Introduction

Aubrey Drake Graham was born on October 24, 1986 in Canada. He started as an actor on the hit television series Degrassi: The Next Generation, and then started rapping under the name Drake. Before his fame as multi-grammy wining artist, he would drop two mixtapes, and then go on to signing his first record deal with Young Money. He would release his debut album titled ‘Thank Me Later’ with many A-list features such as Jay-Z and Alicia Keys. The success of that album would bring him lots of praise, popularity, and anticipation for his next album, ‘Nothing Was the Same’. By this point, Drake had a choke hold on the music industry as his albums were generating nothing but success and popularity for not only him, but his brand and record label OVO that was created in 2012. Since then, Drake has not stopped releasing music and has even been on the Hot 100 chart for eight consecutive years.

library("tidytext")
library("wordcloud2")
library("tidyverse")
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
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## ✓ tibble  3.1.6     ✓ dplyr   1.0.8
## ✓ tidyr   1.2.0     ✓ stringr 1.4.0
## ✓ readr   2.1.2     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Overview

The data that I found is from https://www.kaggle.com/deepshah16/song-lyrics-dataset. I then took out a portion of the data that included Drake and his music and imported it into R.

library("readxl")
Drake <- read_excel("~/Desktop/Drake.xlsx")
## New names:
## * `` -> ...7
Drake %>%
  unnest_tokens(word, Lyric) %>%
  anti_join(stop_words) -> Cleaned_Drake
## Joining, by = "word"

Hypothesis

I predict to find a mix of words including profanity, and words about love. Drake is rapper and a singer whose lyrics range from talking about love, to dissing another rapper or an ex-lover of his. So I think there will be a range of words that will be found.

Lyric Analysis

I then wanted to see what Drake’s most used words are. So I created a created a chart that shows the top 10 most used words that drake has used in his songs.

Cleaned_Drake %>%
  count(word, sort = TRUE) %>%
  filter(!word %in% "A") %>%
  filter(!word %in% "zealous") %>%
  head(10) %>%
  ggplot(aes(x= reorder (word, n), y = n, fill=word)) + geom_col() +
  coord_flip() +
  ggtitle("Drake's Top 10 Most Used Words")

Next, I created a word cloud to show the most popular words that Drake has used during his rap career, along with a frequency chart that shows the frequency of those words.

Cleaned_Drake %>%
  count(word) %>%
  filter(!word %in% "A") %>%
  filter(!word%in% "ovo") %>%
  wordcloud2()
Cleaned_Drake %>%
  filter(!word %in% "A") %>%
  filter(!word%in% "zealous") %>%
  count(word, sort = TRUE) %>%
  head(10) %>%
  knitr::kable()
word n
yeah 1269
drake 883
shit 740
love 578
girl 547
nigga 502
niggas 490
time 489
wanna 410
baby 371

Next, I created several charts that show the top 10 words across certain albums by Drake. The albums I chose to analyze were ‘Views’, ‘More Life’, ‘Scorpion’, and his most recent mixtape, ‘Dark Lane Demo Tapes’

Cleaned_Drake %>%
  filter(Album %in% "Views") %>%
  count(word, sort = TRUE) %>%
  head(10) %>%
  ggplot(aes(reorder(word,n), n, fill=word)) + geom_col() +
  ggtitle("Views Top 10 Words") +
  coord_flip()

This album was released at arguably the peak of a Drake’s career. It gained a lot of popularity in social media through memes which was very helpful for it’s sales. It is no surprise to me that the most used word for this album was “yeah”, it is commonly used by rappers to end a bar or even just take up space in a song as an ad-lib. His least used word for this album was “gon”.

Cleaned_Drake %>%
  filter(Album %in% "More Life") %>%
  count(word, sort = TRUE) %>%
  head(10) %>%
  ggplot(aes(reorder(word,n), n, fill=word)) + geom_col() +
  ggtitle("More Life Top 10 Words") +
  coord_flip()

More Life was released a year after Views, and just proved even more that Drake has the longevity to be a timeless artist. The most used word for this album was “yeah” again. He least used words on this album was “life” and “gotta”

Cleaned_Drake %>%
  filter(Album %in% "Scorpion") %>%
  count(word, sort = TRUE) %>%
  head(10) %>%
  ggplot(aes(reorder(word,n), n, fill=word)) + geom_col() +
  ggtitle("Scorpion Top 10 Words") +
  coord_flip()

Scorpion was released a year after More Life. This album was different than the ‘usual’ drake album, as it had 2 sides to the album, was side being his normal trap-club vibe, and the other being a more reggae dance vibe. The most used word for this album was “yeah”, once again. and the least used word on this album was “love”

Cleaned_Drake %>%
  filter(Album %in% "Dark Lane Demo Tapes") %>%
  count(word, sort = TRUE) %>%
  head(10) %>%
  ggplot(aes(reorder(word,n), n, fill=word)) + geom_col() +
  ggtitle("Dark Lane Demo Tapes Top 10 Words") +
  coord_flip()

Dark Lane Demo Tapes was released in 2020 as a mixtape, two year after Scorpion was released. This mixtape supposedly including songs that were made years before the mixtape release, and you can even notice the how some songs sound similar to his past older albums. The most used word on this mixtape was “yeah”, again. And the least used word on this mixtape was slide.

Conclusion

My prediction of Drake’s lyrics having a range of words seemed to be correct. Upon doing this analysis, I noticed that with each album, there were the same type of words, but their frequencies changed based on the type of album. For example, on ‘Views’, the fourth most used word was “time”, and on “More Life”, time was the seventh most used word. ‘View’ was an album that included a lot of reflection of the past from Drake, while on “More Life”, Drake is just rapping about living his best life. An even better example of this is the word “love”. For ‘Views’, the word “Love” was the third to least used word on the album, while for “More Life”, it was the sixth most used word. As I said before, ‘Views’ was an album of Drake reflecting and many of the songs were not about love. But with ‘More Life’, the songs have a more uplifting tone and are even more positive than those from ‘Views’. Overall, I thought the analysis was very interesting. Looking at these albums based on the words that were said in the lyrics is very enlightening because it shows how each albums is different, although many of the same words are used in each album.