Ansible
PS DSC
Downloads:
106,858
Downloads of v 4.4.2:
1,510
Last Update:
01 Nov 2024
Package Maintainer(s):
Software Author(s):
- R Core Team
Tags:
r r-project r-base admin statistics programming data-analysis programming-language mathematics data-mining statistical-analysis statistical data-acquisition statistical-graphics data-automation
The R Project for Statistical Computing
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4.4.2 | Updated: 01 Nov 2024
Downloads:
106,858
Downloads of v 4.4.2:
1,510
Maintainer(s):
Software Author(s):
- R Core Team
The R Project for Statistical Computing 4.4.2
Legal Disclaimer: Neither this package nor Chocolatey Software, Inc. are affiliated with or endorsed by R Core Team. The inclusion of R Core Team trademark(s), if any, upon this webpage is solely to identify R Core Team goods or services and not for commercial purposes.
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All Checks are Passing
3 Passing Tests
Deployment Method: Individual Install, Upgrade, & Uninstall
To install The R Project for Statistical Computing, run the following command from the command line or from PowerShell:
To upgrade The R Project for Statistical Computing, run the following command from the command line or from PowerShell:
To uninstall The R Project for Statistical Computing, run the following command from the command line or from PowerShell:
Deployment Method:
This applies to both open source and commercial editions of Chocolatey.
1. Enter Your Internal Repository Url
(this should look similar to https://community.chocolatey.org/api/v2/)
2. Setup Your Environment
1. Ensure you are set for organizational deployment
Please see the organizational deployment guide
2. Get the package into your environment
Option 1: Cached Package (Unreliable, Requires Internet - Same As Community)-
Open Source or Commercial:
- Proxy Repository - Create a proxy nuget repository on Nexus, Artifactory Pro, or a proxy Chocolatey repository on ProGet. Point your upstream to https://community.chocolatey.org/api/v2/. Packages cache on first access automatically. Make sure your choco clients are using your proxy repository as a source and NOT the default community repository. See source command for more information.
- You can also just download the package and push it to a repository Download
-
Open Source
-
Download the package:
Download - Follow manual internalization instructions
-
-
Package Internalizer (C4B)
-
Run: (additional options)
choco download r --internalize --source=https://community.chocolatey.org/api/v2/ -
For package and dependencies run:
choco push --source="'INTERNAL REPO URL'" - Automate package internalization
-
Run: (additional options)
3. Copy Your Script
choco upgrade r -y --source="'INTERNAL REPO URL'" [other options]See options you can pass to upgrade.
See best practices for scripting.
Add this to a PowerShell script or use a Batch script with tools and in places where you are calling directly to Chocolatey. If you are integrating, keep in mind enhanced exit codes.
If you do use a PowerShell script, use the following to ensure bad exit codes are shown as failures:
choco upgrade r -y --source="'INTERNAL REPO URL'"
$exitCode = $LASTEXITCODE
Write-Verbose "Exit code was $exitCode"
$validExitCodes = @(0, 1605, 1614, 1641, 3010)
if ($validExitCodes -contains $exitCode) {
Exit 0
}
Exit $exitCode
- name: Install r
win_chocolatey:
name: r
version: '4.4.2'
source: INTERNAL REPO URL
state: present
See docs at https://docs.ansible.com/ansible/latest/modules/win_chocolatey_module.html.
chocolatey_package 'r' do
action :install
source 'INTERNAL REPO URL'
version '4.4.2'
end
See docs at https://docs.chef.io/resource_chocolatey_package.html.
cChocoPackageInstaller r
{
Name = "r"
Version = "4.4.2"
Source = "INTERNAL REPO URL"
}
Requires cChoco DSC Resource. See docs at https://github.com/chocolatey/cChoco.
package { 'r':
ensure => '4.4.2',
provider => 'chocolatey',
source => 'INTERNAL REPO URL',
}
Requires Puppet Chocolatey Provider module. See docs at https://forge.puppet.com/puppetlabs/chocolatey.
4. If applicable - Chocolatey configuration/installation
See infrastructure management matrix for Chocolatey configuration elements and examples.
This package was approved as a trusted package on 02 Nov 2024.
Introduction to R
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.
R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.
The R environment
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
- an effective data handling and storage facility,
- a suite of operators for calculations on arrays, in particular matrices,
- a large, coherent, integrated collection of intermediate tools for data analysis,
- graphical facilities for data analysis and display either on-screen or on hardcopy, and
- a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
- The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.
R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.
Many users think of R as a statistics system. We prefer to think of it of an environment within which statistical techniques are implemented.
R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.
R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.
Package Parameters
/UseInf:- Change the inno setup configuration file to use/save when installing
EXAMPLE
choco install r --params "'/UseInf:C:\r.inf'"
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- R.4.4.2.nupkg (bde9e84127e7) - ## / 64
In cases where actual malware is found, the packages are subject to removal. Software sometimes has false positives. Moderators do not necessarily validate the safety of the underlying software, only that a package retrieves software from the official distribution point and/or validate embedded software against official distribution point (where distribution rights allow redistribution).
Chocolatey Pro provides runtime protection from possible malware.
Copyright (C) 2016 The R Foundation for Statistical Computing
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- r.project (= 4.4.2)
Ground Rules:
- This discussion is only about The R Project for Statistical Computing and the The R Project for Statistical Computing package. If you have feedback for Chocolatey, please contact the Google Group.
- This discussion will carry over multiple versions. If you have a comment about a particular version, please note that in your comments.
- The maintainers of this Chocolatey Package will be notified about new comments that are posted to this Disqus thread, however, it is NOT a guarantee that you will get a response. If you do not hear back from the maintainers after posting a message below, please follow up by using the link on the left side of this page or follow this link to contact maintainers. If you still hear nothing back, please follow the package triage process.
- Tell us what you love about the package or The R Project for Statistical Computing, or tell us what needs improvement.
- Share your experiences with the package, or extra configuration or gotchas that you've found.
- If you use a url, the comment will be flagged for moderation until you've been whitelisted. Disqus moderated comments are approved on a weekly schedule if not sooner. It could take between 1-5 days for your comment to show up.