Homework 5 Key

knitr::opts_chunk$set(comment = ">")
library(tidyverse)
library(ggrepel)
library(ggthemes)

Answer 2

Read in the data, clean up the names, and pivot it in a way so the first few rows look like this:

billboard <- read_csv("billboard_top100.csv") 
billboard_tidy <- billboard |> 
  pivot_longer(starts_with("wk"), 
               names_to = "week", 
               values_to = "rank", 
               values_drop_na = TRUE, 
               names_prefix = "wk", 
               names_transform = list(week = as.integer)) |> 
  janitor::clean_names() 
billboard_tidy

> # A tibble: 5,307 × 6
>    artist  track                   time   date_entered  week  rank
>    <chr>   <chr>                   <time> <date>       <int> <dbl>
>  1 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       1    87
>  2 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       2    82
>  3 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       3    72
>  4 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       4    77
>  5 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       5    87
>  6 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       6    94
>  7 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       7    99
>  8 2Ge+her The Hardest Part Of ... 03:15  2000-09-02       1    91
>  9 2Ge+her The Hardest Part Of ... 03:15  2000-09-02       2    87
> 10 2Ge+her The Hardest Part Of ... 03:15  2000-09-02       3    92
> # ℹ 5,297 more rows

Answer 3

Create a variable named date that corresponds to the week based on the date_entered.

billboard_tidy_date <- billboard_tidy |> 
  mutate(date = if_else(week == 1,
                       date_entered,
                       date_entered + weeks(x = week - 1)))
billboard_tidy_date

1

This if_else basically says, “if week is equal to 1 then assign the date_entered value to date. Otherwise, add the number of weeks minus 1 to the date_entered value (i.e. for the second week you would want to add 2 - 1 or 1 week to the date_entered value).

> # A tibble: 5,307 × 7
>    artist  track                   time   date_entered  week  rank date      
>    <chr>   <chr>                   <time> <date>       <int> <dbl> <date>    
>  1 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       1    87 2000-02-26
>  2 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       2    82 2000-03-04
>  3 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       3    72 2000-03-11
>  4 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       4    77 2000-03-18
>  5 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       5    87 2000-03-25
>  6 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       6    94 2000-04-01
>  7 2 Pac   Baby Don't Cry (Keep... 04:22  2000-02-26       7    99 2000-04-08
>  8 2Ge+her The Hardest Part Of ... 03:15  2000-09-02       1    91 2000-09-02
>  9 2Ge+her The Hardest Part Of ... 03:15  2000-09-02       2    87 2000-09-09
> 10 2Ge+her The Hardest Part Of ... 03:15  2000-09-02       3    92 2000-09-16
> # ℹ 5,297 more rows

Answer 4

Create a dataset of the song(s) with the most weeks in the top 3 by month of 2000.

billboard_top3_month <- billboard_tidy_date |> 
  mutate(month = month(date),
         year = year(date),
         top3 = if_else(rank <= 3 & year == 2000, 1, 0)) |>
  mutate(peak_weeks = sum(top3),
         .by = c(month, artist, track)) |>
  slice_max(peak_weeks,
            by = month) |>
  distinct(month, artist, track, peak_weeks) |>
  arrange(month)
billboard_top3_month

2

We need to be able to filter rows by month so we need to create a month variable from out date variable.

3

This dataset is about the 2000 top 100 but many of these songs charted before or after that year. Therefore, we will need a variable for year as well so we make sure we’re finding the songs that charted in the months of 2000.

4

To create our top3 indicator we provide the two conditions that the rank for that observation is less than or equal to 3 AND the year is 2000. If so, top3 will be assigned a value of 1 and if not it will get a value of 0.

5

To filter for the most weeks in the top 3 we need to count how many weeks a song was in the top 3, grouped by month, song, and artist.

6

Then, by month we can slice the song(s) with the most weeks in the top 3.

7

Now we just want the name of the artist, song, the month they were charting in the top 3 for the most weeks, and the number of weeks

8

To see this in chronological order we want to arrange by the month

> # A tibble: 19 × 4
>    month artist              track                   peak_weeks
>    <dbl> <chr>               <chr>                        <dbl>
>  1     1 Aguilera, Christina What A Girl Wants                3
>  2     2 Savage Garden       I Knew I Loved You               4
>  3     3 Lonestar            Amazed                           4
>  4     4 Hill, Faith         Breathe                          5
>  5     4 Santana             Maria, Maria                     5
>  6     5 Hill, Faith         Breathe                          4
>  7     5 Santana             Maria, Maria                     4
>  8     6 Aaliyah             Try Again                        2
>  9     6 Anthony, Marc       You Sang To Me                   2
> 10     6 Hill, Faith         Breathe                          2
> 11     6 Santana             Maria, Maria                     2
> 12     6 Vertical Horizon    Everything You Want              2
> 13     7 Aaliyah             Try Again                        4
> 14     8 Sisqo               Incomplete                       4
> 15     8 matchbox twenty     Bent                             4
> 16     9 Janet               Doesn't Really Matte...          5
> 17    10 Madonna             Music                            4
> 18    11 Creed               With Arms Wide Open              4
> 19    12 Destiny's Child     Independent Women Pa...          5

Answer 5

Pick one month of 2000 and visualize the entire charting trajectory of the songs that spent at least 1 week in the top 3 during that month.

billboard_top3_month_viz <- billboard_tidy_date |> 
  mutate(month = month(date),
         year = year(date),
         top3 = if_else(rank <= 3 & year == 2000, 1, 0)) |> 
  mutate(month_peak = ifelse(top3 == 1, month, NA),
         .by = c(month, artist, track)) |> 
  filter(any(month_peak == 4),
         .by = c(track, artist)) 

date_order <- billboard_top3_month_viz |> 
  filter(date == date("2000-05-06")) |> 
  arrange(desc(rank))

ggplot(billboard_top3_month_viz, aes(date, rank, group = track, color = artist)) + 
  annotate(geom = "rect", xmin = ymd("2000-04-01"), xmax = ymd("2000-05-01"), ymin = 0, ymax = 85,
           fill = "#59a14f", alpha = 0.25) +
  geom_line(show.legend = TRUE) +
  geom_label_repel(data = billboard_top3_month_viz |> slice_max(date, by = track),
                   aes(label = track), 
                   show.legend = FALSE) +
  scale_color_manual("Artist", values = c("#4e79a7","#f28e2c","#e15759","#76b7b2")) +
  labs(title = "Billboard Top 100 Trajectory for Songs that Hit Top 3 During April 2000", 
       x = "Date", 
       y = "Rank", 
       caption = "Note: April shaded in green") + 
  theme_tufte(base_size = 14) + 
  theme(legend.position = c(0.85, 0.85),
        legend.title.align = 0.5,
        legend.box.background = element_rect(colour = "black", fill = "#f6f7f9"),
        plot.background = element_rect(fill = "#f6f7f9", color = "#f6f7f9"))

9

In order to visualize the entire trajectory, we need to have a variable that indicates when a song was charting in the top 3

10

This allows us to filter by each song whether any of the weeks charted in the top 3 for the month we’re interested in. Without these two steps we could only filter for the weeks in which they were charting and therefore couldn’t plot their entire ranking trajectory.

11

Shading the region for April for reference.

12

Want the legend for the line to appear.

13

Need to use a subset of the data otherwise each row will try to plot a label; I picked the last charting date for each song.

14

Wanted to remove the legend for the label (a lower-case “a”).

15

Specifying the colors I want to use for the 4 different lines.

16

Moving the legend to maximize plotting space.

17

Centering the legend title in the legend box.

18

Putting a black border around the legend.

19

Changing the plot color to the same color as the website.