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create graphs of listenbrainz data
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| library(dplyr) | |
| library(ggplot2) | |
| library(jsonlite) | |
| library(lubridate) | |
| library(stringr) | |
| # load data --------------------------------------------------------------- | |
| data <- fromJSON("ercas_lb-2020-12-23.json") %>% | |
| flatten() %>% | |
| mutate( | |
| listened_at = with_tz( | |
| as_datetime(listened_at), | |
| system("readlink -f /etc/localtime | grep -o '[^/]*/[^/]*$'", intern = TRUE) | |
| ) | |
| ) %>% | |
| filter(listened_at >= as_datetime("2020-01-01")) | |
| # top artists ------------------------------------------------------------- | |
| n_artists <- 10 | |
| top_artists <- data$track_metadata.artist_name %>% | |
| table() %>% | |
| as.data.frame() %>% | |
| arrange(desc(Freq)) %>% | |
| .$. %>% | |
| head(n_artists) | |
| data %>% | |
| mutate( | |
| Artist = ifelse( | |
| #track_metadata.artist_name %in% top_artists, | |
| #track_metadata.artist_name, | |
| str_detect(track_metadata.artist_name, paste(top_artists, collapse = "|")), | |
| str_extract(track_metadata.artist_name, paste(top_artists, collapse = "|")), | |
| "All others" | |
| ) | |
| ) %>% | |
| ggplot() + | |
| aes(x = listened_at, fill = Artist) + | |
| geom_density(position = "stack", color = NA) + | |
| labs( | |
| x = "Date", | |
| y = "Density", | |
| title = sprintf("Top %d most listened-to artists over time", n_artists) | |
| ) + | |
| scale_fill_brewer(palette = "Spectral") + | |
| theme_minimal() + | |
| scale_y_continuous(breaks = NULL) | |
| # top albums -------------------------------------------------------------- | |
| n_albums <- 10 | |
| top_albums <- data$track_metadata.release_name %>% | |
| table() %>% | |
| as.data.frame() %>% | |
| arrange(desc(Freq)) %>% | |
| .$. %>% | |
| head(n_albums) | |
| data %>% | |
| mutate( | |
| Album = ifelse( | |
| track_metadata.release_name %in% top_albums, | |
| ifelse( | |
| str_starts(track_metadata.release_name, "The Idler Wheel"), | |
| "The Idler Wheel (...)", | |
| track_metadata.release_name | |
| ), | |
| "All others" | |
| ) | |
| ) %>% | |
| ggplot() + | |
| aes(x = listened_at, fill = Album) + | |
| geom_density(position = "stack", color = NA) + | |
| labs( | |
| x = "Date", | |
| y = "Density", | |
| title = sprintf("Top %d most listened-to albums over time", n_albums) | |
| ) + | |
| scale_fill_brewer(palette = "Spectral") + | |
| theme_minimal() + | |
| scale_y_continuous(breaks = NULL) | |
| # top songs --------------------------------------------------------------- | |
| n_songs <- 10 | |
| top_songs <- data$track_metadata.track_name %>% | |
| table() %>% | |
| as.data.frame() %>% | |
| arrange(desc(Freq)) %>% | |
| .$. %>% | |
| head(n_songs) | |
| data %>% | |
| mutate( | |
| Track = ifelse( | |
| track_metadata.track_name %in% top_songs, | |
| track_metadata.track_name, | |
| "All others" | |
| ) | |
| ) %>% | |
| ggplot() + | |
| aes(x = listened_at, fill = Track) + | |
| geom_density(position = "stack", color = NA) + | |
| labs( | |
| x = "Date", | |
| y = "Density", | |
| title = sprintf("Top %d most listened-to tracks over time", n_songs) | |
| ) + | |
| scale_fill_brewer(palette = "Spectral") + | |
| theme_minimal() + | |
| scale_y_continuous(breaks = NULL) | |
| # top listening times ----------------------------------------------------- | |
| data %>% | |
| transmute( | |
| week = week(listened_at), | |
| hour = hour(listened_at) | |
| ) %>% | |
| table() %>% | |
| as.data.frame() %>% | |
| ggplot() + | |
| aes(week, hour, fill = log(Freq)) + | |
| geom_tile() + | |
| scale_fill_viridis_c(na.value = "black") + | |
| labs( | |
| x = "Week", | |
| y = "Hour", | |
| title = "Most active listening times (note log scale)" | |
| ) + | |
| theme_minimal() + | |
| theme(axis.text.x = element_text(angle=90)) |
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