Multivariate exploratory data analysis in R
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.
The latest version can be installed from GitHub as follows:
install.packages("devtools")
devtools::install_github("alexym1/booklet")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 0.006586116Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.