DS4 Song Simulation 4.3

install.packages("tidyverse")

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library(tidyverse)

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df<-read_csv("Class Playlist Clean DS4 - Sheet1.csv")

## Rows: 175 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Genre, Artist, Song
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

We created columns to show each genres most voted artist, ex. Yonaguni was voted most in pop/kpop/latin

head(df)

## # A tibble: 6 × 3
##   Genre                    Artist         Song                          
##   <chr>                    <chr>          <chr>                         
## 1 Genre 1 - hiphop/rap     8k             When you call                 
## 2 Genre 2 - pop/kpop/Latin Yonaguni       bad bunny                     
## 3 Genre 1 - hiphop/rap     $uicideboy$    Diemonds                      
## 4 Genre 1 - hiphop/rap     $uicideboy$    Not Even Ghosts Are This Empty
## 5 Genre 1 - hiphop/rap     03 Greedo      Trap House                    
## 6 Genre 5 - alt/indie/folk 10,000 Maniacs Trouble Me

We took count of each genres and how many people voted in these genres

df$Genre <- as.factor(df$Genre)

genre_counts<-df%>%count(df$Genre)
genre_counts

## # A tibble: 6 × 2
##   `df$Genre`                   n
##   <fct>                    <int>
## 1 Genre 1 - hiphop/rap        58
## 2 Genre 2 - pop/kpop/Latin    25
## 3 Genre 3 -  house/ska         5
## 4 Genre 4 -  rnb/soul         14
## 5 Genre 5 - alt/indie/folk    45
## 6 Genre 6 - country/rock      28

We changed the column of each genres probability

genre_counts<-genre_counts%>%mutate(tprob=n/sum(n))

we have 33% hiphop/rap because we rounded to the decimal

genre_counts$tprob <- round(genre_counts$tprob,2)
genre_counts

## # A tibble: 6 × 3
##   `df$Genre`                   n tprob
##   <fct>                    <int> <dbl>
## 1 Genre 1 - hiphop/rap        58  0.33
## 2 Genre 2 - pop/kpop/Latin    25  0.14
## 3 Genre 3 -  house/ska         5  0.03
## 4 Genre 4 -  rnb/soul         14  0.08
## 5 Genre 5 - alt/indie/folk    45  0.26
## 6 Genre 6 - country/rock      28  0.16