Billboard Top 100’s in 2000

Overview

This analysis focuses on the Billboard Top 100 chart from the year 2000, particularly examining the #1 hit singles that stayed at the top position the longest. Using data manipulation and visualization techniques in R, we can gain insights into the music trends of that year and identify the most dominant songs and artists.

Libraries and Data Preparation

We start by loading the necessary R libraries for data manipulation, visualization, and analysis.

Show the code

library(tidyverse)   # For data manipulation and visualization
library(lubridate)   # For date manipulation
library(skimr)       # For summarizing data
library(survival)    # For survival analysis (not used in this example)
library(survminer)   # For visualizing survival analysis (not used in this example)
library(flextable)   # For creating flexible tables
library(DT)          # For interactive tables

The billboard dataset is reshaped to gather weekly rankings into a long format, making it easier to filter and analyze the data.

Show the code

# Load the dataset
data("billboard", package = "tidyverse") # Ensure you have the billboard dataset loaded
ds = billboard

# Reshape the data from wide to long format
ds = billboard %>% gather(key = week, value = rank, wk1:wk76)

# Convert week column to numeric and ensure rank is numeric
ds$week = as.numeric(gsub("wk", "", ds$week))
ds$rank = as.numeric(ds$rank)

Filtering and Summarizing Data

We filter the dataset to include only the rows where the song was ranked #1. Then, we group by artist and track to count the number of weeks each song stayed at the top position.

Show the code

# Filter for #1 ranked songs and summarize the duration at #1
ds = ds %>%
  filter(rank == 1) %>%
  group_by(artist, track) %>%
  summarize(weeksAtNumberOne = n()) %>%
  arrange(desc(weeksAtNumberOne))

# Display the summarized data as a flextable
as_flextable(ds)

artist	track	weeksAtNumberOne
character	character	integer
Destiny's Child	Independent Women Pa...	11
Santana	Maria, Maria	10
Aguilera, Christina	Come On Over Baby (A...	4
Madonna	Music	4
Savage Garden	I Knew I Loved You	4
Destiny's Child	Say My Name	3
Iglesias, Enrique	Be With You	3
Janet	Doesn't Really Matte...	3
Aguilera, Christina	What A Girl Wants	2
Lonestar	Amazed	2
n: 17

We use the flextable library to create a flexible and visually appealing table that displays the artists and tracks along with the number of weeks they stayed at #1.

Additional Visualizations

Show the code

# Summarize total weeks at #1 for each artist
artist_summary <- ds %>%
  group_by(artist) %>%
  summarize(totalWeeksAtNumberOne = sum(weeksAtNumberOne)) %>%
  arrange(desc(totalWeeksAtNumberOne))

# Bar chart of top artists by total weeks at #1
ggplot(artist_summary, aes(x = reorder(artist, totalWeeksAtNumberOne), y = totalWeeksAtNumberOne)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  coord_flip() +
  labs(title = "Top Artists by Total Weeks at #1 in 2000", x = "Artist", y = "Total Weeks at #1") +
  theme_minimal()

Show the code

# Histogram of weeks at #1
ggplot(ds, aes(x = weeksAtNumberOne)) +
  geom_histogram(binwidth = 1, fill = "darkorange", color = "black") +
  labs(title = "Distribution of Weeks at #1", x = "Weeks at #1", y = "Count of Songs") +
  theme_minimal()

Show the code

# Interactive data table
datatable(ds, options = list(pageLength = 10, autoWidth = TRUE),
          caption = 'Number of Weeks Each Song Stayed at #1 in 2000')

Conclusion

This analysis provides a clear view of the songs that dominated the Billboard Top 100 charts in the year 2000. By identifying the tracks that remained at #1 the longest, we can better understand the music trends and preferences of that time. This type of analysis can be expanded to include other years or additional chart metrics to gain further insights into the evolution of popular music.