Gathering and Organizing Data:
This data comes from a spreadsheet that cataloged our class’
favorite songs, and those songs’ artists/genres. Before importing the
data into R Studios I cleaned it up by correcting spelling mistakes and
removing irrelevant data such as the time that a song was submitted to
the Google Form we used to compile data.
I downloaded tidyverse after importing a clean .cvs file of my data,
and then I run a line of code to display the parts of it that are
relevent to the question I’m answering: how likely is it for each given
genre to be played?
install.packages("tidyverse")
## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.3'
## (as 'lib' is unspecified)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
df<-read_csv("Playlist - Clean.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.
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
On first glance this looks good. Now I’m going to make my
genre column a factor type column so that I can categorize by the
different Genres. Then I will look at the counts of each
genre.
df$Genre <- as.factor(df$Genre)
head(df)
## # A tibble: 6 × 3
## Genre Artist Song
## <fct> <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
df %>% count(Genre)
## # A tibble: 6 × 2
## 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
This is accurate, but it will be easier to read if I round it down
to the first two decimals, so I’ll do that.
genre_counts$prob <- round(genre_counts$prob, 2)
genre_counts
## # A tibble: 6 × 3
## Genre n prob
## <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
In conclusion: this table tells me that there is a 33% chance of
getting a Hip-Hop/Latin song, a 14% chance of getting a pop song, a 3%
chance of getting a House/EDM song, an 8% chance of getting an
R&B/Blues song, a 26% chance of getting an Alt/Indie song, and a 16%
chance of getting a country/rock song.