{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE)
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{r} install.packages("tidyverse")
{r} library(tidyverse)
{r} df <- read_csv("DS5 Table - Sheet1.csv")
```{r} gf <- df %>% count(Genre)
```{r}
print (gf){r} gf %>% mutate(probability = n/10) My song genres
are not representative of the song genres in my playlist, as the
playlist had 17% rnb/soul but I ended up getting 50% rnb/soul,and I got
no Indie or Latin. Country/rock, which had the most songs, had the least
of the genres present this time. The probabilities could be different
just because they happened to be this time, or the data could have
changed since I took the %s last time, making its results invalid and
requiring me to redo it. We could make them similar by having larger
trials. This relates to the analyisis and synthesis step of the data
science process by showing what and how we are analyzing and how we can
incorporate it into the future