Interactive Principal Component Analysis in R
Written by Matt Dancho
This article is part of a R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks.
Identify Clusters in your Data:
We’ll make an Interactive PCA visualization to investigate clusters and learn why observations are similar to each other. Here are the links to get set up. 👇
PCA is all about data wrangling
PCA is a great tool for mining your data for clusters. But, most beginners get a few things wrong:
- PCA only works with numeric data
- Categorical data must be encoded as numeric data (e.g. one-hot)
- Numeric data must be scaled (otherwise your PCA will be misleading)
Data Wrangling is SUPER Critical
Full code in the video Github Repository
We need to use
dplyr to encode categorical features as numeric.
PCA will not work with Categorical Data
(You'll get a nice error message)
PCA likes data in this format 😊
What can we do with PCA + ggplot2? Let’s visualize clusters in our data!
First, fit a PCA using
autoplot() from the
Then visualize. As an added bonus, we can make it interactive with
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