Principal Components Analysis Shiny App

June 23, 2013
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(This article was first published on imDEV » r-bloggers, and kindly contributed to R-bloggers)

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")

dataAbove 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.scree plotThe 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.

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

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


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