Jonny
```{r} df<-read_csv(“Class Playlist Clean DS4 - Sheet1.csv”)
First 5 genres of music
```{r}
head(df)
{r} df$Genre<-as.factor(df$Genre) df%>%count(Genre)
Based on this we can see that the majority of songs are
hiphop/rap
{r} df%>%count(Genre) genre_counts<-df%>% count(Genre)
Data saved for access later
{r} genre_counts <- genre_counts %>% mutate(prob = n/sum(n)) genre_counts
Probability
{r} genre_counts<-genre_counts%>%mutate(prob=n/sum(n)) genre_counts
{r} genre_counts$prob<-round(genre_counts$prob,2) genre_counts
This final table shows us the probability of any Genre of music
being played, the highest being Hiphop/rap with a 33% chance of being
played, and alt/indie/folk with a 26% chance. Lowest probability is
house/ska with only a 3% chance of being played.
We know this data is accurate because the numbers (.33, .14, .03,
.08, .26, and .16 add up to 1- 100%)