Downloads:

24,730

Downloads of v 4.0.3:

4,451

Last Update:

10 Oct 2020

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

4.0.3 | Updated: 10 Oct 2020

Downloads:

24,730

Downloads of v 4.0.3:

4,451

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 4.0.3

All Checks are Passing

2 Passing Test

Validation Testing Passed

Verification Testing Passed

Details

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:

>

NOTE: This applies to both open source and commercial editions of Chocolatey.

1. Ensure you are set for organizational deployment

Please see the organizational deployment guide

  • 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://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

3. Enter your internal repository url

(this should look similar to https://chocolatey.org/api/v2)

4. Choose your deployment method:


choco upgrade r -y --source="'STEP 3 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="'STEP 3 URL'"
$exitCode = $LASTEXITCODE

Write-Verbose "Exit code was $exitCode"
$validExitCodes = @(0, 1605, 1614, 1641, 3010)
if ($validExitCodes -contains $exitCode) {
  Exit 0
}

Exit $exitCode


- name: Ensure r installed
  win_chocolatey:
    name: r
    state: present
    version: 4.0.3
    source: STEP 3 URL

See docs at https://docs.ansible.com/ansible/latest/modules/win_chocolatey_module.html.

Coming early 2020! Central Managment Reporting available now! More information...


chocolatey_package 'r' do
  action    :install
  version  '4.0.3'
  source   'STEP 3 URL'
end

See docs at https://docs.chef.io/resource_chocolatey_package.html.


Chocolatey::Ensure-Package
(
    Name: r,
    Version: 4.0.3,
    Source: STEP 3 URL
);

Requires Otter Chocolatey Extension. See docs at https://inedo.com/den/otter/chocolatey.


cChocoPackageInstaller r
{
   Name     = 'r'
   Ensure   = 'Present'
   Version  = '4.0.3'
   Source   = 'STEP 3 URL'
}

Requires cChoco DSC Resource. See docs at https://github.com/chocolatey/cChoco.


package { 'r':
  provider => 'chocolatey',
  ensure   => '4.0.3',
  source   => 'STEP 3 URL',
}

Requires Puppet Chocolatey Provider module. See docs at https://forge.puppet.com/puppetlabs/chocolatey.


salt '*' chocolatey.install r version="4.0.3" source="STEP 3 URL"

See docs at https://docs.saltstack.com/en/latest/ref/modules/all/salt.modules.chocolatey.html.

5. If applicable - Chocolatey configuration/installation

See infrastructure management matrix for Chocolatey configuration elements and examples.

This package was approved as a trusted package on 11 Oct 2020.

Description

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'"

Log in or click on link to see number of positives.

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.

Version Downloads Last Updated Status
The R Project for Statistical Computing 4.0.3 4451 Saturday, October 10, 2020 Approved
The R Project for Statistical Computing 4.0.2 2505 Monday, June 22, 2020 Approved
The R Project for Statistical Computing 4.0.1 560 Saturday, June 6, 2020 Approved
The R Project for Statistical Computing 4.0.0 1494 Friday, April 24, 2020 Approved
The R Project for Statistical Computing 3.6.3 2293 Saturday, February 29, 2020 Approved
The R Project for Statistical Computing 3.6.2 2970 Thursday, December 12, 2019 Approved
The R Project for Statistical Computing 3.6.1 2601 Saturday, July 6, 2019 Approved
The R Project for Statistical Computing 3.6.0 1379 Friday, April 26, 2019 Approved
The R Project for Statistical Computing 3.5.3 484 Monday, March 11, 2019 Approved
The R Project for Statistical Computing 3.5.2 1420 Thursday, December 20, 2018 Approved
Discussion for the The R Project for Statistical Computing Package

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