A Fine Slice Of SQL Server
Predictive Maintenance is one of the big revolutions happening across all major industries right now. Instead of changing parts regularly or even only after they failed it uses Machine Learning methods to predict when a part is going to fail.
If you want to get an introduction to this fascinating developing area, read on!
Click through for an example of how it works.
Comments closedBenjamin Smith’s ideas are intriguing to me and I wish to subscribe to his newsletter:
Jinja is a powerful templating engine that is useful in a variety of contexts. Recently, I discovered how its possible to use the power of Jinja syntax in R with the
jinjarpackage written by David C Hall. Withjinjarand thetidyRSSpackage by Robert Myles it is possible to make an email template that can provide short and informative updates. In his blog, I’m going to share how thejinjarandtidyRSSpackages work and show how to combine them to make a simple daily email newsletter.
Read on to learn how.
Comments closedThomas Williams illuminates us on the caching process:
Caching in R Markdown is a valuable step to get your app, report or visualisation more production-ready. There are one or two potential issues to watch out for, especially when deploying a cache-enabled R Markdown file to a Shiny server – in this post I’ll go over some of these “gotchas”, and how you could address each one.
Click through for those three gotchas.
Comments closedTomaz Kastrun shuffles things around:
The problem is described as:
Given a signed 32-bit integer x, return x with its digits reversed. If reversing x causes the value to go outside the signed 32-bit integer range [-2^31, 2^31 – 1], then return 0.
For example:
x = 120; reversed_x = 21
x = -2310; reversed_x = -132
Read on to see how you can implement this in R.
Comments closedSebastian Sauer looks at Google search data:
You cannot download as much data as you like, there are some restrictions, again, from the same source as above:
Google has incorporated quota limits for Trends searches. This limits the number of search attempts available per user/IP/device. Details of quota limits have not yet been provided, but it may depend on geographical location or browser privacy settings. It has been reported in some cases that this quota is reached very quickly if one is not logged into a Google account before trying to access the Trends service.[52]
Click through to see how you can access this data. In this case, the example focuses on specific categories but there’s a lot more within Google Trends.
Comments closedYou would think base R would have a count function such as count(df$Team) and count(df$Team == “NYY”) but this gives the error “could not find function ‘count’”. Base R does not have a count function. Base R has at last four ways to perform a count:
Click through to learn the different ways available to you, including those built into R itself as well as other packages like dplyr. H/T R-Bloggers.
Ryan Majidimehr has a short list of updates for Azure Synapse Analytics but it includes a big one:
Azure Synapse Analytics provides built-in R support for Apache Spark. As part of this, data scientists can leverage Azure Synapse Analytics notebooks to write and run their R code. This also includes support for SparkR and SparklyR, which allows users to interact with Spark using familiar Spark or R interfaces. To learn more read the official how-to Use R for Apache Spark with Azure Synapse Analytics (Preview).
That it took this long for R support was a bit weird, but I’m glad it’s there now.
Comments closedThere are a growing number of Shiny users across the world, and with many users comes an increasing number of open-source “add-on” packages that extend the functionality of Shiny, both in terms of the front end and the back end of an app.
This blog will highlight 5 UI add-on packages that can massively improve your user experience and also just add a bit of flair to your app. Each package will have an associated example app (some more inspired than others) that I’ve created where you can actually see the UI component in action. All code for example apps can be found on our GitHub.
Click through for the list, as well as examples of how they work.
Comments closedNeil Saunders has some trail cameras:
The camera model I chose is the Campark T85 which for me, had the right combination of features and price point. One useful feature is the ability to transfer images and video to a phone wirelessly (albeit through a rather clunky phone app). Unfortunately, images retrieved in this way have one major flaw: an almost-complete absence of metadata. There is no GPS in the camera of course, but the EXIF data does not include the date/time of the image, nor the camera make.
With a little research, I found a way to add this information to the images later using R and some additional software named exiftool. Here’s how I did it.
Read on to see how Neil solved this issue with a bit of R.
Comments closedJamie Owen and Colin Gillespie don’t have time to write dockerfiles:
For creating a production deployment of a {shiny} application it is often useful to be able to provide a Docker image that contains all the dependencies for that application. Here we explore how one might go about automating the creation of a Dockerfile that will allow us to build such an image for a {shiny} application.
There are some neat tricks in here.
Comments closed