<p>A Shiny app for automated Principal Components Analysis (PCA) based on the R package <a href="http://factominer.free.fr/index.html">factominer</a>. An interpretation in plain English and the R Code to reproduce the results is included in the report. Follow the Instructions on the webpage of the app <a href="https://statsomat.shinyapps.io/Principal-components-analysis/">https://statsomat.shinyapps.io/Principal-components-analysis/</a> to generate the report. Check also the GitHub repository <a href="https://github.com/Statsomat/PCA">https://github.com/Statsomat/PCA</a>.</p>

Shiny apps for automated data analysis, annotated outputs and human-readable interpretation in natural language. Designed especially for learners and applied researchers. Currently available methods: EDA, EDA with Python, Correlation Analysis, Principal Components Analysis, Confirmatory Factor Analysis.

Denise Welsch

Statsomat

Shiny Apps for Automated Data Analysis and Automated
Interpretation

Berit Hunsdieck

Omar Alhelal

pca function

<p>A Shiny app for automated Principal Components Analysis (PCA) based on the R package <a href='http://factominer.free.fr/index.html'>factominer</a>. An interpretation in plain English and the R Code to reproduce the results is included in the report. Follow the Instructions on the webpage of the app <a href='https://statsomat.shinyapps.io/Principal-components-analysis/'>https://statsomat.shinyapps.io/Principal-components-analysis/</a> to generate the report. Check also the GitHub repository <a href='https://github.com/Statsomat/PCA'>https://github.com/Statsomat/PCA</a>.</p>

pca: Principal Components Analysis

Description

Usage

Arguments

Value

Examples