{r} install.packages ("tidyverse")
{r} library("tidyverse")
{r} df <- read_csv("Playlist - Clean.csv")
{r} df <- read_csv("Playlist - Clean.csv")
{r} df$Genre <- as.factor(df$Genre) df %>% count(Genre)
{r} df$Genre<-as.factor(df$Genre)
{r} head(df)
{r} df %>% count(Genre)
{r} genre_counts <- df %>% count(Genre)
{r} genre_counts <- genre_counts %>% mutate(prob = n/sum(n)) genre_counts
{r} genre_counts$prob <- round(genre_counts$prob, 2) genre_counts
## Genre 1 - hiphop/rap has a 33% chance, Genre 2 - pop/kpop/Latin has a
14% chance Genre 3 - house/ska has 3% chance, Genre 4 - rnb/soul has a
8% chance, Genre 5 - alt/indie/folk has a 26% chance, Genre 6 -
country/rock has a 16% of being the the genre.