1 Executive Summary

In recent years, the trend of artists collaborating on songs has become increasingly prevalent. This analysis examines whether this shift is reflected in the data, specifically focusing on the Billboard Hot 100 charts. By parsing and analyzing data from 1958 to 2021, this study investigates the frequency and impact of collaborations versus solo performances among top-charting artists.

The analysis reveals a significant rise in the number of collaborative tracks over time, with a notable decrease in songs released by solo artists. Moreover, the study explores the relationship between the number of collaborators and the success of a song, finding that increased collaborations are associated with higher peak rankings and longer durations on the Billboard charts.

This study underscores the growing importance of collaborations in achieving commercial success in the music industry, as evidenced by the trends observed on the Billboard Hot 100 charts.

2 Background

  • There has been a noticeable increase in artists releasing songs in collaboration with other artists. This project explores whether this trend is reflected in the data: Are Top 100 artists more likely to perform solo or collaborate?

  • Data Description:

    • The Billboard Hot 100 is the United States’ music industry standard record chart, published weekly by Billboard magazine. Chart rankings are determined by a combination of sales, radio play, and online streaming within the United States.

    • Each week, Billboard releases “The Hot 100” chart, listing the songs that were trending based on sales and airplay for that week. This dataset compiles all “The Hot 100” charts released since the chart’s inception in 1958.

    • The data for this analysis was extracted from here.

3 Data Cleaning & Exploratory Data Analysis

  • Determine how collaborations or featuring artists are indicated in the artist name.

  • During exploratory analysis, collaborations or featuring artists were indicated in various formats, including:

    • A Featuring B | A Featuring B & C | A Featuring B, C & D | A Featuring B & C & D |A Featuring A Or B
    • A Feat. B | A Feat. B & C
    • A & B | A , B & C
    • A With B | A With B & C
    • A X B X C | A X B | A x B

  • Maximum Number of Featured Artists: The song with the most featured artists is “Costa Rica” by Dreamville, featuring 9 additional artists (2019).

  • The dataset records Billboard Top 100 hits from 1958-08-04 to 2021-11-06.

  • Caveats:

    1. Vocal Groups and Bands in the 1950s and 1960s: Many vocal groups and bands were named in a format like “A and B” or “A & B” (e.g., James Brown And The Famous Flames; Wade Flemons and the Newcomers; Robert & Johnny). These should be considered as a single artist rather than a collaboration.

    2. Artists Requiring Special Consideration: Lil Nas X & B; Silk Sonic (Bruno Mars & Anderson .Paak)

4 How the Collaboration Trend Has Evolved Over Time

5 Does Solo vs. Collaborative Work Predict a Song’s Popularity on the Billboard Charts?

# summary(lm(peak.rank~artist_number,data=bb_df_cleaned))

ggplot(data=bb_df_cleaned ,aes(x=artist_number,y=peak.rank))+
  geom_smooth(method=lm,colour="#66CC00",size=2,se=TRUE) + 
  scale_x_discrete(limits=c(seq(1,6,1))) +
  scale_y_discrete(limits=c(seq(0,100,10))) + theme_bw()+
  labs(x="Number of Collaborators", y="Peak Rank", title="Increased Collaborations Predict a Higher Peak Rank on the Billboard Charts") +
  theme(plot.title = element_text(hjust = 0.5,size=15))

# geom_point(position=position_jitter(seed=1,width=0.4),colour="#006600",alpha=0.1,size=0.5,shape=3)+
# summary(lm(weeks.on.board~artist_number,data=bb_df_cleaned))

ggplot(data=bb_df_cleaned ,aes(x=artist_number,y=weeks.on.board))+
  geom_smooth(method=lm,colour="#E224D5",size=2,se=TRUE) + 
  scale_x_discrete(limits=c(seq(1,6,1))) +
  scale_y_discrete(limits=c(seq(0,100,10)))+ ylim(c(0,20))+theme_bw()+
  labs(x="Number of Collaborators", y="Number of Weeks on Billboard Top 100", title="More Collaborations Predict a Longer Duration on the Billboard Charts") +
  theme(plot.title = element_text(hjust = 0.5,size=15))

#  geom_point(colour="#4C0099",alpha=0.1,size=0.5,shape=3)+

6 Who’s Topping the Charts?

# length(unique(bb_df_cleaned$artist1)) 7507 artists have hit Billboard Top100 as a main artist

temp=bb_df_cleaned %>% group_by(artist1) %>% summarise(Top100Hits=n()) %>% 
  arrange(desc(Top100Hits))%>% 
  rename(Artist=artist1) 

formattable(head(temp,30) , list(`Top100Hits`=color_bar(color="lightblue")))
Artist Top100Hits
Drake 1494
Taylor Swift 1131
Elvis Presley 988
Elton John 941
Rihanna 916
Kenny Chesney 905
Madonna 885
Chris Brown 870
Tim McGraw 828
Maroon 5 757
Mariah Carey 734
Usher 723
Keith Urban 717
Beyonce 704
Rod Stewart 682
Michael Jackson 665
Stevie Wonder 665
Jason Aldean 656
Whitney Houston 654
R. Kelly 645
Kanye West 634
Rascal Flatts 627
P!nk 620
Brad Paisley 618
Blake Shelton 611
The Beatles 608
Chicago 607
Aretha Franklin 604
Diana Ross 597
The Weeknd 596