Interactive R visuals in Power BI

Power BI has long had the capability to include custom R charts in dashboards and reports. But in sharp contrast to standard Power BI visuals, these R charts were static. While R charts would update when the report data was refreshed or filtered, it wasn’t possible to interact with an R chart on the screen (to display tool-tips, for example). But in the latest update to Power BI, you can create create R custom visuals that embed interactive R charts, like this: The above chart was created with the plotly package, but you can also use htmlwidgets or any other R package that creates interactive graphics. The only restriction is that the output must be HTML, which can then be embedded into the Power BI dashboard or report. You can also publish reports including these interactive charts to the online…
Original Post: Interactive R visuals in Power BI

Updated Data Science Virtual Machine for Windows: GPU-enabled with Docker support

The Windows edition of the Data Science Virtual Machine (DSVM), the all-in-one virtual machine image with a wide-collection of open-source and Microsoft data science tools, has been updated to the Windows Server 2016 platform. This update brings built-in support for Docker containers and GPU-based deep learning.  GPU-based Deep Learning. While prior editions of the DSVM could access GPU-based capabilities by installing additional components, everything is now configured and ready at launch. The DSVM now includes GPU-enabled builds of popular deep learning frameworks including CNTK, Tensorflow, and MXNET. It also includes Microsoft R Server 9.1, and several machine-learning functions in the MicrosoftML package can also take advantage of GPUs. Note that you will need to use an N-series Azure instance to benefit from GPU acceleration, but all of the tools in the DSVM will also work on regular CPU-based instances as well. Docker…
Original Post: Updated Data Science Virtual Machine for Windows: GPU-enabled with Docker support

Microsoft: Principal Data Scientist (Artificial Intelligence & Deep Learning)

[unable to retrieve full-text content]Currently we are expanding our team to support projects that focus on Artificial Intelligence and Cognitive analytics. We are looking for an expert in this field from business/academia with extensive experience in this area.
Original Post: Microsoft: Principal Data Scientist (Artificial Intelligence & Deep Learning)

Using sparklyr with Microsoft R Server

The sparklyr package (by RStudio) provides a high-level interface between R and Apache Spark. Among many other things, it allows you to filter and aggregate data in Spark using the dplyr syntax. In Microsoft R Server 9.1, you can now connect to a a Spark session using the sparklyr package as the interface, allowing you to combine the data-preparation capabilities of sparklyr and the data-analysis capabilities of Microsoft R Server in the same environment. In a presentation by at the Spark Summit (embedded below, and you can find the slides here), Ali Zaidi shows how to connect to a Spark session from Microsoft R Server, and use the sparklyr package to extract a data set. He then shows how to build predictive models on this data (specifically, a deep Neural Network and a Boosted Trees classifier). He also shows how…
Original Post: Using sparklyr with Microsoft R Server

Demo: Real-Time Predictions with Microsoft R Server

At the R/Finance conference last month, I demonstrated how to operationalize models developed in Microsoft R Server as web services using the mrsdeploy package. Then, I used that deployed model to generate predictions for loan delinquency, using a Python script as the client. (You can see slides here, and a video of the presentation below.) With Microsoft R Server 9.1, there are now two ways to operationalize models as a Web service or as a SQL Server stored procedure: Flexible Operationalization: Deploy any R script or function. Real-Time Operationalization: Deploy model objects generated by specific functions in Microsoft R, but generates predictions much more quickly by bypassing the R interpreter. In the demo, which begins at the 10:00 mark in the video below, you can see a comparison of using the two types of deployment. Ultimately, I was able to generate predictions from…
Original Post: Demo: Real-Time Predictions with Microsoft R Server

Interfacing with APIs using R: the basics

While R (and its package ecosystem) provides a wealth of functions for querying and analyzing data, in our cloud-enabled world there’s now a plethora of online services with APIs you can use to augment R’s capabilities. Many of these APIs use a RESTful interface, which means you will typically send/receive data encoded in the JSON format using HTTP commands. Fortunately, as Steph Locke explains in her most recent R Quick tip, the process is pretty simple using R: Obtain an authentication key for using the service  Find the URL of the API service you wish to use Convert your input data to JSON format using toJSON in the jsonlite package Send your data to the API service using the POST function in the httr package. Include your API key using the add_headers function Extract your results from the API response…
Original Post: Interfacing with APIs using R: the basics

Run massive parallel R jobs cheaply with updated doAzureParallel package

At the EARL conference in San Francisco this week, JS Tan from Microsoft gave an update (PDF slides here) on the doAzureParallel package . As we’ve noted here before, this package allows you to easily distribute parallel R computations to an Azure cluster. The package was recently updated to support using automatically-scaling Azure Batch clusters with low-priority nodes, which can be used at a discount of up to 80% compared to the price of regular high-availability VMs. JS Tan using doAzureParallel #rstats package to run simulation on a cluster of 20 low-priority Azure VMs. Total cost: $0.02 #EARLConf2017 — David Smith (@revodavid) June 7, 2017 Using the doAzureParallel package is simple. First, you need to define the cluster you’re going to use as a JSON file. (You can see an example on the right.) Here, you’ll specify your Azure credentials, the size of the…
Original Post: Run massive parallel R jobs cheaply with updated doAzureParallel package

Teach kids about R with Minecraft

As I mentioned earlier this week, I was on a team at the ROpenSci Unconference (with Brooke Anderson, Karl Broman, Gergely Daróczi, and my Microsoft colleagues Mario Inchiosa and Ali Zaidi) to work on a project to interface the R language with Minecraft. The resulting R package, miner, is now available to install from Github. The goal of the package is to introduce budding programmers to the R language via their interest in Minecraft, and to that end there’s also a book (R Programming with Minecraft) and associated R package (craft) under development to provide lots of fun examples of manipulating the Minecraft world with R. Create objects in Minecraft with R functions If you’re a parent you’re probably already aware of the Minecraft phenomenon, but if not: it’s kinda like the Lego of the digital generation. Kids (and kids-and-heart) enter a virtual 3-D world composed…
Original Post: Teach kids about R with Minecraft

Watch presentations from R/Finance 2017

It was another great year for the R/Finance conference, held earlier this month in Chicago. This is normally a fairly private affair: with attendance capped at around 300 people every year, it’s a somewhat exclusive gathering of the best and brightest minds from industry and academia in financial data analysis with R. But for the first time this year (and with thanks to sponsorship from Microsoft), videos of the presentations are available for viewing by everyone. I’ve included the complete list (copied from the R/Finance website) below, but here are a few of my favourites: You can find an up-to-date version of the table below at the R/Finance website (click on the “Program” tab), and you can also browse the videos at Channel 9. Note that the lightning talk sessions (in orange) are bundled together in a single video, which you…
Original Post: Watch presentations from R/Finance 2017

Microsoft R Open 3.4.0 now available

Microsoft R Open (MRO), Microsoft’s enhanced distribution of open source R, has been upgraded to version 3.4.0 and is now available for download for Windows, Mac, and Linux. This update upgrades the R language engine to R 3.4.0, reduces the size of the installer image, and updates the bundled packages. R 3.4.0 (upon which MRO 3.4.0 is based) is a major update to the R language, with many fixes and improvements. Most notably, R 3.4.0 introduces a just-in-time (JIT) compiler to improve performance of the scripts and functions that you write. There have been a few minor tweaks to the language itself, but in general functions and packages written for R 3.3.x should work the same in R 3.4.0. As usual, MRO points to a fixed CRAN snapshot from May 1 2017, but you can use the built-in checkpoint package to access packages…
Original Post: Microsoft R Open 3.4.0 now available