Music

Some explorations of music: Taste of music critics vs the public. Most acclaimed years of music. When did rock music die?

Table of Contents

• Introduction
• Years with most acclaimed music
• Critics vs the public
• When did rock die?

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Introduction

The music preferences of critics is from Acclaimed Music, and lists of most sold music is from Tsort.

Years with most acclaimed music

imports

library(pacman)

p_load(tidyverse, magrittr, pander, naniar, glue, readxl, rap, janitor, scales, zoo)

source('../../src/extra.R', echo = F, encoding="utf-8")

read data

excel_to_tsv <- function(){
  read_excel("files/top_10000_songs_180715.xlsx") %>% 
    select(Critic_rank = "PLACE\r\n2018-JUL-15", Artist, Song, Year) %>% 
    write_tsv("files/acclaimed_songs.tsv")
  
  read_excel("files/top_3000_albums_180715.xlsx") %>% 
    select(Critic_rank = "PLACE\r\n2018-JUL-15", Artist, Album, Year) %>% 
    write_tsv("files/acclaimed_albums.tsv")  
}

artist_c <- read_tsv("files/artist_corrections.tsv")

album_c <- read_tsv("files/album_corrections.tsv")

song_c <- read_tsv("files/song_corrections.tsv")

exclude_albums = c("1", "Saturday Night Fever", "The Bodyguard", 
                   "Grease: The Original Soundtrack")

correct_songs <- function(df){
  df %>%  mutate(song = stringr::str_replace_all
                 (song, setNames(song_c$old, song_c$new))) %>% 
    mutate(song = tools::toTitleCase(as.character(song)))
}

correct_albums <- function(df){
  df %>% mutate(album = stringr::str_replace_all
                (album, setNames(album_c$old, album_c$new))) %>%
    mutate(album = tools::toTitleCase(as.character(album))) %>%
    filter(!album %in% exclude_albums)
}

correct_artists <- function(df){
    df %>% mutate(artist = stringr::str_replace_all
                  (artist, setNames(artist_c$old, artist_c$new))) %>% 
      mutate(artist = tools::toTitleCase(as.character(artist)))
}

selling_songs <- read_csv("files/selling_songs.csv") %>% clean_names() %>% 
  select(artist, song = name, public_rank = position) %>% 
  correct_artists() %>% correct_songs()

acclaimed_songs <- read_tsv("files/acclaimed_songs.tsv") %>% clean_names() %>% 
  correct_artists() %>% correct_songs()

selling_albums <- read.csv("files/selling_albums.csv") %>% clean_names() %>% 
  select(artist, album = name, year, public_rank = position) %>% 
  correct_albums() %>% correct_artists()

acclaimed_albums <- read_tsv("files/acclaimed_albums.tsv") %>% clean_names() %>% 
  correct_albums() %>% correct_artists()

genres <- read_delim(delim=";", "files/genres.csv", col_types = cols(.default = "c")) %>% clean_names()

create score

high_n <- 7000

get_rank <- function(col) {
  return(log(log(10 + high_n)) - log(log(10 + col)))
}

rankify <- function(df){
  df %>% replace_na(list(public_rank=high_n, critic_rank=high_n)) %>% 
    mutate(public_value = get_rank(public_rank),
    critic_value = get_rank(critic_rank),
    dif = critic_value - public_value,
    sum = critic_value + public_value) %>% 
    replace_with_na(list(public_rank=high_n, critic_rank=high_n))
}

songs = full_join(acclaimed_songs, selling_songs, by=c("artist", "song")) %>% rankify()

albums = full_join(acclaimed_albums, selling_albums, by=c("artist", "album")) %>% rankify()

code

albums_year <- albums %>% mutate(year = year.x) %>% group_by(year) %>% 
  summarise(acclaim_albums = sum(critic_value))

songs_year <- songs %>% group_by(year) %>% 
  summarise(acclaim_songs = sum(critic_value))

acclaim <- inner_join(albums_year, songs_year, by="year") %>% 
  mutate(acclaim = acclaim_songs + acclaim_albums) %>% filter(year > 1949)

plot_acclaim <- function(df){
  df %>% 
    mutate(decade = cut_interval(year, length=10, right=F)) %>% 
    ggplot(aes(x = year, y = acclaim, fill=decade, order=decade)) +
      geom_bar(stat="identity") +
      theme(axis.text.x = element_text(face="bold", color="#993333", size=11),
            text = element_text(size=15)) +
      guides(fill=FALSE) + 
      scale_x_continuous(
        breaks=c(1955, 1965, 1975, 1985, 1995, 2005, 2015), 
        labels=c("1950s", "1960s", "1970s", "1980s", "1990s", "2000s", "2010s")) + 
      scale_fill_manual(values=jcol) + 
    labs(y = "Critical acclaim", x = "Year")
}

Plot based on albums and singles

acclaim %>% plot_acclaim()

Plot based on albums only

albums_year %>% filter(year > 1949) %>% 
  mutate(acclaim = acclaim_albums) %>% plot_acclaim()

Critics vs the public

code

plotg <- function(df, a, b, label, col){
  df %>% 
    ggplot(aes_string(a, b, label=label, color=col)) +
    geom_abline(intercept=0, slope=1, linetype="dashed") +
    geom_text(check_overlap=T) +
    scale_colour_gradient2(low = muted("red"), high=muted("blue")) + 
    guides(color=F, size=F) +
    theme( 
      axis.title.y = element_text(margin = margin(t = 0, r = 30, b = 0, l = 0)),
      axis.title.x = element_text(margin = margin(t = 30, r = 0, b = 0, l = 0)),
      axis.text.x = element_blank(),
      axis.ticks.x = element_blank(),
      axis.text.y = element_blank(),
      axis.ticks.y = element_blank(),
      axis.title = element_text(size=20)
      ) +
    labs(x = "More liked by critics", y = "More liked by the public")
}

songs

songs %>% mutate(dif = critic_value - public_value) %>% 
  plotg("critic_value", "public_value", "song", "dif")

albums

albums %>% mutate(album_dif = critic_value - public_value) %>% 
  plotg("critic_value", "public_value", "album", "album_dif")

When did rock die?

code

genres %<>% 
  inner_join(albums, by=c("artist", "album")) %>%
  rap(isRock = logical() ~"Rock" %in% c(parent1, parent2, parent3),
      isPop = logical() ~"Pop" %in% c(parent1, parent2, parent3),
      isElectronic = logical() ~"Electronic" %in% c(parent1, parent2, parent3),
      isRB = logical() ~"R&B" %in% c(parent1, parent2, parent3),
      isFolk = logical() ~"Folk" %in% c(parent1, parent2, parent3),
      isDance = logical() ~"Dance" %in% c(parent1, parent2, parent3),
      isHiphop = logical() ~"Hip Hop" %in% c(parent1, parent2, parent3),
      isJazz = logical() ~"Jazz" %in% c(parent1, parent2, parent3)
      )

genres_critics <- genres %>% 
  group_by(year) %>% 
  summarise(
    All = sum(critic_value),
    Rock = sum(critic_value[isRock]) /All,
    Pop = sum(critic_value[isPop]) / All,
    Electronic = sum(critic_value[isElectronic]) / All,
    "R&B" = sum(critic_value[isRB]) / All,
    #Folk = sum(critic_value[isFolk]) / All,
    #Dance = sum(critic_value[isDance]) / All,
    Hiphop = sum(critic_value[isHiphop]) / All,
    Jazz = sum(critic_value[isJazz]) / All)

genres_public <- genres %>% 
  group_by(year) %>% 
  summarise(
    All = sum(public_value),
    Rock = sum(public_value[isRock]) / All,
    Pop = sum(public_value[isPop]) / All,
    Electronic = sum(public_value[isElectronic]) / All,
    "R&B" = sum(public_value[isRB]) / All,
    #Folk = sum(public_value[isFolk]) / All,
    #Dance = sum(public_value[isDance]) / All,
    Hiphop = sum(public_value[isHiphop]) / All,
    Jazz = sum(public_value[isJazz]) / All)

plot_genres <- function(df){
  df %>% select(-All) %>% 
    gather(genre, acclaim, -year) %>% 
    filter(year > 1958) %>%
    group_by(genre) %>% 
    mutate(acclaim = rollmean(acclaim, 5, na.pad=TRUE)) %>% 
    ungroup() %>% 
    drop_na() %>% 
    ggplot(aes(x=year, y=acclaim, group=genre, color=genre)) + 
      geom_line(size=1.3) +
      scale_color_manual(values=jcol) +
      scale_x_discrete(breaks = seq(1960, 2010, by = 10)) + 
      theme(axis.text.y = element_blank(),
            axis.text.x = element_text(face="bold", color="#993333", size=12),
            text = element_text(size=18),
            legend.text = element_text(size=15)) +
      labs(x = "Year")
}

critics

plot_genres(genres_critics) + ylab("Critical acclaim")

public

plot_genres(genres_public) + ylab("Public popularity")