Site icon R-bloggers

Principal Components Analysis Shiny App

[This article was first published on imDEV » r-bloggers, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I’ve recently started experimenting with making Shiny apps, and today I wanted to make a basic app for calculating and visualizing principal components analysis (PCA). Here is the basic interface I came up with. Test drive the app for yourself using the code below or  check out the the R code HERE.

library(shiny)
runGist("5846650")

Above is an example of the user interface which consists of  data upload (from.csv for now), and options for conducting PCA using the  pcaMethods package. The various outputs include visualization of the eigenvalues and cross-validated eigenvalues (q2), which are helpful for selecting the optimal number of model components.The PCA scores plot can be used to evaluate extreme (leverage) or moderate (DmodX) outliers. A Hotelling’s T-squared confidence intervals as an ellipse would also be a good addition for this.

The variable loadings can be used to evaluate the effects of data scaling and other pre-treatments.

The next step is to interface the calculation of PCA to a dynamic plot which can be used to map data to plotting characteristics.


To leave a comment for the author, please follow the link and comment on their blog: imDEV » r-bloggers.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.