Billboard Top 100 Trends

Assignment 6

Author

Meredith Briggs

Intro to the Billboard Top 100

The Billboard Top 100 is a list of the top 100 most popular songs on the radio. I have recently been interested in Taylor Swift’s music trends so I wanted to look at the larger picture of music trends. After scrapping data from the past 20 weeks, I will be analyzing some trends among songs and artists.

In order to scrape my data, I used the Billboard Top 100 website (https://www.billboard.com/charts/hot-100/) and my user agent was my personal identifier. I scraped the songs, artists, position on the chart, position on the chart in the previous week, weeks on chart, the peak position on the chart, and the week of the chart.

Below are the packages I will use to manipulate the data.

Data Preparation

The first column in the data is just the number row, which does not add anything to the data so I am going to remove it.

I want to be able to use the billboard_week as an actual data with the lubridate package. This means I have to clean the data by removing the “week of” string and covert the rest of the string into a date.

Trends and Analysis

What is the trend of “Too Sweet” by Hoozier over the past 20 weeks?

Based on the line chart, Too Sweet had been decreasing in popularity until a recent spike on November 16, 2024.

How many songs have multiple artists?

# A tibble: 2 × 3
  is_mutli_artist total percentage
  <lgl>           <int>      <dbl>
1 FALSE             236       70.2
2 TRUE              100       29.8

After filtering out the individual artists and duplicate songs, I created a column that indicates if the song has a multiple artist or not. Then I used this to calculate the percent compared to all the songs on the chart.

The final percent shows that 29.76% of the unique songs on the Billboard Top 100 for the past 20 weeks has multiple artist on the song.

What 10 songs have been on the chart the longest?

# A tibble: 10 × 3
   billboard_song        billboard_artist                     billboard_weeks_on
   <chr>                 <chr>                                             <dbl>
 1 Lose Control          "Teddy Swims"                                        65
 2 Stick Season          "Noah Kahan"                                         52
 3 I Remember Everything "Zach Bryan Featuring Kacey Musgrav…                 52
 4 Cowgirls              "Morgan Wallen Featuring ERNEST"                     47
 5 Beautiful Things      "Benson Boone"                                       42
 6 Greedy                "Tate McRae"                                         42
 7 Monster Mash          "Bobby \"Boris\" Pickett And The Cr…                 41
 8 Lovin On Me           "Jack Harlow"                                        38
 9 Feather               "Sabrina Carpenter"                                  37
10 Austin                "Dasha"                                              35

I was surprised that Lose Control by Teddy Swims had 65 weeks on the chart, which is longer than a year. Especially because I listen to the radio every morning and afternoon on my commute and I do not know this song.

What are the trends of Chappell Roan’s songs?

First I filtered to only have Chappell as an artist. Then I grouped by song becasue every song appeared more than once over all the weeks. . Finally I created a line chart to show the trend of each song over the past 20 weeks.

Based on these trends, there was a peak in many songs for Chappell in late August on the charts, but overall, Good Luck Babe has stayed a top song on the charts.

Which artist has the most songs on the chart?

First, I filtered for distinct songs because I did want the same song to contribute to the artist over and over again. Then I summarized to create a count of songs for each artist.

Based on this bar chart, Future and Rod Wave have the most songs on the chart over the past 20 weeks. This makes sense because they have both recently released new albums so their new songs were probably on the charts for a short time rather than a long time.

--- title: "Billboard Top 100 Trends" subtitle: "Assignment 6" author: "Meredith Briggs" format: html: # Options related to HTML output. code-tools: TRUE # Allow the code tools option showing in the output. embed-resources: TRUE # Embeds all components into a single HTML file. execute: # Options related to the execution of code chunks. warning: FALSE # FALSE: Code chunk sarnings are hidden by default. message: FALSE # FALSE: Code chunk messages are hidden by default. echo: FALSE # TRUE: Show all code in the output. editor: visual --- ## Intro to the Billboard Top 100 The Billboard Top 100 is a list of the top 100 most popular songs on the radio. I have recently been interested in Taylor Swift's music trends so I wanted to look at the larger picture of music trends. After scrapping data from the past 20 weeks, I will be analyzing some trends among songs and artists. In order to scrape my data, I used the Billboard Top 100 website (<https://www.billboard.com/charts/hot-100/>) and my user agent was my personal identifier. I scraped the songs, artists, position on the chart, position on the chart in the previous week, weeks on chart, the peak position on the chart, and the week of the chart. Below are the packages I will use to manipulate the data. ```{r} #| label: setup #| include: TRUE library(tidyverse) # For all the tidy things library(lubridate) # For dealing with dates in a polite way library(skimr) # For reviewing data easily library(dplyr) # For easy manipulation of data library(knitr) # Useful tools when 'knitting' (rendering) Quarto documents. ``` ```{r} #| label: loading billboard data #| include: FALSE #| echo: FALSE billboard_top_100<- read_csv("https://myxavier-my.sharepoint.com/:x:/g/personal/briggsm4_xavier_edu/EWhbRlpc_l9Ar_mYGq7Vwx8BjI53pBufY0COzWMI8kh4JA?download=1") ``` ## Data Preparation The first column in the data is just the number row, which does not add anything to the data so I am going to remove it. ```{r} #| label: remove first column #| include: FALSE #| echo: FALSE billboard_top_100 <- subset(billboard_top_100, select = -c(1)) ``` I want to be able to use the billboard_week as an actual data with the lubridate package. This means I have to clean the data by removing the "week of" string and covert the rest of the string into a date. ```{r} #| label: create usable date #| include: FALSE #| echo: FALSE billboard_top_100$billboard_week<- mdy(str_extract(billboard_top_100$billboard_week, "\\w+ \\d{1,2}, \\d{4}")) ``` ## Trends and Analysis ### What is the trend of "Too Sweet" by Hoozier over the past 20 weeks? ```{r} #| label: Trend of too sweet position on chart #| include: TRUE billboard_top_100 %>% filter(billboard_song=="Too Sweet") %>% ggplot(aes(x=billboard_week,y=billboard_position))+ geom_line()+ geom_point()+ scale_y_reverse() + labs(title="Trend of Too Sweet by Hoozier") ``` Based on the line chart, Too Sweet had been decreasing in popularity until a recent spike on November 16, 2024. ### How many songs have multiple artists? ```{r} billboard_top_100 %>% distinct(billboard_song, billboard_artist) %>% mutate(is_mutli_artist = str_detect( billboard_artist,"&|and|Featuring")) %>% group_by(is_mutli_artist) %>% summarize(total=n()) %>% ungroup() %>% mutate(percentage = (total / sum(total)) * 100) ``` After filtering out the individual artists and duplicate songs, I created a column that indicates if the song has a multiple artist or not. Then I used this to calculate the percent compared to all the songs on the chart. The final percent shows that 29.76% of the unique songs on the Billboard Top 100 for the past 20 weeks has multiple artist on the song. ### What 10 songs have been on the chart the longest? ```{r} #| label: longest on chart billboard_top_100 %>% arrange(desc(billboard_weeks_on)) %>% distinct(billboard_song, billboard_artist, .keep_all = TRUE) %>% select(billboard_song, billboard_artist, billboard_weeks_on) %>% slice_head(n = 10) ``` I was surprised that *Lose Control* by Teddy Swims had 65 weeks on the chart, which is longer than a year. Especially because I listen to the radio every morning and afternoon on my commute and I do not know this song. ### What are the trends of Chappell Roan's songs? ```{r} #| label: trends of Chappell Roan's songs billboard_top_100 %>% filter(billboard_artist=="Chappell Roan") %>% group_by(billboard_song) %>% ggplot(aes(x=billboard_week,y=billboard_position, color=billboard_song))+ geom_line()+ geom_point()+ scale_y_reverse() + labs(title="Trend of Chappell Roan on the Top 100") ``` First I filtered to only have Chappell as an artist. Then I grouped by song becasue every song appeared more than once over all the weeks. . Finally I created a line chart to show the trend of each song over the past 20 weeks. Based on these trends, there was a peak in many songs for Chappell in late August on the charts, but overall, *Good Luck Babe* has stayed a top song on the charts. ### Which artist has the most songs on the chart? ```{r} #| label: most song by artist on chart billboard_top_100 %>% distinct(billboard_song, billboard_artist) %>% group_by(billboard_artist) %>% summarise(num_songs = n(), .groups = "drop") %>% slice_max(num_songs, n = 10) %>% ggplot(aes(x=reorder(billboard_artist, -num_songs), y=num_songs)) + geom_bar(stat = "identity", fill = "blue") + labs(title = "Number of Songs per Artist", x="Artist", y="Number of Songs") + theme(axis.text.x = element_text(angle = 45, hjust = 1)) ``` First, I filtered for distinct songs because I did want the same song to contribute to the artist over and over again. Then I summarized to create a count of songs for each artist. Based on this bar chart, Future and Rod Wave have the most songs on the chart over the past 20 weeks. This makes sense because they have both recently released new albums so their new songs were probably on the charts for a short time rather than a long time.