Let’s do the obvious thing and plot 10-year overall charts of artists and albums – NB: I’m using Last.FM’s brand color here 😉
Scrobs <- scrobsĪrtist and album chartsI have a dataframe “scrobs” with one scrobble per row now. Scrobs$timestamp <- parse_date_time(scrobs$datetext, "dmYHM") Be advised that this takes quite a long time.ĭevtools::install_github(“zappingseb/analyze_last_fm”)īefore moving on, let’s parse the timestamp and order the dataset by it. Getting scrobble dataI’m pretty sure this can be done more efficiently, but this is how I’m getting the data. That’s a complete dataset from 2009 to 2018 with exactly 65,356 “scrobbles” (which is the word Last.FM uses to describe one instance of a playback of a song). I used it to download my complete listening history from Last.FM for the last ten years. Recently, I’ve come across the analyzelastfm package by Sebastian Wolf.
Alright, seems like this is developing into a blog where I am increasingly investigating my own music listening habits.
0 kommentar(er)
