Revolutions

Handling dates and times in R: a free online course

If you ever need to work with data involving dates, times or durations in R, then take a look at this free course on LinkedIn Learning presented by Mark Niemann-Ross: R Programming in Data Science: Dates and Times. Here's the course overview from the introductory video:

When did Mount St Helens last erupt? How many birds migrated south this month? Which month last year was the coldest on average? These are questions related to time. And if you're going to perform calculations on this type of data, then you need date and time data structures. I

I'll show you how to store dates and times, how to retrieve dates and times, and how to add and subtract dates and times to discover the duration of an event or the difference between two dates. I'll show how to handle dates for business research and for financial forecasting. We'll explore functions include with base R for dates and time calculations, and we'll explore packages that provide special ways to work with dates and times. 

The course does assume some basic familiarity with R syntax, but does start from the beginning with the base R date and time classes before moving on to the newer Tidyverse packages for handling date and time data. Each chapter also includes a quiz to test your knowledge.

The course content is free to view, and all the R scripts associated with the course are available to download as well. 

LinkedIn Learning: R Programming in Data Science: Dates and Times

Obtaining tokens with AzureAuth inside a Shiny app

by Hong Ooi, senior data scientist, Microsoft Azure

As of version 1.2.0 (released to CRAN late last month), it’s possible to use the AzureAuth package to login interactively to Azure from within a Shiny webapp. Because a Shiny app has separate UI and server components, some changes had to be made to the interactive authentication flows. In particular, the authorization step (logging in to Azure) has to be conducted separately from the token acquisition step.

AzureAuth now provides the build_authorization_uri function to facilitate this separation. You call this function to obtain a URI that you browse to in order to login to Azure. Once you have logged in, Azure will return an authorization code as part of a redirect, which can be captured by the browser. The code is then passed to the get_azure_token function in the auth_code argument, which then completes the task of acquiring the token.

Here is a skeleton Shiny app that demonstrates its use.

Note that this process is only necessary within a web app, and only when using an interactive authentication flow. In a normal R session, or when using the client credentials or resource owner grant flows, you can simply call get_azure_token directly.

A DevOps Process for Deploying R to Production

I've been at the EARL Conference in London this week, and as always it's been inspiring to see so many examples of R being used in production at companies like Sainsbury's, BMW, Austria Post, PartnerRe, Royal Free Hospital, the BBC, the Financial Times, and many others. My own talk, A DevOps Process for Deploying R to Production, presented one process for automating the process of building and deploying R-based applications using Azure Pipelines and Azure Machine Learning Service. The talk at EARL wasn't recorded, but you can see the slides here, and also watch a slightly shorter version of the talk as it was presented at the useR!2019 conference in Toulouse, below:

If you'd like to try setting up a build process for R yourself with Azure Pipelines, this GitHub repository is a good place to start. It provides a simple example of a model built with R, which gets triggered on check-in to the repository (you can see the builds in Pipelines, here). The README.md file also includes links to useful resources on setting up an end-to-end workflow for machine learning.

GitHub (revodavid):  MLOps with R and Azure Pipelines