Data & Vinyls — A discogs library exploration with R

5 minute read

As a vinyl lover and data addict, I had some fun making requests on the Discogs API with R, in order to know better what is inside my library.

Every vinyl lover knows about Discogs. But did you know you could easily access the API? Here are the lines of code I used to access my library.

Note : You can download the data used here in JSON, or directly in R :

collection_complete <- jsonlite::fromJSON(txt = "http://colinfay.me/data/collection_complete.json", simplifyDataFrame = TRUE)

Major Tom to Discogs API

Before starting, I’ll need these two functions: _ %>% and ` %||%`._

library(magrittr) #for %>%
`%||%` <- function(a,b) if(is.null(a)) b else a

Let’s first get my Discogs profile:

user <- "_colin"
content <- httr::GET(paste0("https://api.discogs.com/users/", user, "/collection/folders"))
content <- rjson::fromJSON(rawToChar(content$content))$folders
content
## [[1]]
## [[1]]$count
## [1] 308
## 
## [[1]]$resource_url
## [1] "https://api.discogs.com/users/_colin/collection/folders/0"
## 
## [[1]]$id
## [1] 0
## 
## [[1]]$name
## [1] "All"

This first request brings in the environment all the information about a profil (here “_colin”, aka : me).

$count
``` tells us the number of entries in the library : 308.  The ```r 
$id
``` element is the number of “folders” created by the user – 0 corresponding to the whole collection, without any list specification.
### Create a dataframe with all the vinyls
The Discogs API sends back pages with 100 max results. Here, my collection has 308, so I'll use a ```r 
repeat
``` loop to query all the data, and store them in a dataframe.
```r 
collec_url <- httr::GET(paste0("https://api.discogs.com/users/", user, "/collection/folders/", content[[1]]$id, "/releases?page=1&amp;per_page=100"))
if (collec_url$status_code == 200){
  collec <- rjson::fromJSON(rawToChar(collec_url$content))
  collecdata <- collec$releases
    if(!is.null(collec$pagination$urls$`next`)){
      repeat{
        url <- httr::GET(collec$pagination$urls$`next`)
        collec <- rjson::fromJSON(rawToChar(url$content))
        collecdata <- c(collecdata, collec$releases)
        if(is.null(collec$pagination$urls$`next`)){
          break
        }
      }
    }
}
  collection <- lapply(collecdata, function(obj){
    data.frame(release_id = obj$basic_information$id %||% NA,
               label = obj$basic_information$labels[[1]]$name %||% NA,
               year = obj$basic_information$year %||% NA,
               title = obj$basic_information$title %||% NA, 
               artist_name = obj$basic_information$artists[[1]]$name %||% NA,
               artist_id = obj$basic_information$artists[[1]]$id %||% NA,
               artist_resource_url = obj$basic_information$artists[[1]]$resource_url %||% NA, 
               format = obj$basic_information$formats[[1]]$name %||% NA,
               resource_url = obj$basic_information$resource_url %||% NA)
  }) %>% do.call(rbind, .) %>% 
    unique()

Here is what the dataframe looks like:

library(pander)
pander(head(collection))
Table continues below
release_id label year title
5181773 A&M Records 1982 Night And Day
3690646 A&M Records (2) 2012 God Save The Queen
944917 Alexi Delano Limited 2007 The Acid Sessions Vol. 4
906983 Alphabet City 2007 Urban Minds / Skattered
8112758 Amerilys 1986 Follement Vôtre
5800664 Anette Records 2014 And The Dead Shall Lie There
Table continues below
artist_name artist_id artist_resource_url format
Joe Jackson 75280 https://api.discogs.com/artists/75280 Vinyl
Sex Pistols 31753 https://api.discogs.com/artists/31753 Vinyl
Alexi Delano 26 https://api.discogs.com/artists/26 Vinyl
Pacjam 488187 https://api.discogs.com/artists/488187 Vinyl
Diane Dufresne 647100 https://api.discogs.com/artists/647100 Vinyl
Ancient Mith 302464 https://api.discogs.com/artists/302464 Vinyl
resource_url
https://api.discogs.com/releases/5181773
https://api.discogs.com/releases/3690646
https://api.discogs.com/releases/944917
https://api.discogs.com/releases/906983
https://api.discogs.com/releases/8112758
https://api.discogs.com/releases/5800664

I can’t see, I can’t see I’m going blind

And now, let’s start visualising!

Most frequent labels

library(ggplot2)
ggplot(as.data.frame(head(sort(table(collection$label), decreasing = TRUE), 10)), aes(x = reorder(Var1, Freq), y = Freq)) + 
  geom_bar(stat = "identity", fill = "#B79477") + 
  coord_flip() + 
  xlab("Label") +
  ylab("Fréquence") +
  ggtitle("Labels les plus fréquents")

labels les plus représentés dans la collection discogs

Philips and Polydor, what a surprise!

Most frequent artists

ggplot(as.data.frame(head(sort(table(collection$artist_name), decreasing = TRUE), 10)), aes(x = reorder(Var1, Freq), y = Freq)) + 
  geom_bar(stat = "identity", fill = "#B79477") + 
  coord_flip() + 
  xlab("Artistes") +
  ylab("Fréquence") +
  ggtitle("Artistes les plus fréquents")

Artistes les plus représentés

So… here’s the big revelation: I love Serge Gainsbourg and Geogres Brassens (guilty pleasure).

Release date

ggplot(dplyr::filter(collection, year != 0), aes(x = year)) + 
  geom_bar(stat = "count", fill = "#B79477") + 
  xlab("Année de sortie") +
  ylab("Fréquence") +
  ggtitle("Date de sorties des vinyles de la collection")

Dates de sorties

Looks like I’m not a big 90’s fan! My library show a bimodal distribution, with one mode around the 80’s, and one around 2005.

It’s time to go deeper

So, can we get more information about this library?

Hello, it’s me again

_Note: between the writing of this blogpost and now, Discogs seems to have put a rate limit on its API. For the creation of collection_2, you should consider using Sys.sleep(). More on that here.

collection_2 <- lapply(as.list(collection$release_id), function(obj){
  url <- httr::GET(paste0("https://api.discogs.com/releases/", obj))
  url <- rjson::fromJSON(rawToChar(url$content))
  data.frame(release_id = obj, 
             label = url$label[[1]]$name %||% NA,
             year = url$year %||% NA, 
             title = url$title %||% NA, 
             artist_name = url$artist[[1]]$name %||% NA, 
             styles = url$styles[[1]] %||% NA,
             genre = url$genre[[1]] %||% NA,
             average_note = url$community$rating$average %||% NA, 
             votes = url$community$rating$count %||% NA, 
             want = url$community$want %||% NA, 
             have = url$community$have %||% NA, 
             lowest_price = url$lowest_price %||% NA, 
             country = url$country %||% NA)
}) %>% do.call(rbind, .) %>% 
  unique()

Here, I have used the release_id element to make a request about each vinyl.

Most frequent genre

ggplot(as.data.frame(head(sort(table(collection_2$genre), decreasing = TRUE), 10)), aes(x = reorder(Var1, Freq), y = Freq)) + 
  geom_bar(stat = "identity", fill = "#B79477") + 
  coord_flip() + 
  xlab("Genre") +
  ylab("Fréquence") +
  ggtitle("Genres les plus fréquents")

Genres les plus fréquentsOH GOD, what a surprise! Almost half of my collection is made of rock albums (who could have guessed?).

Countries

ggplot(as.data.frame(head(sort(table(collection_2$country), decreasing = TRUE), 10)), aes(x = reorder(Var1, Freq), y = Freq)) + 
  geom_bar(stat = "identity", fill = "#B79477") + 
  coord_flip() + 
  xlab("Pays d'origine") +
  ylab("Fréquence") +
  ggtitle("Pays les plus fréquents")

Pays d'origine des vinyles

Average note

ggplot(collection_2, aes(x = average_note)) + 
  geom_histogram(fill = "#B79477") +
  xlab("Note moyenne") +
  ylab("Fréquence") +
  ggtitle("Notes moyennes des vinyles de la collection")

Thanks a lot Discogs! It looks like I’ve got quite good musical tastes (thanks for the ego boost :) !)

Prices of vinyls (low range)

ggplot(collection_2, aes(x = lowest_price)) + 
  geom_histogram(fill = "#B79477") +
  xlab("Prix le plus bas") +
  ylab("Fréquence") +
  ggtitle("Prix le plus bas des vinyles de la collection")

densite-prix-bas

Ok, I’m not gonna be rich selling my vinyl collection…

Let’s finish!

collection_complete <- merge(collection, collection_2, by = c("release_id","label", "year", "title", "artist_name"))

Relationship between price and “want”

lm_want <- lm(formula = lowest_price ~ want, data = collection_complete)
summary(lm_want)
##Residuals:
##   Min     1Q Median     3Q    Max 
##-8.043 -4.628 -2.224  2.179 49.608 

##Coefficients:
##            Estimate Std. Error t value Pr(>|t|)    
##(Intercept) 6.715418   0.450582  14.904  &lt; 2e-16 ***
##want        0.005004   0.001788   2.799  0.00546 ** 
##---
##Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

##Residual standard error: 7.306 on 301 degrees of freedom
##  (5 observations deleted due to missingness)
##Multiple R-squared:  0.02536,    Adjusted R-squared:  0.02213 
##F-statistic: 7.833 on 1 and 301 DF,  p-value: 0.005461

Here, we can see a correlation between the price and the number of users that put a “want” on a particular vinyl.

ggplot(collection_complete, aes(x = lowest_price, y = want)) + geom_point(size = 3, color = "#B79477") + geom_smooth(method = "lm") + xlab("Prix le plus bas") + ylab("Nombre de \"want\"") + ggtitle("Prix et \"want\" des vinyles de la collection")

Prix en fonction du nombre de want

Price and average note

lm_note <- lm(formula = lowest_price ~ average_note, data = collection_complete)
lm_note$coefficients
##  (Intercept) average_note 
##    -1.504767     2.207834

Here, no significative correlation.

ggplot(collection_complete, aes(x = lowest_price, y = average_note)) + 
  geom_point(size = 3, color = "#B79477") +
  xlab("Prix le plus bas") +
  ylab("Note moyenne") +
  ylim(c(0,5)) +
  ggtitle("Prix et notes des vinyles de la collection")

Prix en fonction des notes

And to conclude…

Next step… create a package to access the Discogs API? Why not! Let’s put this on my to-do…

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