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factoextra : Extract and Visualize the Results of Multivariate Data Analyses

Provides some easy-to-use functions to extract and visualize the output of multivariate data analyses, including PCA (Principal Component Analysis), CA (Correspondence Analysis), MCA (Multiple Correspondence Analysis), FAMD (Factor Analysis of Mixed Data), MFA (Multiple Factor Analysis) and HMFA (Hierarchical Multiple Factor Analysis) functions from several packages : PCA, CA, MCA, MFA, HMFA [FactoMineR]; prcomp and princomp [stats]; dudi.pca, dudi.coa, dudi.acm [ade4]; ca [ca]; corresp [MASS]; epPCA, epCA, epMCA [ExPosition]. It contains also many functions for simplifying clustering analysis workflows. The ggplot2 plotting system is used. See http://www.sthda.com/english/rpkgs/factoextra for more information, documentation and examples.

Installation

Install from CRAN as follow:

install.packages("factoextra")

Or, install the latest version from GitHub:

# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/factoextra")

Geting started

Find out more at http://www.sthda.com/english/wiki/factoextra-r-package

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Version

Install

install.packages('factoextra')

Monthly Downloads

102,866

Version

1.0.4

License

GPL-2

Maintainer

Alboukadel Kassambara

Last Published

January 9th, 2017

Functions in factoextra (1.0.4)

fviz_cluster

Visualize Clustering Results
facto_summarize

Subset and summarize the output of factor analyses
fviz_cos2

Visualize the quality of representation of rows/columns
eclust

Visual enhancement of clustering analysis
eigenvalue

Extract and visualize the eigenvalues/variances of dimensions
fviz_add

Add supplementary data to a plot
dist

Enhanced Distance Matrix Computation and Visualization
deprecated

Deprecated Functions
fviz_ca

Visualize Correspondence Analysis
fviz_contrib

Visualize the contributions of row/column elements
fviz_dend

Enhanced Visualization of Dendrogram
fviz_mca

Visualize Multiple Correspondence Analysis
fviz_mclust

Plot Model-Based Clustering Results using ggplot2
fviz_famd

Visualize Factor Analysis of Mixed Data
fviz_hmfa

Visualize Hierarchical Multiple Factor Analysis
fviz_ellipses

Draw confidence ellipses around the categories
hcut

Computes Hierarchical Clustering and Cut the Tree
hkmeans

Hierarchical k-means clustering
fviz_mfa

Visualize Multiple Factor Analysis
fviz_nbclust

Dertermining and Visualizing the Optimal Number of Clusters
get_hmfa

Extract the results for individuals/variables/group/partial axes - HMFA
get_clust_tendency

Assessing Clustering Tendency
get_mca

Extract the results for individuals/variables - MCA
get_famd

Extract the results for individuals and variables - FAMD
fviz

Visualizing Multivariate Analyse Outputs
get_ca

Extract the results for rows/columns - CA
poison

Poison
housetasks

House tasks contingency table
fviz_pca

Visualize Principal Component Analysis
fviz_silhouette

Visualize Silhouette Information from Clustering
get_pca

Extract the results for individuals/variables - PCA
get_mfa

Extract the results for individuals/variables/group/partial axes - MFA
print.factoextra

Print method for an object of class factoextra
decathlon2

Athletes' performance in decathlon
multishapes

A dataset containing clusters of multiple shapes