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Tag: Packages

Changes in the foreach package
by Hong Ooi, Senior Data Scientist at Microsoft and maintainer of the foreach package This post is to announce some new and upcoming changes in the foreach package. First, foreach can now be found on GitHub! The repository is at https://github.com/RevolutionAnalytics/foreach, replacing its old home on R-Forge. Right now the …
AzureR updates: AzureStor, AzureVM, AzureGraph, AzureContainers
Some major updates to AzureR packages this week! As well as last week's AzureRMR update, there are changes to AzureStor, AzureVM, AzureGraph and AzureContainers. All of these are live on CRAN. AzureStor 3.0.0 There are substantial enhancements to multiple-file transfers (up and down). You can supply a vector of pathnames …
AzureVM update: flexible and powerful deployment and management of VMs in Azure
by Hong Ooi, senior data scientist, Microsoft Azure I'm happy to announce version 2.0 of AzureVM, a package for deploying and managing virtual machines in Azure. This is a complete rewrite of the package, with the objective of making it a truly generic and flexible tool for working with VMs …
Fun with R and the Noops
Earlier this week, Github introduced Noops, a collection of simple black-box machines with API endpoints, with the goal of challenging developers of all skill levels to solve problems with them. Five "Noops" machines have been released so far along with challenges suitable for beginner programmers, with 15 further machines (and …
AzureR and AzureKeyVault
by Hong Ooi, senior data scientist, Microsoft Azure Just a couple of announcements regarding my family of packages for working with Azure from R. First, the packages have moved from the cloudyr org on GitHub to the Azure org, thus making them "official". A (rather spartan) homepage is here, containing …
Use foreach with HPC schedulers thanks to the future package
The future package is a powerful and elegant cross-platform framework for orchestrating asynchronous computations in R. It's ideal for working with computations that take a long time to complete; that would benefit from using distributed, parallel frameworks to make them complete faster; and that you'd rather not have locking up …
AzureStor: an R package for working with Azure storage
by Hong Ooi, senior data scientist, Microsoft Azure A few weeks ago, I introduced the AzureR family of packages for working with Azure in R. Since then, I’ve also written articles on how to use AzureRMR to interact with Azure Resource Manager, how to use AzureVM to manage virtual machines, …
3-D shadow maps in R: the rayshader package
Data scientists often work with geographic data that needs to be visualized on a map, and sometimes the maps themselves are the data. The data is often located in two-dimensional space (latitude and longitude), but for some applications we have a third dimension as well: elevation. We could represent the …
3-D shadow maps in R: the rayshader package
Data scientists often work with geographic data that needs to be visualized on a map, and sometimes the maps themselves are the data. The data is often located in two-dimensional space (latitude and longitude), but for some applications we have a third dimension as well: elevation. We could represent the …
Using gganimate to illustrate the luminance illusion
Many illusions are based on the fact that our perceptions of color or brightness of an object are highly dependent on the background surrounding the object. For example, in this image (an example of the Cornsweet illusion) the upper and lower blocks are exactly the same color, according to the …
Make R speak
Every wanted to make R talk to you? Now you can, with the mscstts package by John Muschelli. It provides an interface to the Microsoft Cognitive Services Text-to-Speech API (hence the name) in Azure, and you can use it to convert any short piece of text to a playable audio …
Interpreting machine learning models with the lime package for R
Many types of machine learning classifiers, not least commonly-used techniques like ensemble models and neural networks, are notoriously difficult to interpret. If the model produces a surprising label for any given case, it's difficult to answer the question, "why that label, and not one of the others?". One approach to …