gold room

With my pulse on the animal jewels

Of the rules that you choose to use to get loose

With the luminous moves

Bored of these limits, let me get, let me get it like

Wow!

When it comes to surreal lyrics and videos, I’m always thinking of Beck. Above, I cited the beginning of the song “Wow” from his latest album “Colors” which has received rather mixed reviews. In this post, I want to show you what I have done with Spotify’s API. We will also see how “Colors” compares to all the other albums by Beck on a range of the API’s measures. This is what we gonna do:

• Set up the R packages to access the Spotify and Last.FM APIs
• Get chart information on a specific user (in this case: me) from Last.FM
• Get information on these top artists from Spotify
• Visualize some of the information with ggplot2
• Get information on all the songs by Beck and Radiohead
• Again: Visualize this information

First, let’s set things up. If you want to download the RLastFM package (it’s not on CRAN), you can follow these instruction. If you need an account for the Spotify API, you can get one on this page. Get an account for the Last.FM API with this form.

library(spotifyr)
library(RLastFM)
library(dplyr)

# Spotify
s.id <- "< API ID >“
s.sc <- "< API Secret"

# LastFM
l.ky <- "< API Key >“
l.ap <- "http://ws.audioscrobbler.com/2.0/"

Sys.setenv(SPOTIFY_CLIENT_ID = s.id)
Sys.setenv(SPOTIFY_CLIENT_SECRET = s.sc)

access_token <- get_spotify_access_token()

I slightly adapted the user.getTopArtists() function in the RLastFM package because I wanted to use the “limit” parameter of the Last.FM API.

user.getTopArt2 <- function (username, period = NA, key = l.ky, parse = TRUE, limit = 20) {
  params = list(method = “user.gettopartists”, user = username, 
                period = period, api_key = key, limit = limit)
  params = params[!as.logical(lapply(params, is.na))]
  ret = getForm(l.ap, .params = params)
  doc = xmlParse(ret, asText = TRUE)
  if (parse) 
    doc = RLastFM:::p.user.gettopartists(doc)
  return(doc) }

Also, I adapted the function get_artist_audio_features() from the spotifyr package to override the dialog where I am required to type the name of the artist in interactive sessions.

get.art.audio.feats <- function (artist_name, access_token = get_spotify_access_token()) {
  artists <- get_artists(artist_name)
  selected_artist <- artists$artist_name[1]
  artist_uri <- artists$artist_uri[artists$artist_name == 
                                     selected_artist]
  albums <- get_albums(artist_uri)
  if (nrow(albums) > 0) {
    albums <- select(albums, -c(base_album_name, base_album, 
                                num_albums, num_base_albums, album_rank))
  }
  else {
    stop(paste0(“Cannot find any albums for \””, selected_artist, 
                “\” on Spotify”))
  }
  album_popularity <- get_album_popularity(albums)
  tracks <- get_album_tracks(albums)
  track_features <- get_track_audio_features(tracks)
  track_popularity <- get_track_popularity(tracks)
  tots <- albums %>% left_join(album_popularity, by = “album_uri”) %>% 
    left_join(tracks, by = “album_name”) %>% left_join(track_features, 
                                                       by = “track_uri”) %>% left_join(track_popularity, by = “track_uri”)
  return(tots) }

Now, we getting the relevant data. First, we are accessing the current top 20 artists (of all time) of a specific Last.FM user.

top.arts <- as.data.frame(user.getTopArt2("< user name >“, period = “overall”, limit = 20))
head(top.arts)
                     artist playcount                                 mbid rank
1                  Menomena      1601 ad386705-fb8c-40ec-94d7-e690e079e979    1
2                      Beck      1594 a8baaa41-50f1-4f63-979e-717c14979dfb    2
3                 Radiohead      1556 a74b1b7f-71a5-4011-9441-d0b5e4122711    3
4                 PJ Harvey      1546 e795e03d-b5d5-4a5f-834d-162cfb308a2c    4
5                    Prince      1501 cdc0fff7-54cf-4052-a283-319b648670fd    5
6 Nick Cave & The Bad Seeds      1243 172e1f1a-504d-4488-b053-6344ba63e6d0    6

Now, we are accessing the “artist audio features” for each one of the top 20 artists. I’m including a Sys.sleep(5) here, because I don’t want to access Spotify’s API too frequently. With 5 seconds between each artist, we should be on the safe side – I guess 2 seconds would be enough.

Please note that get.art.audio.feats() returns a tibble that contains one row for each of the artist’s tracks. That is why I have to aggregate the data by artist. Hence, the “audio features” of an artist are the mean values of all the tracks by this artist.

art.info.list <- list()
for (i in 1:nrow(top.arts)) {
  cat(i, “\n”)
  new.art.info <- get.art.audio.feats(top.arts[i, "artist"])
  new.art.info$artist <- top.arts[i, "artist"]
  art.info.list[[length(art.info.list)+1]] <- new.art.info
  Sys.sleep(5)
}
art.info <- do.call("rbind", art.info.list)

agg.art.info <- aggregate(cbind(danceability, energy, speechiness,
                                acousticness, instrumentalness, liveness,
                                valence, tempo, track_popularity) ~ artist,
                          data = art.info, FUN = mean)

Now, let’s merge Last.FM’s playcount information with the information we got from Spotify’s API. We are also sorting the dataframe by my playcount on Last.FM.

agg.art.info <- merge(agg.art.info, top.arts[,c("playcount", "rank", "artist")], by = "artist", all.x = T, all.y = F)
agg.art.info <- agg.art.info[order(agg.art.info$playcount, decreasing = T),]
saveRDS(agg.art.info, file = “agg.art.info.Rds”)


What we get is this:

                      artist danceability    energy speechiness acousticness instrumentalness  liveness
7                   Menomena    0.5397246 0.5475217  0.05414783    0.2678826       0.28483706 0.1451246
3                       Beck    0.6043865 0.6106874  0.06503006    0.2653964       0.20943021 0.1872540
13                 Radiohead    0.4095833 0.5755077  0.05504551    0.3131903       0.39465664 0.2026224
11                 PJ Harvey    0.5155636 0.4578315  0.09909212    0.4328224       0.13406858 0.1739836
12                    Prince    0.7078350 0.7295825  0.04883786    0.2464166       0.04265596 0.1405495
8  Nick Cave & The Bad Seeds    0.4147489 0.5152961  0.05414545    0.3465942       0.07602337 0.2952866
     valence    tempo track_popularity playcount rank
7  0.3084768 131.4496          5.84058      1601    1
3  0.4795632 115.6165         21.64417      1594    2
13 0.2858109 118.7648         47.62821      1556    3
11 0.4331848 118.7181         21.32727      1546    4
12 0.7672718 126.3280         25.81553      1501    5
8  0.3521593 119.5002         27.47619      1243    6

Just a few quick notes concerning the audio features given by the Spotify API (I’m citing Spotify’s webpage here):

• danceability: Danceability describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity. A value of 0.0 is least danceable and 1.0 is most danceable.
• energy: Energy is a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale. Perceptual features contributing to this attribute include dynamic range, perceived loudness, timbre, onset rate, and general entropy.
• speechiness: Speechiness detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. Values above 0.66 describe tracks that are probably made entirely of spoken words. Values between 0.33 and 0.66 describe tracks that may contain both music and speech, either in sections or layered, including such cases as rap music. Values below 0.33 most likely represent music and other non-speech-like tracks.
• acousticness: A confidence measure from 0.0 to 1.0 of whether the track is acoustic. 1.0 represents high confidence the track is acoustic.
• instrumentalness: Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context. Rap or spoken word tracks are clearly “vocal”. The closer the instrumentalness value is to 1.0, the greater likelihood the track contains no vocal content. Values above 0.5 are intended to represent instrumental tracks, but confidence is higher as the value approaches 1.0.
• liveness: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. A value above 0.8 provides strong likelihood that the track is live.
• valence: A measure from 0.0 to 1.0 describing the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry).
• tempo: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration.
• track_popularity: The popularity of a track is a value between 0 and 100, with 100 being the most popular. The popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are.
  Generally speaking, songs that are being played a lot now will have a higher popularity than songs that were played a lot in the past. Duplicate tracks (e.g. the same track from a single and an album) are rated independently. 

I am only using a few of these features for visualizations here, namely danceability, valence, and track_popularity. Let’s start with a scatter plot. ggplot2 and the really great package “ggrepel” already do a good job here.

library(ggplot2)
library(ggrepel)
library(tidyr)
library(scales)
library(RColorBrewer)


data <- readRDS("agg.art.info.Rds")
Now, I want to have a semantic profile plot for my top 5 artists. It’s a little bit of coding work before we get this.

for (var in c(“tempo”, “track_popularity”, “playcount”)) {
  data[, paste0(“norm.”, var)] <- rescale(data[, var], to = c(0, 1))
}

data.long <- gather(data, key = variable, value = value, c("danceability", "energy",
                    “speechiness”, “acousticness”, “instrumentalness”, “liveness”, “valence”))

substr(data.long$variable, 1, 1) <- toupper(substr(data.long$variable, 1, 1))
data.long$artist <- factor(data.long$artist, levels = data$artist)

ggplot(data.long[data.long$rank %in% 1:5,], aes(y = value, x = variable, group = artist, col = artist)) +
  geom_line(size = 2, lineend = “round”, alpha = .8) +
  scale_color_manual(values = brewer.pal(5, “Spectral”)) +
  scale_y_continuous(breaks = NULL) +
  labs(x = “”, y = “”, col = “Artist”) +
  coord_flip() +
  theme_dark() + theme(axis.text.y = element_text(size = 15),

                       panel.grid.major.y = element_line(size = 1))

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