How to Install and Uninstall R-core.x86_64 Package on Rocky Linux 9
Last updated: November 25,2024
1. Install "R-core.x86_64" package
Here is a brief guide to show you how to install R-core.x86_64 on Rocky Linux 9
$
sudo dnf update
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$
sudo dnf install
R-core.x86_64
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2. Uninstall "R-core.x86_64" package
Please follow the guidelines below to uninstall R-core.x86_64 on Rocky Linux 9:
$
sudo dnf remove
R-core.x86_64
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$
sudo dnf autoremove
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3. Information about the R-core.x86_64 package on Rocky Linux 9
Last metadata expiration check: 0:50:53 ago on Fri Feb 16 06:49:52 2024.
Available Packages
Name : R-core
Version : 4.3.2
Release : 1.el9
Architecture : x86_64
Size : 62 M
Source : R-4.3.2-1.el9.src.rpm
Repository : epel
Summary : The minimal R components necessary for a functional runtime
URL : https://www.r-project.org
License : GPL-2.0-or-later
Description : A language and environment for statistical computing and graphics.
: R is similar to the award-winning S system, which was developed at
: Bell Laboratories by John Chambers et al. It provides a wide
: variety of statistical and graphical techniques (linear and
: nonlinear modelling, statistical tests, time series analysis,
: classification, clustering, ...).
:
: R is designed as a true computer language with control-flow
: constructions for iteration and alternation, and it allows users to
: add additional functionality by defining new functions. For
: computationally intensive tasks, C, C++ and Fortran code can be linked
: and called at run time.
Available Packages
Name : R-core
Version : 4.3.2
Release : 1.el9
Architecture : x86_64
Size : 62 M
Source : R-4.3.2-1.el9.src.rpm
Repository : epel
Summary : The minimal R components necessary for a functional runtime
URL : https://www.r-project.org
License : GPL-2.0-or-later
Description : A language and environment for statistical computing and graphics.
: R is similar to the award-winning S system, which was developed at
: Bell Laboratories by John Chambers et al. It provides a wide
: variety of statistical and graphical techniques (linear and
: nonlinear modelling, statistical tests, time series analysis,
: classification, clustering, ...).
:
: R is designed as a true computer language with control-flow
: constructions for iteration and alternation, and it allows users to
: add additional functionality by defining new functions. For
: computationally intensive tasks, C, C++ and Fortran code can be linked
: and called at run time.