Multivariate Exploratory Data Analysis

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) when variables are categorical, Multiple Factor Analysis (MFA) when variables are structured in groups.

booklet

Multivariate exploratory data analysis in R

Overview

booklet is a ground-up rewrite of FactoMineR that provides a set of functions for multivariate exploratory data analysis. It is designed to be a more user-friendly version of FactoMineR. The main goal was to make the package more intuitive and easier to use. The package is still under development, and some functions are not yet implemented. However, the main functions are already available.

Installation

The latest version can be installed from GitHub as follows:

install.packages("devtools")
devtools::install_github("alexym1/booklet")

Example

library(booklet)

# Get active individuals
X_active <- pca_standardize_norm(iris[, -5])
head(X_active)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1   -0.8976739  1.01560199    -1.335752   -1.311052
#> 2   -1.1392005 -0.13153881    -1.335752   -1.311052
#> 3   -1.3807271  0.32731751    -1.392399   -1.311052
#> 4   -1.5014904  0.09788935    -1.279104   -1.311052
#> 5   -1.0184372  1.24503015    -1.335752   -1.311052
#> 6   -0.5353840  1.93331463    -1.165809   -1.048667

# Get eigs
eigs <- pca_eigen(X_active)
eigs$values
#> [1] 434.856175 136.190540  21.866774   3.086511

# Get principal components
ind_coords <- pca_ind_coords(eigs)
head(ind_coords)
#>       Dim.1      Dim.2       Dim.3        Dim.4
#> 1 -2.257141 -0.4784238  0.12727962  0.024087508
#> 2 -2.074013  0.6718827  0.23382552  0.102662845
#> 3 -2.356335  0.3407664 -0.04405390  0.028282305
#> 4 -2.291707  0.5953999 -0.09098530 -0.065735340
#> 5 -2.381863 -0.6446757 -0.01568565 -0.035802870
#> 6 -2.068701 -1.4842053 -0.02687825  0.006586116