BAN 350 Assignment 1: Gestalt Principles in Music Data

Author

Zachary Bechard

Introduction

The Rolling Stones Top 500 Albums dataset shows the ranking of albums from various genres that span from Frank Sinatra’s “In the Wee Small Hours” in 1955 to The Beach Boys 2011 “The Smile Sessions.” This analysis focuses on the Rock genre, revealing the distribution of Rock artists over time by decade, and then analyzing the number of albums by each Rock artist. The goal of this analysis is to help visualize how Rock music has evolved through the decades, listing key artists of the genre as well.

First Categorical Analysis

Visualization:

Code

library(tidyverse)

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Code

library(ggplot2)
top_albums <- read_csv(
  "https://jsuleiman.com/datasets/Rolling_Stones_Top_500_Albums.csv",
    locale = locale(encoding = "ISO-8859-2", asciify = TRUE))
summary(top_albums)

     Number           Year         Album              Artist         
 Min.   :  1.0   Min.   :1955   Length:500         Length:500        
 1st Qu.:125.8   1st Qu.:1970   Class :character   Class :character  
 Median :250.5   Median :1976   Mode  :character   Mode  :character  
 Mean   :250.5   Mean   :1979                                        
 3rd Qu.:375.2   3rd Qu.:1988                                        
 Max.   :500.0   Max.   :2011                                        
    Genre             Subgenre        
 Length:500         Length:500        
 Class :character   Class :character  
 Mode  :character   Mode  :character

Code

rock_albums <- top_albums |> 
  filter(str_detect(tolower(Genre), "rock")) |> 
  mutate(decade = floor(Year / 10) * 10)
rock_by_decade <- rock_albums |> count(decade)

ggplot(rock_by_decade, aes(x = as.factor(decade), y = n)) +
  geom_bar(stat = "identity", fill = "seagreen") +
  labs(title = "Number of Rock Artists by Decade", x = "Decade", y = "Number of Artists") +
  theme_minimal()

Analysis and Reflection:

The bar chart above reveals that Rock artists had a major presence in the 1960s, 1970s, and 1980s, with the genre peaking in the 1970s. The trend shows a decline in later decades, granting insight on changing musical trends and diversification.

I chose to use a bar chart for its simplicity in comparing populations for each decade. I selected a color that easily stands out from the white background, making trends easy to interpret. The Gestalt principle or this chart could be Continuity as the x-axis is in logical chronological order.

My visualization effectively communicates the story of how popular Rock albums are through the years. I would say that the strengths of this visualization are in the clear depiction of trends, but labels or annotations could help decipher the chart more.

Second Categorical Analysis

Visualization:

Code

rock_artist_count <- rock_albums |> count(Artist)

ggplot(rock_artist_count, aes(x = reorder(Artist, n), y = n)) +
  geom_bar(stat = "identity", fill = "lightslateblue") +
  coord_flip() +
  labs(title = "Number of Albums per Rock Artist", x = "Artist", y = "Number of Albums") +
  theme_minimal()

Analysis and Reflection:

The second visualization highlights certain artists that have contributed numerous albums to the genre such as The Beatles, The Rolling Stones, or Bruce Springsteen.

I chose a horizontal bar chart for this visualization type because artist names are quite long, and words are read left to right… Again, the color I chose shows a clear distinction from the chart’s background, following the Gestalt principle Figure-Ground.

This chart clearly communicates the artists who dominated the genre, however, the names are quite crowded. This could be positively altered by showing artists who produced 2 or more albums. This way, more than half of the artists will be gone from the list. To show the full 500 names though, I can’t find a way to de-clutter the names.

Conclusion

The two visualizations shown above successfully illustrate the historical evolution of Rock music over the decades, revealing the top artists for better understanding. The first visualization shows the genre’s presence in music over time. while the second adds the whole list of artists and the number of albums they each created for reference.

This analysis could be extended to explore the cultural factors behind trends shown in the charts. One could also compare the Rock genre to other genres from the dataset to see which genre was the most popular for each decade.