How to Install and Uninstall r-cran-ggplot2 Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 14,2024

1. Install "r-cran-ggplot2" package

This tutorial shows how to install r-cran-ggplot2 on Ubuntu 20.10 (Groovy Gorilla)

$ sudo apt update $ sudo apt install r-cran-ggplot2

2. Uninstall "r-cran-ggplot2" package

Please follow the instructions below to uninstall r-cran-ggplot2 on Ubuntu 20.10 (Groovy Gorilla):

$ sudo apt remove r-cran-ggplot2 $ sudo apt autoclean && sudo apt autoremove

3. Information about the r-cran-ggplot2 package on Ubuntu 20.10 (Groovy Gorilla)

Package: r-cran-ggplot2
Architecture: all
Version: 3.3.2+dfsg-2
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: 6899
Depends: r-base-core (>= 4.0.2-1), r-api-4.0, r-cran-digest, r-cran-glue, r-cran-gtable (>= 0.1.1), r-cran-isoband, r-cran-mass, r-cran-mgcv, r-cran-rlang (>= 0.3.0), r-cran-scales (>= 0.5.0), r-cran-tibble, r-cran-withr (>= 2.0.0)
Recommends: r-cran-testthat (>= 2.1.0), r-cran-sp, r-cran-mapproj, r-cran-hexbin, r-cran-vdiffr (>= 0.3.0), r-cran-quantreg
Suggests: r-cran-covr, r-cran-dplyr, r-cran-hmisc, r-cran-knitr, r-cran-lattice, r-cran-maps, r-cran-maptools, r-cran-multcomp, r-cran-munsell, r-cran-nlme, r-cran-rcolorbrewer, r-cran-rmarkdown, r-cran-rpart, r-cran-sf (>= 0.7-3), r-cran-svglite (>= 1.2.0.9001)
Filename: pool/universe/r/r-cran-ggplot2/r-cran-ggplot2_3.3.2+dfsg-2_all.deb
Size: 3123712
MD5sum: ea402a1442ab75aba51cb76d2612d44d
SHA1: 54d08cc3212389c9d8d14d9205aa50f035720a64
SHA256: 9700964e3c84090a7fdc2451dc9b6285e6b2544240bc4347dfaf1b4b30746ebe
SHA512: 73f9bb560cf94ae5dbf4757e0193d5fcb9dd98a423f921397dbf6a20e4584837d7740995ae4eb3dfb4c843017721f5d91cc36eefa0dcd8a8989005d542735017
Homepage: https://cran.r-project.org/package=ggplot2
Description-en: implementation of the Grammar of Graphics
ggplot2 combines the advantages of both base and lattice graphics.
Conditioning and shared axes are handled automatically, and you can
still build up a plot step by step from multiple data sources. It
also implements a sophisticated multidimensional conditioning system
and a consistent interface to map data to aesthetic attributes.
Description-md5: c0bde8209df613291d395c4f0ceff7e5