R Tools For Visual Studio

Matt Willis has a two-parter on R Tools for Visual Studio.  First, an introduction:

Once all the prerequisites have been installed it is time to move onto the fun stuff! Open up Visual Studio 2015 and add an R Project: File > Add > New Project and select R. You will be presented with the screen below, name the project AutomobileRegression and select OK.

Microsoft have done a fantastic job realising that the settings and toolbar required in R is very different to those required when using Visual Studio, so they have split them out and made it very easy to switch between the two. To switch to the settings designed for using R go to R Tools > Data Science Settings you’ll be presented with two pop ups select Yes on both to proceed. This will now allow you to use all those nifty shortcuts you have learnt to use in RStudio. Anytime you want to go back to the original settings you can do so by going to Tools > Import/Export Settings.

Next is executing an Azure Machine Learning web service within RTVS:

Whilst in R you can implement very complex Machine Learning algorithms, for anyone new to Machine Learning I personally believe Azure Machine Learning is a more suitable tool for being introduced to the concepts.

Please refer to this blog where I have described how to create the Azure Machine Learning web service I will be using in the next section of this blog. You can either use your own web service or follow my other blog, which has been especially written to allow you to follow along with this blog.

Coming back to RTVS we want to execute the web service we have created.

RTVS has grown on me.  It’s still not R Studio and may never be, but they’ve come a long way in a few months.

Related Posts

Comparing TensorFlow Versus PyTorch

Anirudh Rao compares PyTorch to TensorFlow: For small-scale server-side deployments both frameworks are easy to wrap in e.g. a Flask web server. For mobile and embedded deployments, TensorFlow works really well. This is more than what can be said of most other deep learning frameworks including PyTorch. Deploying to Android or iOS does require a non-trivial amount of work in TensorFlow. You don’t have to rewrite the entire inference portion of your model in Java or C++. […]

Read More

Looking At Databricks Cluster Pricing

Tristan Robinson takes a look at Azure Databricks pricing: The use of databricks for data engineering or data analytics workloads is becoming more prevalent as the platform grows, and has made its way into most of our recent modern data architecture proposals – whether that be PaaS warehouses, or data science platforms. To run any […]

Read More

Categories

January 2017
MTWTFSS
« Dec Feb »
 1
2345678
9101112131415
16171819202122
23242526272829
3031