Yu Carl

https://yucarl.com/2021/10/150/

Today, I am going to compare three dimensionality reduction methods, they are PCA (Principle Component), PLS (Partial least squares) and UMAP(Uniform manifold approximation and projection). The dataset we use today is Billboard top 100 songs, comes from #TidyTuesday.

Explore data

Our goal is to reduce the dimensionality of the features of Billboard Top 100 songs, connecting the positions of the songs with mostly audio features available from Spotify.

library(tidyverse)

## billboard ranking data
billboard <- readr::read_csv("billboard.csv")

## spotify feature data
audio_features <- readr::read_csv("audio_features.csv")

Using the data.table package to import .csv files is faster, but Billboard Top 100 songs dataset is not very large, so readr is sufficient.

	url	week_id	week_position	song	performer	song_id	instance	previous_week_position	peak_position	weeks_on_chart
1	http://www.billboard.com/charts/hot-100/1965-07-17	7/17/1965	34.00	Don’t Just Stand There	Patty Duke	Don’t Just Stand TherePatty Duke	1.00	45.00	34.00	4.00
2	http://www.billboard.com/charts/hot-100/1965-07-24	7/24/1965	22.00	Don’t Just Stand There	Patty Duke	Don’t Just Stand TherePatty Duke	1.00	34.00	22.00	5.00
3	http://www.billboard.com/charts/hot-100/1965-07-31	7/31/1965	14.00	Don’t Just Stand There	Patty Duke	Don’t Just Stand TherePatty Duke	1.00	22.00	14.00	6.00
4	http://www.billboard.com/charts/hot-100/1965-08-07	8/7/1965	10.00	Don’t Just Stand There	Patty Duke	Don’t Just Stand TherePatty Duke	1.00	14.00	10.00	7.00
5	http://www.billboard.com/charts/hot-100/1965-08-14	8/14/1965	8.00	Don’t Just Stand There	Patty Duke	Don’t Just Stand TherePatty Duke	1.00	10.00	8.00	8.00
6	http://www.billboard.com/charts/hot-100/1965-08-21	8/21/1965	8.00	Don’t Just Stand There	Patty Duke	Don’t Just Stand TherePatty Duke	1.00	8.00	8.00	9.00