Revolutions

R 3.6.1 is now available

On July 5, the R Core Group released the source code for the latest update to R, R 3.6.1, and binaries are now available to download for Windows, Linux and Mac from your local CRAN mirror.

R 3.6.1 is a minor update to R that fixes a few bugs. As usual with a minor release, this version is backwards-compatible with R 3.6.0 and remains compatible with your installed packages. Nonetheless, an upgrade is recommended to address issues including:

• A fix for "lock errors" when attempting to re-install packages on Windows
• Efficiency improvements and other fixes when using "quasi" links with Generalized Linear Models
• Fixed axis labels for boxplots
• writeClipboard on Windows now supports Unicode, so you can copy text with special characters from R for pasting

See the release notes for the complete list.

By long tradition the code name for this release, "Action of the Toes", is likely a reference to the Peanuts comic. If you know specifically which one, let us know in the comments!

R-announce mailing list: R 3.6.1 is released

What's new in R 3.6.0

A major update to the open-source R language, R 3.6.0, was released on April 26 and is now available for download for Windows, Mac and Linux. As a major update, it has many new features, user-visible changes and bug fixes. You can read the details in the release announcement, and in this blog post I'll highlight the most significant ones.

Changes to random number generation. R 3.6.0 changes the method used to generate random integers in the sample function. In prior versions, the probability of generating each integer could vary from equal by up to 0.04% (or possibly more if generating more than a million different integers). This change will mainly be relevant to people who do large-scale simulations, and it also means that scripts using the sample function will generate different results in R 3.6.0 than they did in prior versions of R. If you need to keep the results the same (for reproducibility or for automated testing), you can revert to the old behavior by adding RNGkind(sample.kind="Rounding")) to the top of your script.

Changes to R's serialization format. If you want to save R data to disk with R 3.6.0 and read it back with R version 3.4.4 or earlier, you'll need to use readRDS("mydata.Rd",version=2) to save your data in the old serialization format, which has been updated to Version 3 for this release. (The same applies to the functions save, serialize, and byte-compiled R code.)  The R 3.5 series had forwards-compatibility in mind, and can already read data serialized in the Version 3 format.

Improvements to base graphics. You now have more options the appearance of axis labels (and perpendicular labels no longer overlap), better control over text positioning, a formula specification for barplots, and color palettes with better visual perception.

Improvements to package installation and loading, which should eliminate problems with partially-installed packages and reduce the space required for installed packages.

More functions now support vectors with more than 2 billion elements, including which and pmin/pmax.

Various speed improvements to functions including outer, substring, stopifnot, and the $ operator for data frames.

Improvements to statistical functions, including standard errors for T tests and better influence measures for multivariate models.

R now uses less memory, thanks to improvements to functions like drop, unclass and seq to use the ALTREP system to avoid duplicating data.

More control over R's memory usage, including the ability limit the amount of memory R will use for data. (Attempting to exceed this limit will generate a "cannot allocate memory" error.) This is particularly useful when R is being used in production, to limit the impact of a wayward R process on other applications in a shared system.

Better consistency between platforms. This has been an ongoing process, but R now has fewer instances of functions (or function arguments) that are only available on limited platforms (e.g. on Windows but not on Linux). The documentation is now more consistent between platforms, too. This should mean fewer instances of finding R code that doesn't run on your machine because it was written on a different platform.

There are many other specialized changes as well, which you can find in the release notes. Other than the issues raised above, most code from prior versions of R should run fine in R 3.6.0, but you will need to re-install any R packages you use, as they won't carry over from your R 3.5.x installation. Now that a couple of weeks have passed since the release, most packages should be readily available on CRAN for R 3.6.0.

As R enters its 20th year of continuous stable releases, please do take a moment to reflect on the ongoing commitment of the R Core Team for their diligence in improving the R engine multiple times a year. Thank you to everyone who has contributed. If you'd like to support the R project, here's some information on contributing to the R Foundation.

Final note: the code name for R 3.6.0 is "Planting of a Tree". The R code-names generally refer to Peanuts comics: if anyone can identify what the R 3.6.0 codename is referring to, please let us know in the comments! Update: Thanks for the comments! The consensus seems to be that the name refers to Arbor Day, which coincided with R 3.6.0's release day of April 26.

Ah, the "It's Arbor Day, Charlie Brown!" origin strip…https://t.co/aOdAqZBKaE pic.twitter.com/VrSCFaU74h

— Mara Averick (@dataandme) May 10, 2019

R-announce mailing list: R 3.6.0 is released

Availability of Microsoft R Open 3.5.2 and 3.5.3

It's taken a little bit longer than usual, but Microsoft R Open 3.5.2 (MRO) is now available for download for Windows and Linux. This update is based on R 3.5.2, and accordingly fixes a few minor bugs compared to MRO 3.5.1. The main change you will note is that new CRAN packages released since R 3.5.1 can now be used with this version of MRO.

Microsoft R Open 3.5.3, based on R 3.5.3, will be available next week, on May 10. Microsoft R Open 3.6.0, based on the recently-released R 3.6.0, is currently under development, and we'll make an announcement here when it's available too.

One thing to note: as of version 3.5.2 Microsoft R Open is no longer distributed for MacOS systems. If you want to continue to take advantage of the Accelerate framework on MacOS for, it's not too difficult to tweak the CRAN binary for MacOS to enable multi-threaded computing, and the additional open source bundled packages (like checkpoint and iterators) are available to install from CRAN or GitHub. 

As always, we hope you find Microsoft R Open useful, and if you have any comments or questions please visit the Microsoft R Open forum. You can follow the development of Microsoft R Open at the MRO Github repository. To download Microsoft R Open, simply follow the link below.

MRAN: Download Microsoft R Open

R 3.5.3 now available

The R Core Team announced yesterday the release of R 3.5.3, and updated binaries for Windows and Linux are now available (with Mac sure to follow soon). This update fixes three minor bugs (to the functions writeLines, setClassUnion, and stopifnot), but you might want to upgrade just to avoid the "package built under R 3.5.4" warnings you might get for new CRAN packages in the future.

R releases typically reference the Peanuts cartoon, but the code-name for this release, "Great Truth", is somewhat of a mystery. There may be a clue in the release date, March 11. Anyone got any ideas?

For more details on this latest update to the R language, check out the announcement below. And as always, thanks to the members of the R Core Team for their contributions to all R users with the R project.

R-announce mailing list: R 3.5.3 is released

 

R 3.5.2 now available

R 3.5.2, the latest version of the R language for statistical computation and graphics from the R Foundation, was released today. (This release is codenamed "Eggshell Igloo", likely in reference to this or this Peanuts cartoon.) Compared to R 3.5.1, this update includes only bug fixes, so R scripts and packages compatible with R 3.5.0 or R 3.5.1 should work without modification.

Installers for R on Windows and Debian Linux are available now; other platforms should be available from CRAN within the next few days. If you want to see the full list of fixes, check out the announcement from the R Core Group at the link below.

R-announce mailing list: R 3.5.2 is released

AzureR: R packages to control Azure services

by Hong Ooi, senior data scientist, Microsoft Azure

This post is to announce a new family of packages we have developed as part of the CloudyR project for talking to Azure from R: AzureR.

As background, some of you may remember the AzureSMR package, which was written a few years back as an R interface to Azure. AzureSMR was very successful and gained a significant number of users, but it was never meant to be maintainable in the long term. As more features were added it became more unwieldy until its design limitations became impossible to ignore.

The AzureR family is a refactoring/rewrite of AzureSMR that aims to fix the earlier package's shortcomings.

The core package of the family is AzureRMR, which provides a lightweight yet powerful interface to Azure Resource Manager. It handles authentication (including automatically renewing when a session token expires), managing resource groups, and working with individual resources and templates. It also calls the Resource Manager REST API directly, so you don't need to have PowerShell or Python installed; it depends only on commonly used R packages like httr, jsonlite and R6.

Here's what some code using AzureRMR looks like. Note that it uses R6 classes to represent Azure objects, hence the use of $ to call methods.

library(AzureRMR)

az <- az_rm$new(tenant="{tenant_id}",
    app="{app_id}",
    password="{password}")

# get a subscription and resource group
sub <- az$get_subscription("{subscription_id}")
rg <- sub$get_resource_group("rgName")

# get a resource (storage account)
stor <- rg$get_resource(type="Microsoft.Storage/storageAccounts",
    name="myStorage")

# method chaining works too
stor <- az$
    get_subscription("{subscription_id}")$
    get_resource_group("rgName")$
    get_resource(type="Microsoft.Storage/storageAccounts",
        name="myStorage")

# create a new resource group and resource
rg2 <- sub$create_resource_group("newRgName", location="westus")

stor2 <- rg2$create_resource(type="Microsoft.Storage/storageAccounts",
    name="myNewStorage",
    kind="Storage", 
    sku=list(name="Standard_LRS", tier="Standard"))

# delete them
stor2$delete(confirm=FALSE)
rg2$delete(confirm=FALSE)

You can use AzureRMR to work with any Azure service, but a key idea behind it is that it's extensible: you can write new packages that provide extra functionality relevant to specific Azure offerings. This deals with one of the main shortcomings of AzureSMR: as a monolithic package, it became steadily more difficult to add new features as time went on. The packages that extend AzureRMR in this way constitute the AzureR family.

Currently, the AzureR family includes the following packages:

• AzureVM is a package for working with virtual machines and virtual machine clusters. It allows you to easily deploy, manage and delete a VM from R. A number of templates are provided based on the Data Science Virtual Machine, or you can also supply your own template.
• AzureStor is a package for working with storage accounts. In addition to a Resource Manager interface for creating and deleting storage accounts, it also provides a client interface for working with the storage itself. You can upload and download files and blobs, list file shares and containers, list files within a share, and so on. It supports authenticated access to storage via either a key or a shared access signature (SAS).
• AzureContainers is a package for working with containers in Azure: specifically, it provides an interface to Azure Container Instances, Azure Container Registry and Azure Kubernetes Service. You can easily create an image, push it to an ACR repo, and then deploy it as a service to AKS. It also provides lightweight shells to docker, kubectl and helm (assuming these are installed).

I'll be writing future blog posts to describe each of these in further detail.

All the AzureR packages are part of the CloudyR Project, which aims to make R work better with cloud services of all kinds. They are not (yet) on CRAN, but you can obtain them with devtools::install_github.

library(devtools)
install_github("cloudyr/AzureRMR")
install_github("cloudyr/AzureVM")
install_github("cloudyr/AzureStor")
install_github("cloudyr/AzureContainers")

If you use Azure with R, I hope you'll find these packages to be of significant benefit to your workflow. If you have any questions or comments, please contact me at [email protected].

Ed Note: This is the first post in a series. For more detail on the individual packages, here are links to the followup posts:

• AzureRMR: an R interface to Azure Resource Manager
• AzureVM: managing virtual machines in Azure
• AzureContainers: deploy a predictive service with containers
• AzureStor: an R package for working with Azure storage

Microsoft R Open 3.5.1 now available

Microsoft R Open 3.5.1 has been released, combining the latest R language engine with multi-processor performance and tools for managing R packages reproducibly. You can download Microsoft R Open 3.5.1 for Windows, Mac and Linux from MRAN now. Microsoft R Open is 100% compatible with all R scripts and packages, and works with all your favorite R interfaces and development environments.

This update brings a number of minor fixes to the R language engine from the R core team. It also makes available a host of new R packages contributed by the community, including packages for downloading financial data, connecting with analytics systems, applying machine learning algorithms and statistical models, and many more. New R packages are released every day, and you can access packages released after the 1 August 2018 CRAN snapshot used by MRO 3.5.1 using the checkpoint package.

We hope you find Microsoft R Open useful, and if you have any comments or questions please visit the Microsoft R Open forum. You can follow the development of Microsoft R Open at the MRO Github repository. To download Microsoft R Open, simply follow the link below.

MRAN: Download Microsoft R Open

R 3.5.1 update now available

Last week the R Core Team released the latest update to the R statistical data analysis environment, R version 3.5.1. This update (codenamed "Feather Spray" — a Peanuts reference) makes no user-visible changes and fixes a few bugs. It is backwards-compatible with R 3.5.0, and users can find updates for Windows, Linux and Mac systems at their local CRAN mirror. (The update to Microsoft R Open featuring the R 3.5.1 engine is scheduled for release on August 29.)

The complete list of fixes to R 3.5.1 is included in the release announcement, found at the link below. 

R-announce mailing list: R 3.5.1 is released

Microsoft R Open 3.5.0 now available

Microsoft R Open 3.5.0 is now available for download for Windows, Mac and Linux. This update includes the open-source R 3.5.0 engine, which is a major update with many new capabilities and improvements to R. In particular, it includes a major new framework for handling data in R, with some major behind-the-scenes performance and memory-use benefits (and with further improvements expected in the future).

Microsoft R Open 3.5.0 points to a fixed CRAN snapshot taken on June 1 2018. This provides a reproducible experience when installing CRAN packages by default, but you always change the default CRAN repository or the built-in checkpoint package to access snapshots of packages from an earlier or later date.

Relatedly, many new packages have been released since the last release of Microsoft R Open, and you can browse a curated list of some interesting ones on the Microsoft R Open Package Spotlight page.

We hope you find Microsoft R Open useful, and if you have any comments or questions please visit the Microsoft R Open forum. You can follow the development of Microsoft R Open at the MRO Github repository. To download Microsoft R Open, simply follow the link below.

MRAN: Download Microsoft R Open

New round of R Consortium grants announced

The R Consortium has just announced its latest round of project grants. After reviewing the proposals submitted by the R community, the Infrastructure Steering Committee has elected to fund the following projects for the Spring 2018 season:

• Further updates to the DBI package, to provide a consistent interface between R and databases
• Updating the infrastructure in R for building binary packages on Windows and Mac
• A collaboration with Statisticians in the Pharmaceutical Industry to validate R packages to regulatory standards
• A project to validate and maintain some of the historic numerical algorithms used by R 
• A working group to facilitate working with US census data in R
• Consolidation of tools and workflows for handling missing data in R
• Templates and tools for the development of teaching resources with R

This latest round of grants brings the total funding for projects proposed by R community members to more than $650,000. The R Consortium itself is funded by its members (including my own employer, Microsoft), so if you'd like to see more such projects why not ask your own employer to become a member? 

R Consortium: Announcing the R Consortium ISC Funded Project grant recipients for Spring 2018