Consider the data in billboard.csv containing every song to appear on the weekly Billboard Top 100 chart since 1958, up through the middle of 2021. Each row of this data corresponds to a single song in a single week. For our purposes, the relevant columns here are:
performer: who performed the song
song: the title of the song
year: year (1958 to 2021)
week: chart week of that year (1, 2, etc)
week_position: what position that song occupied that week on the Billboard top 100 chart.
Use your skills in data wrangling and plotting to answer the following three questions.
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)Part A: Make a table of the top 10 most popular songs since 1958, as measured by the total number of weeks that a song spent on the Billboard Top 100. Note that these data end in week 22 of 2021, so the most popular songs of 2021 will not have up-to-the-minute data; please send our apologies to The Weeknd.
Your table should have 10 rows and 3 columns: performer, song, and count, where count represents the number of weeks that song appeared in the Billboard Top 100. Make sure the entries are sorted in descending order of the count variable, so that the more popular songs appear at the top of the table. Give your table a short caption describing what is shown in the table.
(Note: you’ll want to use both performer and song in any group_by operations, to account for the fact that multiple unique songs can share the same title.)
Solution:
billboard = read.csv("billboard.csv")
billboard$X = NULL
relevantColumns = c("performer", "song", "year", "week", "week_position")
billboardRelevant = billboard[relevantColumns]
billboardTop = billboardRelevant %>%
filter(week_position <= 100, year >= 1958) %>%
group_by(performer, song) %>%
summarise(count = n(), .groups = 'drop') %>%
arrange(desc(count))
tenthValue = unique(billboardTop$count)[10]
billboardTop10 = billboardTop %>%
filter(count >= tenthValue)
print(billboardTop10)## # A tibble: 17 x 3
## performer song count
## <chr> <chr> <int>
## 1 Imagine Dragons Radioactive 87
## 2 AWOLNATION Sail 79
## 3 Jason Mraz I'm Yours 76
## 4 The Weeknd Blinding Lights 76
## 5 LeAnn Rimes How Do I Live 69
## 6 LMFAO Featuring Lauren Bennett & GoonRock Party Rock Anthem 68
## 7 OneRepublic Counting Stars 68
## 8 Adele Rolling In The Deep 65
## 9 Jewel Foolish Games/You Were Meant~ 65
## 10 Carrie Underwood Before He Cheats 64
## 11 Gabby Barrett Featuring Charlie Puth I Hope 62
## 12 Lifehouse You And Me 62
## 13 The Lumineers Ho Hey 62
## 14 Imagine Dragons Demons 61
## 15 Post Malone Circles 61
## 16 Lady Antebellum Need You Now 60
## 17 Los Del Rio Macarena (Bayside Boys Mix) 60
Part B: Is the “musical diversity” of the Billboard Top 100 changing over time? Let’s find out. We’ll measure the musical diversity of given year as the number of unique songs that appeared in the Billboard Top 100 that year. Make a line graph that plots this measure of musical diversity over the years. The x axis should show the year, while the y axis should show the number of unique songs appearing at any position on the Billboard Top 100 chart in any week that year. For this part, please filter the data set so that it excludes the years 1958 and 2021, since we do not have complete data on either of those years. Give the figure an informative caption in which you explain what is shown in the figure and comment on any interesting trends you see.
There are number of ways to accomplish the data wrangling here. For example, you could use two distinct sets of data-wrangling steps. The first set of steps would get you a table that counts the number of times that a given song appears on the Top 100 in a given year. The second set of steps operate on the result of the first set of steps; it would count the number of unique songs that appeared on the Top 100 in each year, irrespective of how many times it had appeared.
Solution:
#b.
billboardTop100AnyTimePerYear = billboardRelevant %>%
filter(week_position <= 100)
billboardTop100AnyTimePerYearCount = billboardTop100AnyTimePerYear %>%
group_by(year) %>%
summarise(countOfSongs = n_distinct(performer, song))
ggplot(data = billboardTop100AnyTimePerYearCount[billboardTop100AnyTimePerYearCount$year > 1958
& billboardTop100AnyTimePerYearCount$year < 2021, ]) +
geom_line(aes(x = year, y = countOfSongs)) +
scale_x_continuous(breaks = seq(1959, 2020, 2), guide = guide_axis(angle = 45)) +
xlab(label = "Year") +
ylab(label = "No of unique songs") +
labs(title = "Diversity of songs",
subtitle = "# unique songs across years on Billboard Top 100",
caption = "Observation: Maximum number of unique songs in the Billboard top 100 peaked in 1966 and
its trend has continually declined until 2001 where it has its trough.
Since then, the trend started to grow back up, reaching its maximum at the latest year considered in analysis.") +
theme(plot.caption = element_text(hjust = 0.5))
Part C: Let’s define a “ten-week hit” as a single song that appeared on the Billboard Top 100 for at least ten weeks. There are 19 artists in U.S. musical history since 1958 who have had at least 30 songs that were “ten-week hits.” Make a bar plot for these 19 artists, showing how many ten-week hits each one had in their musical career. Give the plot an informative caption in which you explain what is shown.
Solution:
tenWeekHit = billboardRelevant %>%
filter(week_position <= 100) %>%
group_by(performer, song) %>%
summarise(songsWeeklyCount = n()) %>%
filter(songsWeeklyCount >= 10) %>%
group_by(performer) %>%
summarise(songsCount = n()) %>%
filter(songsCount >= 30)## `summarise()` has grouped output by 'performer'. You can override using the
## `.groups` argument.
ggplot(data = tenWeekHit) +
geom_col(aes(x = reorder(performer, songsCount), y = songsCount)) +
coord_flip() +
scale_y_continuous(breaks = seq(0, 60, 2)) +
xlab(label = "Performers") +
ylab(label = "Number of Songs") +
labs(title = "Ten week hit",
subtitle = "Performers with at least 30 songs, each featured for at least 10 weeks",
caption = "Elton John leads the list comfortably by 8 songs more than Madonna.
The list is packed tightly from rank 2.") +
theme(plot.caption = element_text(hjust = 0.5))