Last updated: 2020-06-01
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Knit directory: requestival/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | fce3c92 | Luke Zappia | 2020-05-31 | Update searching for track ID |
| html | fce3c92 | Luke Zappia | 2020-05-31 | Update searching for track ID |
| Rmd | 3a53852 | Luke Zappia | 2020-05-31 | Add augmentation |
| html | 3a53852 | Luke Zappia | 2020-05-31 | Add augmentation |
source(here::here("code", "setup.R"))The tidy we scraped from the HTML pages is now in a nice clean and tidy format but maybe there is some other information we can add to it from other sources?
requestival <- read_tsv(
PATHS$tidied,
col_types = cols(
DateTime = col_datetime(format = ""),
Song = col_character(),
Artist = col_character(),
Release = col_character(),
IsUnearthed = col_logical(),
UnearthedURL = col_character(),
SpotifyQuery = col_character(),
SpotifyURL = col_character(),
YouTubeQuery = col_character(),
YouTubeURL = col_character()
)
) %>%
mutate(DateTime = with_tz(DateTime, "Australia/Sydney"))Chunk time: 0.04 secs
We already have search queries for Spotify so maybe we can pull some useful information from their database? Luckily they have a nice API that we can access with the {spotifyr} package. See the {spotifyr} documentation for more details about how this works.
access_token <- get_spotify_access_token(
client_id = SPOTIFY_CLIENT_ID,
client_secret = SPOTIFY_CLIENT_SECRET
)Chunk time: 0.14 secs
To get more information about each song we need track IDs which we don’t currently have. The queries we extracted from the Spotify links give use a clue about how to format the search terms but unfortunately we can’t use them directly 😿. Because they were designed for the web spaces and other characters have been replaced with codes (e.g. %20 for space) but the {spotifyr} search function prefers the standard characters. It’s not hard to construct the queries from the information we have though.
For some songs we will get multiple results. Here we try and handle that by first search for the song, artist and release. If there are any results we return the first one. If not we try a simpler search with just the song and artist, and again return the first result.
get_id <- function(Song, Artist, Release, ...) {
# Search for artist, track, album
query <- glue('artist:"{Artist}" track:"{Song}" album: "{Release}"')
results <- search_spotify(
query,
type = "track",
market = "AU",
authorization = access_token
) %>%
filter(album.release_date_precision == "day")
# Return the earliest result if there are any
if (nrow(results) > 0) {
return(results$id[1])
}
# Otherwise search for just artist and track
query <- glue('artist:"{Artist}" track:"{Song}"')
results <- search_spotify(
query,
type = "track",
market = "AU",
authorization = access_token
) %>%
filter(album.release_date_precision == "day")
# Return the first result or NA
if (nrow(results) > 0) {
return(results$id[1])
} else {
return(NA)
}
}
requestival <- requestival %>%
mutate(SpotifyID = pmap_chr(requestival, get_id)) %>%
mutate(HasSpotify = !is.na(SpotifyID))Chunk time: 2.06 mins
The track IDs are character strings that look something like this: 7tOcPDj3vyopZ404pY6UuP. For our 1187 songs we were able to find 994 IDs. These IDs aren’t very interesting by themselves but we can use them to retrieve other information from the Spotify database.
Now that we have track IDs we can get some more information about each of the Requestival songs.
tracks <- requestival %>%
filter(HasSpotify) %>%
pull(SpotifyID) %>%
map_dfr(function(.id) {
track_info <- get_track(
.id,
market = "AU",
authorization = access_token
)
tibble(
SpotifyID = track_info$id,
AlbumDate = track_info$album$release_date,
Duration = track_info$duration_ms,
Explicit = track_info$explicit,
Popularity = track_info$popularity
)
}) %>%
mutate(
Duration = Duration / 1000,
AlbumDate = ymd(AlbumDate)
)
tracksChunk time: 1.35 mins
A lot of the track information isn’t useful or is redundant with what we already have but I have picked out a few things that might be interesting: the track duration (in seconds), whether it is explicit or not and the Spotify popularity score.
We can also use the Spotify API to retrieve some information about the audio features of each song.
audio <- requestival %>%
filter(HasSpotify) %>%
pull(SpotifyID) %>%
map_dfr(function(.id) {
audio_features <- get_track_audio_features(
.id,
authorization = access_token
)
}) %>%
mutate(IsMajor = mode == 1) %>%
select(
SpotifyID = id,
IsMajor,
Loudness = loudness,
Tempo = tempo,
Valence = valence,
Energy = energy,
Danceability = danceability,
Speechiness = speechiness,
Acousticness = acousticness,
Liveness = liveness
)
audioChunk time: 1.48 mins
Detail about the audio features can be found here but in general they try to provide a numeric description of the track. Some things like loudness, tempo or whether the track is in a major key are simply calculated from the track but others such as “danceability” or “acousticness” are more abstract summaries of what the track sounds like.
Let’s join the information we have downloaded from Spotify to our data scraped from the HTML pages and save it for analysis.
requestival <- requestival %>%
left_join(tracks, by = "SpotifyID") %>%
left_join(audio, by = "SpotifyID") %>%
distinct()
write_tsv(requestival, PATHS$augmented)
requestivalChunk time: 0.48 secs
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.0 (2020-04-24)
os macOS Catalina 10.15.4
system x86_64, darwin17.0
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Berlin
date 2020-06-01
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[3] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
P ── Loaded and on-disk path mismatch.Chunk time: 0.26 secs