How to Install and Uninstall r-cran-factominer Package on Ubuntu 21.10 (Impish Indri)

Last updated: November 26,2024

1. Install "r-cran-factominer" package

Learn how to install r-cran-factominer on Ubuntu 21.10 (Impish Indri)


    
        
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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 guidance below to uninstall r-cran-factominer on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-cran-factominer
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the r-cran-factominer package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-factominer
Architecture: all
Version: 2.4-1
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: 3734
Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-cran-car, r-cran-cluster, r-cran-dt, 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
Suggests: r-cran-markdown
Filename: pool/universe/r/r-cran-factominer/r-cran-factominer_2.4-1_all.deb
Size: 3750068
MD5sum: 9a15ea446bad8b33a6eaa38f51d6914b
SHA1: 038ee63096074c8c84eb3bdde991d0386bc9dfa3
SHA256: 90fc5a56d0e7ad220bd4fcc878b31b3efcdbbb1ffdd0fe913f5332feb5a023e6
SHA512: 923b94dfc5be7b7d8abd9ef7e99640f41b39712fe26d7a4ce31718cf86affd7ac7daae8b35e3e38aafb679cc89b9f1fa01cec5ddc91a9516cf29dffa8ba00868
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