https://github.com/yueguoguo/r-on-azure

Compilation of R packages and tools for doing data science and AI on Azure cloud
https://github.com/yueguoguo/r-on-azure
Last synced: 4 months ago
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Compilation of R packages and tools for doing data science and AI on Azure cloud
Host: GitHub
URL: https://github.com/yueguoguo/r-on-azure
Owner: yueguoguo
Created: 2017-12-17T07:32:43.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2022-01-24T01:51:17.000Z (about 3 years ago)
Last Synced: 2024-08-13T07:03:34.035Z (8 months ago)
Size: 19.5 KB
Stars: 32
Watchers: 4
Forks: 12
Open Issues: 0
Metadata Files:
- Readme: README.md
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jimsghstars - yueguoguo/r-on-azure - Compilation of R packages and tools for doing data science and AI on Azure cloud (Others)
README

        R on Azure

---

**I noticed that though this is site has not been activey maintained by myself for a while there are still people starred/forked the repository. Gladly the repository content provides help to these people. I will keep an eye on the repository - for all the readers and/or users of the repository, please feel free to make any contributions to update the listed resources and references whenever you feel necessary. Thanks!**

Development of data science and AI becomes easier than ever before thanks to cloud computing. The Github repo site collects a set of R packages, tools, and case-studies for doing R data science on Azure cloud. 

## R packages and tools

These packages and tools are categoried into four groups, representing four typical tasks data scientists or AI developers may frequently work on. 

Category|Features

----------------------|-----------------------------------------------------------------------------------------------------

[Cloud resource operation and administration](https://github.com/yueguoguo/r-on-azure#cloud-resource-operation-and-administration)|Simplify the way to interact with Azure cloud platform and operate resouces on Azure for various tasks.

[Scalable and advanced analytics](https://github.com/yueguoguo/r-on-azure#scalable-and-advanced-analytics)|Enable large-scale and parallel data analytics in R environment.

[Remote interaction and access to Cloud instance](https://github.com/yueguoguo/r-on-azure#interaction-and-remote-access)|Enhance work efficiency on cloud for R based analytics. 

 [Application and service deployment](https://github.com/yueguoguo/r-on-azure#application-and-service-deployment)|Make operationalizing solution and deploying it as service easy.

### Cloud resource operation and administration

R packages and tools in this category are featured by offering a simplified way to interact with Azure cloud platform and operate resouces (e.g., blob storage, Data Science Virtual Machine, Azure Batch Service, etc.) on Azure for various tasks. 

* [AzureSMR](https://github.com/Microsoft/AzureSMR) - R package for managing a selection of Azure resources. Targeted at Data Scientists who need to control Azure Resources without needing to both Administrators. APIs include Storage Blobs, HDInsight(Nodes, Hive, Spark), ARM, VMs.

* [AzureDSVM](https://github.com/Azure/AzureDSVM) - R package that offers convenient harness of Azure DSVM, remote execution of scalable and elastic data science work, and monitoring of on-demand resource consumption.

* [doAzureParallel](https://github.com/Azure/doAzureParallel) - R package that allows users to submit parallel workloads in Azure.

* [rAzureBatch](https://github.com/Azure/rAzureBatch) - a HTTP proxy library written in R for Azure.

* [AzureML](https://github.com/RevolutionAnalytics/AzureML) - an R interface to [AzureML](https://studio.azureml.net/) experiments, datasets, and web services.

* [AzureR](https://github.com/Azure/AzureR) - Family of packages for interacting with Azure from R

  * [AzureRMR](https://github.com/cloudyr/AzureRMR) - Base functionality for Azure Resource Management: authenticate, get subscriptions, get resource groups.

  * [AzureAuth](https://github.com/Azure/AzureAuth) - R package for OAuth 2.0 authentication with Azure Active Directory

  * [AzureKeyVault](https://github.com/Azure/AzureKeyVault) - R interface to Azure Key Vault

  * [AzureKusto](https://github.com/Azure/AzureKusto) - R interface to Azure Data Explorer, aka Kusto

  * [AzureStor](https://github.com/cloudyr/AzureStor) - R package for Azure Storage management.

  * [AzureContainers](https://github.com/cloudyr/AzureContainers) - R supports for container related services on Azure, that is, [Azure Container Instances](https://azure.microsoft.com/en-us/services/container-instances/), [Azure Kubernetes Services](https://azure.microsoft.com/en-us/services/kubernetes-service/), and [Azure Container Registry](https://azure.microsoft.com/en-us/services/container-registry/).

  * [AzureGraph](https://github.com/cloudyr/AzureGraph) - a simple interface to the [Microsoft Graph API](https://developer.microsoft.com/en-us/graph).

  * [AzureVM](https://github.com/Azure/AzureVM) - R package for managing virtual machines in Azure

### Scalable and advanced analytics

R packages and tools in this category allow one to performan large-scale R-based analytics on cloud with the bleeding-edge frameworks such as Spark, Hadoop, Microsoft Cognitive Toolkit, Tensorflow, Keras, etc. 

**NOTE**: many of the tools are pre-installed and configured for direct use on Azure Data Science Virtual Machine.

#### Scalable analytics

* [dplyrXdf](https://github.com/RevolutionAnalytics/dplyrXdf) - a dplyr backend for Revolution Analytics xdf files.

* [sparklyr](http://spark.rstudio.com/) - R interface for Apache Spark.

* [SparkR](https://spark.apache.org/docs/latest/sparkr.html) - SparkR is an R package that provides a light-weight frontend to use Apache Spark from R.

#### Deep learning

* [CNTK-R](https://github.com/Microsoft/CNTK-R) - R bindings to the CNTK library.

* [tensorflow](https://tensorflow.rstudio.com/) - R interface to Tensorflow.

* [mxnet](https://mxnet.incubator.apache.org/api/r/index.html) - The MXNet R package brings flexible and efficient GPU computing and state-of-art deep learning to R.

* [keras](https://keras.rstudio.com/) - R interface to Keras.

* [darch](https://github.com/maddin79/darch) - Create deep architectures in R. 

* [deepnet](https://cran.r-project.org/web/packages/deepnet/index.html) - Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on.

* [gpuR](https://github.com/cdeterman/gpuR) - R interface to use GPU.

#### Compositive

* [RevoScaleR](https://docs.microsoft.com/en-us/machine-learning-server/r-reference/revoscaler/revoscaler) - a collection of portable, scalable, and distributable R functions for importing, transforming, and analyzing data at scale. 

* [MicrosoftML](https://docs.microsoft.com/en-us/machine-learning-server/r-reference/microsoftml/microsoftml-package) - a package that provides state-of-the-art fast, scalable machine learning algorithms and transforms for R.

* [h2o](https://github.com/h2oai/h2o-3) - R interface to H2O.

### Interaction and remote access

The R packages and tools in this category help data scientists or developers to easily remote access or interact with Azure cloud instances or services for convenient development.

* [mrsdeploy](https://docs.microsoft.com/en-us/machine-learning-server/r-reference/mrsdeploy/mrsdeploy-package) - an R package that provides functions for establishing a remote session in a console application and for publishing and managing a web service that is backed by the R code block or script you provided.

* [R Tools for Visual Studio](https://www.visualstudio.com/vs/rtvs/) - IDE with R support.

* [RStudio Server](https://www.rstudio.com/products/rstudio/#Server) - IDE for remote R session with access via Internet browser.

* [JupterHub](https://jupyterhub.readthedocs.io/en/latest/) - Jupyter notebook with multi-user access.

* [IRKernel](https://github.com/IRkernel/IRkernel) - R kernel for Jupyter notebook. 

### Application and service deployment 

The R packages and tools in this category are used for deploying an R-based analytics or applicaiton as services or interfaces that can be conveniently consumed by end-users or developers. 

* [mrsdeploy](https://docs.microsoft.com/en-us/machine-learning-server/r-reference/mrsdeploy/mrsdeploy-package) - an R package that provides functions for deploying easily-consumable service within R session. 

* [AzureML](https://cran.r-project.org/web/packages/AzureML/index.html) - an R package to allow one to interact with [Azure Machine Learning Studio](https://studio.azureml.net/) for publishing R functions as API services. 

* [Azure Container Instances](https://azure.microsoft.com/en-us/services/container-instances/) - Azure service to allow running a containerized R analytics on cloud.

* [Azure Container Services](https://azure.microsoft.com/en-us/services/container-service/) - Azure service that simplifies deployment, management, and operation of orchestrated containers of R analytics. 

* [Shiny server](https://www.rstudio.com/products/shiny/shiny-server/) - Develop and publish Shiny based web applications online.

## Real-world use cases

The real-world use cases below show case Azure cloud-based analytical solutions that involve the aforementioned R packages or tools. 

Use case|Key R packages or tools

----------------------|-----------------------------------------------------------------------------------------------------

[Campaign management](https://github.com/Microsoft/r-server-campaign-optimization)|RevoScaleR, RTVS/RStudio

[Customer churn prediction](https://github.com/Microsoft/SQL-Server-R-Services-Samples/tree/master/Churn)|RevoScaleR, MicrosoftML, RTVS/RStudio

[Energy demand forecasting](https://github.com/Microsoft/SQL-Server-R-Services-Samples/tree/master/EnergyDemandForecasting)|RevoScaleR, MicrosoftML, RTVS/RStudio

[Fraud detection](https://microsoft.github.io/r-server-fraud-detection/index.html)|RevoScaleR, RTVS/RStudio

[Galaxies classification](https://github.com/Microsoft/acceleratoRs/tree/master/GalaxyClassificationWorkflow)|RevoScaleR, mrsdeploy, MicrosoftML, RTVS/RStudio

[Performance test tuning](https://github.com/Microsoft/SQL-Server-R-Services-Samples/tree/master/PerfTuning)|RevoScaleR, RTVS/RStudio

[Predictive maintenance](https://github.com/Microsoft/SQL-Server-R-Services-Samples/tree/master/PredictiveMaintanenceModelingGuide)|RevoScaleR, RTVS/RStudio

[Retail forecasting](https://github.com/Microsoft/SQL-Server-R-Services-Samples/tree/master/RetailForecasting)|RevoScaleR, RTVS/RStudio

[Credit risk scoring](https://github.com/Microsoft/acceleratoRs/tree/master/CreditRiskPrediction)|MicrosoftML, mrsdeploy, Shiny, RTVS/RStudio

[Drop-out prediction](https://github.com/Microsoft/acceleratoRs/tree/master/EducationAnalytics)|MicrosoftML, Jupyter Notebook

[Product demand forecasting](https://github.com/Microsoft/acceleratoRs/tree/master/ProductDemandForecast)|RevoScaleR, RTVS/RStudio

[Solar panel forecasting](https://github.com/Microsoft/acceleratoRs/tree/master/SolarPanelForecasting)|AzureSMR, AzureDSVM, keras, RTVS/RStudio

[Employee attrition prediction](https://github.com/Microsoft/acceleratoRs/tree/master/EmployeeAttritionPrediction)|AzureSMR, AzureDSVM, Azure Container Services, Shiny, RTVS/RStudio

[Flight delay prediction](https://github.com/Microsoft/acceleratoRs/tree/master/flightDelayPredictionWithDSVM)|AzureSMR, AzureDSVM, MicrosoftML, SparkR, RTVS/RStudio

[Monte Carlo price simulation](https://github.com/Azure/doAzureParallel/blob/master/samples/montecarlo/montecarlo_pricing_simulation.R)|doAzureParallel, RTVS/RStudio
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