This report is an exploration of the artist Chance the Rapper.

Warning: Explicit Content

I will be looking at how/if Chance’s music becomes more positive as his life becomes positive. I will be looking at his four main albums “10 Day” (2012), “Acid Rap” (2013), “Coloring Book” (2016), and “The Big Day” (2019). I chose only to look at these works with the help of Wikipedia and Spotify. Although Spotify only has the three albums “Acid Rap”, “Coloring Book”, and “The Big Day” on it’s platform, if you go to the about section under the artist, Spotify recognizes “10 Day” as Chance the Rapper’s first real mixtape/ album (mixtape and album are used interchangeably in his case). With that said, Spotify recognizes his second album “Acid Rap” as his first to hit the charts. Using “10 Day” in my analysis can be useful to show how his music changes as his fame grows. I will also look at the explicit words within Chance the Rapper’s albums. I will be looking at if the number of curse words go down throughout each album. The reduction in curse words throughout his music as each album passes represents his changing image. I will be using both the Genuis API and Spotify API to get my necessary information.

First I download the necessary packages

library(genius)
library(tidyverse)
library(tidytext)
library(textdata)
library(wordcloud2)
library(ggplot2)
library(devtools)
library(spotifyr)
library(knitr)
library(ggjoy)
library(dplyr)
library(plotly)
library(ggthemes)

Next I use my Spoitfy API access keys to download Chance the Rapper Spotify data

Here I am going to download each individual album

“10 Day”

10 Day is Chance the Rapper’s real first mixtape. This mixtape was released during a ten day suspension he received in High School for bringing weed onto his campus leading to the album name “10 Day” - https://en.wikipedia.org/wiki/Chance_the_Rapper.

tenday <- genius_album(artist = "Chance the Rapper", album = "10 Day")
View(tenday)

How many words are in this album

tenday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  count()

## # A tibble: 1 x 1
##       n
##   <int>
## 1  7043

7043

How many times do the most popular words occur within the album?

tenday %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  filter(!word %in% c("tahm", "igh")) %>% 
  head(10) %>%
  knitr::kable()

word	n
juke	108
fuck	97
bout	68
nigga	62
mama	37
time	36
break	32
niggas	32
shit	32
brain	26

the most used words are fairly negative

What is the sentiment of these words?

tenday %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  inner_join(get_sentiments("bing")) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  filter(!word %in% c("tahm", "igh")) %>% 
  head(10) %>%
  knitr::kable()

word	n	sentiment	value
fuck	97	negative	-4
shit	32	negative	-4
fucking	18	negative	-4
love	16	positive	3
smile	10	positive	2
bitch	9	negative	-5
funny	9	negative	4
warm	9	positive	1
worried	8	negative	-3
bad	7	negative	-3

note –> funny has a negative sentiment but a positive value?? (shortcoming of bing lexicon). list of words changes due to which can be measured by sentiment or not. stays in line with original observation of mostly negative words

What words stand out the most in a word cloud

tenday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  filter(!word %in% c("tahm", "igh")) %>% 
  wordcloud2()

“Juke”, “Fuck” “Bout”

Looking at Spotify Data

Chance_the_Rapper %>% 
  filter(album_name %in% "10 Day") -> TenDayAlbum
View(TenDayAlbum)

Which track in 10 Day has the highest danceability?

Chance_the_Rapper %>% 
  filter(album_name %in% "10 Day") %>% 
  select(danceability, album_name, track_name) %>% 
  kable()

danceability	album_name	track_name
0.826	10 Day	14,400 Minutes
0.612	10 Day	Nostalgia
0.743	10 Day	Missing You
0.513	10 Day	Windows
0.565	10 Day	Brain Cells
0.392	10 Day	Long Time
0.492	10 Day	22 Offs
0.490	10 Day	U Got Me Fucked Up
0.639	10 Day	Family
0.813	10 Day	Juke Juke
0.437	10 Day	Fuck You Tahm Bout
0.608	10 Day	Long Time II
0.473	10 Day	Prom Night
0.705	10 Day	Hey Ma

14,400 minutes & Juke Juke have the highest danceability (highest .826)

Can each track’s danceability be shown in a graph?

TenDayAlbum %>% 
  ggplot()+
  geom_col(aes(x=track_name, y=danceability))+
  theme_economist()+
  theme(axis.text.x = element_text(angle = 90))

What is the distribution of the danceability within 10 Day?

ggplot(TenDayAlbum, aes(x=danceability)) +
  geom_density(alpha=0.7)+
  labs(x="Danceability", y="Density") +
  theme_economist()+
  ggtitle("Distribution of Danceability Data in 10 Day")

average of 2.5 songs have a danceability rate between .5 and .6

Using a heatmap code created by http://student.elon.edu/bwilliamson5/FinalProject/index.html to analyize danceability in 10 Day

ggplot(data = TenDayAlbum, aes(x = danceability, y = track_name)) +
  geom_tile(aes(fill=danceability, height=1 , width=.1),colour="red") +
  scale_y_discrete(breaks = TenDayAlbum$track_name) +
  scale_fill_gradient2(low = "#FF0000", midpoint=0.6,
                       space="Lab", mid="#FFE500", high = "#09B505") +
  labs(x = "Danceability") +
  labs(y = "Track Name") +
  labs(title = "10 Day Album Danceability")

Looking at the explicit words within 10 Day

Within 10 Day Chance is a high school druggie with a failing music career

10 Day without any stop or soft words

tenday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words)-> tendayexplicit

How often are the 10 most explicit words throughout all albums used in 10 Day?

tendayexplicit %>% 
  filter(word %in% c("fuck", "shit", "niggas", "bitch", "ass", "fucking", "damn", "fucked", "dick", "bitches")) %>% 
  count(word, sort = TRUE) %>%
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_economist()

“fuck” is used nearly 100 times within the album

“Acid Rap”

During Acid Rap, Chance is going through his drug addiction but his music career is starting to take off. The artist “Tyler the Creator” allowed Chance to open for him allowing his music to reach new audiences. In the song “Cocoa Butter Kisses” you can see his family’s disapproval of his addiction in the lyrics: “Put Visine inside my eyes so my grandma would fucking hug me” - http://www.mtv.com/news/1707187/chance-the-rapper-acid-rap-mixtape/

acidrap <- genius_album(artist = "Chance the Rapper", album = "Acid Rap")
View(acidrap)

How many words are in this album

acidrap %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  count()

## # A tibble: 1 x 1
##       n
##   <int>
## 1  7781

7781

How many times do the most popular words occur within the album

acidrap %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  filter(!word %in% c("tahm", "igh", "na", "ooh")) %>% 
  head(10) %>%
  knitr::kable()

word	n
love	58
juice	52
shit	51
jam	49
yeah	49
kisses	39
smoke	33
fuck	29
bout	27
bitch	24

the top three words used are fairly positive in this album (maybe a sign of growth?)

What is the sentiment of these words?

acidrap %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  inner_join(get_sentiments("bing")) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  filter(!word %in% c("tahm", "igh", "na", "ooh")) %>% 
  head(10) %>%
  knitr::kable()

word	n	sentiment	value
love	58	positive	3
shit	51	negative	-4
fuck	29	negative	-4
bitch	24	negative	-5
miss	20	negative	-2
lost	10	negative	-3
scared	10	negative	-2
fucking	9	negative	-4
damn	8	negative	-4
dick	7	negative	-4

only one positive sentiment word. list of words changes due to which can be measured by sentiment or not.

What words stand out the most in a word cloud

acidrap %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  filter(!word %in% c("tahm", "igh", "na", "ooh")) %>% 
  wordcloud2()

“love”, “jam”, “yeah”, “juice”, “shit”, “kisses”, “smoke”, “fuck”

Loading Spotify Data

Chance_the_Rapper %>% 
  filter(album_name %in% "Acid Rap") -> AcidRapAlbum
View(AcidRapAlbum)

Which track in Acid Rap has the highest danceability?

Chance_the_Rapper %>% 
  filter(album_name %in% "Acid Rap") %>% 
  select(danceability, album_name, track_name) %>% 
  kable()

danceability	album_name	track_name
0.605	Acid Rap	Good Ass Intro
0.768	Acid Rap	Pusha Man
0.472	Acid Rap	Paranoia
0.679	Acid Rap	Cocoa Butter Kisses
0.759	Acid Rap	Juice
0.667	Acid Rap	Lost
0.551	Acid Rap	Everybody’s Something
0.417	Acid Rap	Interlude (That’s Love)
0.725	Acid Rap	Favorite Song
0.594	Acid Rap	NaNa
0.721	Acid Rap	Smoke Again
0.727	Acid Rap	Acid Rain
0.661	Acid Rap	Chain Smoker
0.692	Acid Rap	Everything’s Good (Good Ass Outro)

Juice and Pusha Man have the highest danceability (highest .768)

Each track’s danceability shown in a graph

AcidRapAlbum %>% 
  ggplot()+
  geom_col(aes(x=track_name, y=danceability))+
  theme_economist()+
  theme(axis.text.x = element_text(angle = 90))+
  theme(axis.text.x = element_text(size = 5))

What is the distribution of the danceability within Acid Rap?

ggplot(AcidRapAlbum, aes(x=danceability)) +
  geom_density(alpha=0.7)+
  labs(x="Danceability", y="Density") +
  theme_economist()+
  ggtitle("Distribution of Danceability Data in Acid Rap")

average of 4 songs have a danceability rate between .6 and .8

Using a heatmap code created by http://student.elon.edu/bwilliamson5/FinalProject/index.html to analyize danceability in Acid Rap

ggplot(data = AcidRapAlbum, aes(x = danceability, y = track_name)) +
  geom_tile(aes(fill=danceability, height=1 , width=.1),colour="red") +
  scale_y_discrete(breaks = AcidRapAlbum$track_name) +
  scale_fill_gradient2(low = "#FF0000", midpoint=0.6,
                       space="Lab", mid="#FFE500", high = "#09B505") +
  labs(x = "Danceability") +
  labs(y = "Track Name") +
  labs(title = "Acid Rap Album Danceability")

Looking at the explicit words within Acid Rap

In Acid Rap Chance’s music is finally taking off and hitting the charts

Acid Rap without any stop or soft words

acidrap %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words)-> acidrapexplicit

How often are the 10 most explicit words throughout all albums used in Acid Rap?

acidrapexplicit %>% 
  filter(word %in% c("fuck", "shit", "niggas", "bitch", "ass", "fucking", "damn", "fucked", "dick", "bitches")) %>% 
  count(word, sort = TRUE) %>%
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_economist()

Although “shit” is used almost 50 times throughout the album, “fuck” has reduced to only about 30 times. The amount of overall curse words has decreased.

“Coloring Book”

Coloring Book is the first album where Chance’s music has more of a gospel-y sound. His music begins to focus on moving forward from his past and focusing on his future and his music. In the song “Finish Line/ Drown” he states: “Last year got addicted to Xans, Started forgetting my name and started missing my chance” showing how his mistakes were holding him back. Chance also recognizes the good things in his life. In the song “Blessings” he says: “I know the difference in blessings and worldly possessions, Like my ex-girl getting pregnant, And her becoming my everything”. As seen in this Genuis article, drugs is the reason there was a 3-year gap between Acid Rap and Coloring Book.

coloringbook <- genius_album(artist = "Chance the Rapper", album = "Coloring Book")
View(coloringbook)

How many words are in this album

coloringbook %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  count()

## # A tibble: 1 x 1
##       n
##   <int>
## 1  7665

7665

How many times do the most popular words occur within the album

coloringbook %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  filter(!word %in% c("tahm", "igh", "na", "ayy", "bom")) %>% 
  head(10) %>%
  knitr::kable()

word	n
ready	44
blessings	35
god	33
night	32
drinking	28
citywide	27
gon	27
drugs	24
hey	24
deserve	23

the top 5 most used words in the song are very positive

What is the sentiment of these words?

coloringbook %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  inner_join(get_sentiments("bing")) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  filter(!word %in% c("tahm", "igh", "na", "ayy", "bom")) %>% 
  head(10) %>%
  knitr::kable()

word	n	sentiment	value
praise	16	positive	3
bitch	15	negative	-5
fuck	15	negative	-4
love	13	positive	3
miracle	13	positive	4
shit	11	negative	-4
bad	9	negative	-3
woo	9	positive	3
free	7	positive	1
damn	6	negative	-4

mix of negative and positive sentiments. odd that “f word” has a lower value than “b word”. list of words changes due to which can be measured by sentiment or not.

What words stand out the most in a word cloud

coloringbook %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  filter(!word %in% c("tahm", "igh", "na", "ayy", "bom")) %>% 
  wordcloud2()

“ready”, “blessings”, “god”, “night”, “drinking”, “deserve”, “friends”, “citywide”

Loading Spotify Data

Chance_the_Rapper %>% 
  filter(album_name %in% "Coloring Book") -> CBAlbum
View(CBAlbum)

Which track in Coloring Book has the highest danceability?

Chance_the_Rapper %>% 
  filter(album_name %in% "Coloring Book") %>% 
  select(danceability, album_name, track_name) %>% 
  kable()

danceability	album_name	track_name
0.531	Coloring Book	All We Got (feat. Kanye West & Chicago Children’s Choir)
0.652	Coloring Book	No Problem (feat. Lil Wayne & 2 Chainz)
0.693	Coloring Book	Summer Friends (feat. Jeremih & Francis & The Lights)
0.404	Coloring Book	D.R.A.M. Sings Special
0.714	Coloring Book	Blessings (feat. Jamila Woods)
0.461	Coloring Book	Same Drugs
0.838	Coloring Book	Mixtape (feat. Young Thug & Lil Yachty)
0.771	Coloring Book	Angels (feat. Saba)
0.505	Coloring Book	Juke Jam (feat. Justin Bieber & Towkio)
0.704	Coloring Book	All Night (feat. Knox Fortune)
0.439	Coloring Book	How Great (feat. Jay Electronica & My cousin Nicole)
0.705	Coloring Book	Smoke Break (feat. Future)
0.456	Coloring Book	Finish Line / Drown (feat. T-Pain, Kirk Franklin, Eryn Allen Kane & Noname)
0.381	Coloring Book	Blessings (feat. Ty Dolla $ign, Anderson .Paak, BJ The Chicago Kid, Raury & Jamila Woods)

Mixtape (feat. Young Thug & Lil Yachty) and Angels (feat. Saba) have the highest dancability (highest .838)

Each track’s danceability shown in a graph

CBAlbum %>% 
  ggplot()+
  geom_col(aes(x=track_name, y=danceability))+
  theme_economist()+
  theme(axis.text.x = element_text(angle = 90))+
  theme(axis.text.x = element_text(size = 2))

What is the distribution of the danceability within Coloring Book?

ggplot(CBAlbum, aes(x=danceability)) +
  geom_density(alpha=0.7)+
  labs(x="Danceability", y="Density") +
  theme_economist()+
  ggtitle("Distribution of Danceability Data in Coloring Book")

average of 2 songs have a danceability rate of either between .4 and .5 or .7 and .8

Using a heatmap code created by http://student.elon.edu/bwilliamson5/FinalProject/index.html to analyize danceability in Coloring Book

ggplot(data = CBAlbum, aes(x = danceability, y = track_name)) +
  geom_tile(aes(fill=danceability, height=1 , width=.1),colour="red") +
  scale_y_discrete(breaks = CBAlbum$track_name) +
  scale_fill_gradient2(low = "#FF0000", midpoint=0.6,
                       space="Lab", mid="#FFE500", high = "#09B505") +
  theme(axis.text.y = element_text(size = 3))+
  labs(x = "Danceability") +
  labs(y = "Track Name") +
  labs(title = "Coloring Book Album Danceability")

Looking at the explicit words within Coloring Book

In Coloring Book Chance is over his addiction and back in love with his pregnant ex girlfriend

Coloring Book without any stop or soft words

coloringbook %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words)-> coloringbookexplicit

How often are the 10 most explicit words throughout all albums used in Coloring Book?

coloringbookexplicit %>% 
  filter(word %in% c("fuck", "shit", "niggas", "bitch", "ass", "fucking", "damn", "fucked", "dick", "bitches")) %>% 
  count(word, sort = TRUE) %>%
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_economist()

“fuck” and “bitch” are only used about 15 times throughout this album, a big drop in comparison to the others. Most gospel like album, could be why there is such a decrease in curse words.

“The Big Day”

The Big Day is Chance the Rapper’s most recent and most mainstream album. He begins to move away from his Gospel/Blues-y vibe and into a more pop like tone.

bigday <- genius_album(artist = "Chance the Rapper", album = "The Big Day")
View(bigday)

How many words are in this album

bigday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  count()

## # A tibble: 1 x 1
##       n
##   <int>
## 1 11722

11722

How many times do the most popular words occur within the album

bigday %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  filter(!word %in% c("tahm", "igh", "na", "ooh", "yeah", "uh")) %>% 
  head(10) %>%
  knitr::kable()

word	n
single	66
love	54
time	45
baby	41
bag	38
forever	37
die	36
step	33
day	32
people	29

the top 8 most used words in this song are very positive

What is the sentiment of these words?

bigday %>%
  unnest_tokens(word, lyric) %>%
  select(word) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE) %>%
  inner_join(get_sentiments("bing")) %>% 
  inner_join(get_sentiments("afinn")) %>% 
  filter(!word %in% c("tahm", "igh", "na", "ooh", "yeah", "uh")) %>% 
  head(10) %>%
  knitr::kable()

word	n	sentiment	value
love	54	positive	3
die	36	negative	-3
fuck	19	negative	-4
crazy	17	negative	-2
shit	16	negative	-4
worth	15	positive	2
bad	14	negative	-3
damn	11	negative	-4
bitch	10	negative	-5
hug	8	positive	2

mix of positive and negative words. list of words changes due to which can be measured by sentiment or not.

What words stand out the most in a word cloud

bigday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) %>% 
  filter(!word %in% c("tahm", "igh", "na", "ooh", "yeah", "uh")) %>% 
  wordcloud2()

“single”, “love”, “baby”, “time”, “forever”

Loading Spotify Data

Chance_the_Rapper %>% 
  filter(album_name %in% "The Big Day") -> BigDayAlbum
View(BigDayAlbum)

Which track in the Big Day has the highest danceability?

Chance_the_Rapper %>% 
  filter(album_name %in% "The Big Day") %>% 
  select(danceability, album_name, track_name) %>% 
  head(15) %>% 
  kable()

danceability	album_name	track_name
0.767	The Big Day	All Day Long
0.606	The Big Day	Do You Remember
0.793	The Big Day	Eternal
0.899	The Big Day	Hot Shower
0.657	The Big Day	We Go High
0.751	The Big Day	I Got You (Always and Forever)
0.707	The Big Day	Photo Ops (Skit)
0.543	The Big Day	Roo
0.654	The Big Day	The Big Day
0.780	The Big Day	Let’s Go On The Run
0.869	The Big Day	Handsome
0.822	The Big Day	Big Fish
0.926	The Big Day	Ballin Flossin
0.710	The Big Day	4 Quarters In The Black (Skit)
0.672	The Big Day	5 Year Plan

Ballin Flossin and Hot Shower have the highest danceability (highest .909)

Each track’s danceability shown in a graph

BigDayAlbum %>% 
  ggplot()+
  geom_col(aes(x=track_name, y=danceability))+
  theme_economist()+
  theme(axis.text.x = element_text(angle = 90))+
theme(axis.text.x = element_text(size = 5))

What is the distribution of the danceability within The Big Day?

ggplot(BigDayAlbum, aes(x=danceability)) +
  geom_density(alpha=0.7)+
  labs(x="Danceability", y="Density") +
  theme_economist()+
  ggtitle("Distribution of Danceability Data in The Big Day")

average of 3 songs have a danceability rate between .7 and .8

Using a heatmap code created by http://student.elon.edu/bwilliamson5/FinalProject/index.html to analyize danceability in The Big Day

ggplot(data = BigDayAlbum, aes(x = danceability, y = track_name)) +
  geom_tile(aes(fill=danceability, height=1 , width=.1),colour="red") +
  scale_y_discrete(breaks = BigDayAlbum$track_name) +
  scale_fill_gradient2(low = "#FF0000", midpoint=0.6,
                       space="Lab", mid="#FFE500", high = "#09B505") +
  theme(axis.text.y = element_text(size = 4))+
  labs(x = "Danceability") +
  labs(y = "Track Name") +
  labs(title = "The Big Day Album Danceability")

Looking at the explicit words within The Big Day

In The Big Day Chance is at the height of his music fame and a father

The Big Day without any stop or soft words

bigday %>% 
  unnest_tokens(word, lyric) %>% 
  select(word) %>% 
  anti_join(stop_words)-> bigdayexplicit

How often are the 10 most explicit words throughout all albums used in The Big Day?

bigdayexplicit %>% 
  filter(word %in% c("fuck", "shit", "niggas", "bitch", "ass", "fucking", "damn", "fucked", "dick", "bitches")) %>% 
  count(word, sort = TRUE) %>%
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_economist()

“fuck” goes back up to being used around 20 times, while there is still a decrease in overall curse words in this album this could have to do with the more “mainstream” sound he is trying to achieve in The Big Day

Downloading all albums together for a joint analysis

chance_albums <- tribble(
  ~artist, ~title,
  "Chance the Rapper", "10 Day",
  "Chance the Rapper", "Acid Rap",
  "Chance the Rapper", "Coloring Book",
  "Chance the Rapper", "The Big Day"
)
View(chance_albums)
chance_lyrics <- chance_albums %>% 
  add_genius(artist, title, type="album")

All lyrics from all albums

View(chance_lyrics)

Removing any stop and soft words leaving only sentiment words left

chance_lyrics %>% 
  unnest_tokens(word, lyric) %>% 
  anti_join(stop_words) %>% 
  count(word, sort = TRUE) -> chance_word_count

What words occur the most throughout all his albums

wordcloud2(chance_word_count)

“yeah”, “fuck”, “juke”, “love”, “time”, “nigga”, “shit”, “god”

How much the 20 most common words are used throughout the albums

chance_word_count %>% 
  head(20) %>% 
  ggplot(aes(reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_economist()

Finding the sentiments of the most common used words throughout the albums

chance_word_count %>% 
  inner_join(get_sentiments("afinn"))-> chance_sentiment
View(chance_sentiment)

What are the most negative sentiment words

chance_sentiment %>% 
  arrange(desc(-value))

## # A tibble: 351 x 3
##    word              n value
##    <chr>         <int> <dbl>
##  1 niggas           78    -5
##  2 bitch            58    -5
##  3 bitches          10    -5
##  4 motherfucking     3    -5
##  5 cock              1    -5
##  6 motherfucker      1    -5
##  7 prick             1    -5
##  8 fuck            160    -4
##  9 shit            110    -4
## 10 ass              33    -4
## # … with 341 more rows

The n-word is probably not used in a negative way rather talking about people he probably considers family. An example of this is in the song “No Problem”: “If one more label try to stop me, It’s gon’ be some dreadhead niggas in ya lobby, huh huh”. According to Genius, in this song the "dreadhead n*ggas" he is refering to are the other artists from Chicago he wants to collaborate with, but is not allowed to due to record labels trying to control him and his music

What is the overall sentiment of his works

mean(chance_sentiment$value)

## [1] -0.4501425

-0.4501425

Are the words used in Chance’s lyrics positive or negative?

chance_word_count %>% 
  inner_join(get_sentiments("bing")) -> chance_sentiment2
View(chance_sentiment2)

How many negative words are there in comparison to positive words?

chance_sentiment2 %>% 
  ggplot() + geom_col(aes(sentiment, n)) + theme_economist()

There are many more negative words overall than positive throughout his works

Most frequent explicit words with highest negative sentiment throughout all albums

chance_word_count %>% 
  inner_join(get_sentiments("afinn")) %>% 
  filter(value == "-5"|value == "-4") %>% 
  arrange(desc(n))

## # A tibble: 20 x 3
##    word              n value
##    <chr>         <int> <dbl>
##  1 fuck            160    -4
##  2 shit            110    -4
##  3 niggas           78    -5
##  4 bitch            58    -5
##  5 ass              33    -4
##  6 fucking          28    -4
##  7 damn             27    -4
##  8 fucked           14    -4
##  9 dick             12    -4
## 10 bitches          10    -5
## 11 hell              4    -4
## 12 bullshit          3    -4
## 13 motherfucking     3    -5
## 14 asshole           2    -4
## 15 pissed            2    -4
## 16 cock              1    -5
## 17 fucker            1    -4
## 18 motherfucker      1    -5
## 19 prick             1    -5
## 20 torture           1    -4

Overall analysis using Spotify data

What do the different chords used say about his music

Chance_the_Rapper %>% 
  count(key_mode, sort = TRUE) %>% 
  head(10) %>% 
  kable()

key_mode	n
C# major	12
A major	9
G major	8
F minor	7
A# major	5
B minor	5
D major	5
F# minor	5
B major	4
C major	4

C major and G major are used most often in pop music –> https://mixedinkey.com/captain-plugins/wiki/common-chord-progressions-pop-music/#

what is the most joyful Chance the Rapper song?

Chance_the_Rapper %>% 
  arrange(-valence) %>% 
  select(track_name, valence) %>% 
  head(15) %>% 
  kable()

track_name	valence
NaNa	0.891
Pusha Man	0.883
The Big Day	0.822
The Big Day	0.814
Favorite Song	0.811
Nostalgia	0.810
Juice	0.804
Ballin Flossin	0.789
No Problem (feat. Lil Wayne & 2 Chainz)	0.788
Long Time	0.773
Ballin Flossin	0.766
22 Offs	0.726
All Day Long	0.717
Family	0.698
All Day Long	0.697

NaNa is the most joyful song

Valence by album:

Chance_the_Rapper %>%
  group_by(album_name) %>%
  summarise(mean(valence)) %>%
  arrange(desc(`mean(valence)`)) %>%
  kable()

album_name	mean(valence)
10 Day	0.5571429
The Big Day	0.5499091
Acid Rap	0.4915714
Coloring Book	0.4329286

The Big Day has the highest Valence meaning it is the most joyful album

Which album has the biggest emotional journey?

ggplot(Chance_the_Rapper, aes(x = valence, y = album_name)) + 
  geom_joy() + 
  theme_economist() +
  ggtitle("Joyplot of Chance the Rapper's joy distributions", 
          subtitle = "Based on valence pulled from Spotify's Web API with spotifyr")

Acid Rap has the biggest emotional journey (makes sense with drug addiction) and The Big Day is the most joyful.

Using code from https://msmith7161.github.io/what-is-speechiness/, I will be looking at how Danceability was distributed between the albums

green <- "#1ed760"
yellow <- "#e7e247"
pink <- "#ff6f59"
blue <- "#17bebb"

ggplot(Chance_the_Rapper, aes(x=danceability, fill=album_name,
                              text = paste(album_name)))+
  geom_density(alpha=0.7, color=NA)+
  scale_fill_manual(values=c(green, yellow, pink, blue))+
  labs(x="Danceability", y="Density") +
  guides(fill=guide_legend(title="Album Name"))+
  theme_economist()+
  ggtitle("Distribution of Danceability Data")

Acid Rap and The Big Day have the highest danceability

Explicit Conclusion:

All main 10 curse words throughout all Chance’s albums decrease in each album. I believe the large explicitness in 10 Day is Chance releasing his anger towards the world. Using so many curse words is representative of his failures. That could be a big reason why this first album did not take off. In Acid Rap the amount of curse words decrease by almost half. This could relate to his music career taking off. There is a new lightness to his music even though he is experiencing drug addiction. In Coloring Book the explicitness decreases by another third from the amount in Acid Rap. Coloring Book is Chance at the happiest we have seen him. He is over his drug addiction and reflecting on his past. He uses his music to continue forward and look at the blessings around him “Like {his} ex-girl getting pregnant, And her becoming {his} everything”. The Big Day is released three years after Coloring Book. During this time Chance’s fame is growing and he become a father. This time allows for a lot of growth but also possibly a new image/sound. This can be quantified by the increase in danceability from album to album. The release of The Big Day looks into a new tone in Chance’s music. The Big Day is extremely pop-y and mainstream sounding. I believe the slight increase in explicitness in The Big Day from Coloring Book is an example of the more mainstream look he is going for.

Overall Conclusion:

From my analysis I believe Chance the Rapper’s music did become more positive as his life became more positive. This conclusion is due more to the increase in danceability and Valence per album rather than the lyrics. While the lyrics in most albums have large negative sentiments, the danceability and joy in each album increases. Although not necessarily the lyrics, the tone of the albums overall become more positive. Looking at the frequency of curse words within each album also shows the decline in negativity or even need for those words as time continues.

MEA 329 Final Project - Chance the Rapper

Devon Margulies

11/23/2020

This report is an exploration of the artist Chance the Rapper.

Warning: Explicit Content

First I download the necessary packages

Next I use my Spoitfy API access keys to download Chance the Rapper Spotify data

Here I am going to download each individual album

“10 Day”

Looking at Spotify Data

Looking at the explicit words within 10 Day

“Acid Rap”

Loading Spotify Data

Looking at the explicit words within Acid Rap

“Coloring Book”

Loading Spotify Data

Looking at the explicit words within Coloring Book

“The Big Day”

Loading Spotify Data

Looking at the explicit words within The Big Day

Downloading all albums together for a joint analysis

Overall analysis using Spotify data

Explicit Conclusion:

Overall Conclusion: