A Fine Slice Of SQL Server
You 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 closedYou should never embed passwords or other “secrets” – sensitive data – in code. A better way is to put sensitive data into configuration, and load configuration from your code. Read on to find out how to do this in R Markdown (and Shiny).
Click through for one way to do this. Just make sure you .gitignore excluded .env files.
Tomaz Kastrun knows if you can sum those together:
CanSum problem is a problem where a given array of integers (
nums) and a target integer (target), return boolean (TRUE \ FALSE) indicating, that theinteger can be calculated using two or more numbers in arraytargetnums.You may assume that each integer from the array can be used multiple times. You can also assume, that any integer (in
numsor intarget) is from 0 to +1e9 and the length of thenumsarray is from 2 to 1000 elements.
Click through for an example of one brute-force solution, followed by a much faster solution.
Comments closedCosima Meyer explains how debugging works in R with RStudio:
Three basic commands in RStudio let you do the debugging:
debug(function_name),browser(), andundebug(function_name).With
debug(function_name)you start the debugging of your function – it’s basically like a mole that digs in. When you’re in debug mode, you can also call the objects in your function.
Read the whole thing to learn the power of debugging beyond the print() statement. H/T R-Bloggers.
Sebastian Sauer takes us through an interesting scenario.
No lede here because it’s almost 100% code and headers. A quick description of this is that we can see ways to parse columns in an R DataFrame and plot visuals without hard-coding the column name in our plot definition, using a variable instead.
And I had to rewrite the synopsis above because I used the data science term “variable” until hitting a wall when describing the programming term “variable.”
Comments closedThomas Williams waits for the keystroke:
This R Markdown snippet demonstrates “debouncing”: waiting until a user stops changing an input, before updating dependent charts and tables. Debouncing is often used in web sites to prevent the user interface “jumping” as data is being entered, especially when the update takes a noticeable amount of time – for instance calling an API or database, or doing a calculation.
Read on to see an abridged example, as well as a link to the full version.
Comments closed