How to Install and Uninstall r-cran-factominer Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 03,2024

1. Install "r-cran-factominer" package

This guide covers the steps necessary to install r-cran-factominer on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-factominer
            
                    
    
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2. Uninstall "r-cran-factominer" package

Please follow the instructions below to uninstall r-cran-factominer on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                r-cran-factominer
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-factominer package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-factominer
Architecture: all
Version: 2.3-2build1
Priority: optional
Section: universe/gnu-r
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 3726
Depends: r-base-core (>= 4.0.0.20200528-1), r-api-4.0, r-cran-car, r-cran-cluster, r-cran-ellipse, r-cran-flashclust, r-cran-lattice, r-cran-leaps, r-cran-mass, r-cran-scatterplot3d, r-cran-ggplot2, r-cran-ggrepel
Recommends: r-cran-knitr
Filename: pool/universe/r/r-cran-factominer/r-cran-factominer_2.3-2build1_all.deb
Size: 3742356
MD5sum: 07a5a2710842b8f9b988759fd674b831
SHA1: de28790ff9c5da6dec7b13e5e8ae7921fa4dd7a7
SHA256: 946189c2647d7ba42857174ff27efe0eae547a1c1f7e322a43966549432007ca
SHA512: 030c70669996ee3edd96bb0273d954897078dfcf5ff13c33f732408b850520d7291d9dc031cace5f34c1291f5567c796a39aa0396001e24a1ccb65ab0e9a0640
Homepage: https://cran.r-project.org/package=FactoMineR
Description-en: Multivariate Exploratory Data Analysis and Data Mining
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) and multiple correspondence analysis (MCA) when variables are
categorical, Multiple Factor Analysis when variables are structured in
groups, etc. and hierarchical cluster analysis.
Description-md5: ffde10a8a141621725979372058f5a00