A Fine Slice Of SQL Server
Sebastian Sauer compares all values across a row:
Sometimes is is neccessary to compute functions, such as mean values, rowwise, ie., summing the values for multiple variables (
my_vars) for each observation.
It’s actually pretty easy, and applies to functions other than mean(). Click through for a demonstration.
Michael Mayer shows off the native R pipe:
What does the pipe do? It puts the object on its left as the first argument into the function on its right:
iris %>% head()is a funny way of writinghead(iris). It helps to avoid long function chains likef(g(h(x))), or repeated assignments.In 2021 and version 4.1, R has received its native forward pipe operator
|>so that we can write nice code like this:
Tying pipe syntax all back together, the magrittr pipe %>% was (as I recall) built with the F# pipe |> in mind. In R 4.1 and later, the built-in pipe is |>, as is right and natural in this world. Regardless, do check the comment before trying out this code, as it appear to work for R 4.2 and later, though not 4.1.
Russ Hyde continues a series on shinytest2:
Here, we will write a simple shiny app (as an R package) and show how to generate tests for this app using {shinytest2}. As discussed in the previous post, {shinytest2} tests your app as if a user was interacting with it in their browser. The tests generated are application-focussed rather than component-focussed and so give some overall guarantees on how the app should behave.
This post is slightly more technical than the last, and assumes that the reader is comfortable with creating and unit-testing packages in R, and with shiny development in general.
Click through to see the code, as well as plenty of explanation.
Comments closedTomaz Kastrun starts the stopwatch:
When you are working with large datasets performance comes to everyone’s mind. Especially when converting datasets from one data type to another. And choosing the right method can make a huge difference.
So in this case, I will be creating a dummy list, and I will convert the values in the list into data.frame.
Click through for a variety of techniques and how well they performed. There are some good solutions in the comments as well.
Comments closedRuss Hyde begins a new series:
{shinytest2} builds upon the {shinytest} package and was written by Barret Schloerke and his colleagues at RStudio. Like puppeteer, {shinytest2} uses the Chrome DevTools Protocol to interact with the browser, which is a pretty stable basis for building a browser automation tool (the predecessor {shinytest} was built on a now-unsupported browser library called PhantomJS, so we strongly recommend migrating to {shinytest2} if you are still using {shinytest}). Test scripts are written in R and so should be accessible to R developers who are comfortable with {testthat}. There is an automated tool (described in the next post) for creating these test scripts. Also, {shinytest2} understands the architecture of shiny apps, and so it is simple to access the
inputandoutputvariables that are stored by a shiny app at any given time, theinputs can be modified easily as well – to access these variables using the more general UI-based end-to-end testing tools is much more difficult.
Read on for the “why” behind this series and the next posts will get into more of the “how.”
Comments closedUmachandar Jayachandran makes an announcement:
In June 2021, we announced that Microsoft Machine Learning Server & Microsoft R Open will be retired on July 1, 2022. In continuation with the retirement process, the Microsoft R Application Network (MRAN) website and CRAN Time Machine will be retired on July 1, 2023.
If you are using MRAN, click through and review the dates so you don’t have the rug pulled out from under you.
Comments closedBenjamin Smith updates an R package:
The
mapBlisspackage is a R package which I developed which allows for users to make custom souvenir quality maps of their flights, road trips and favorite cities by utilizing the power of theleafletand other R packages (for a full list, see the Github README here). The goal of the package is to imitate the visualization and print-ability of maps produced by businesses like Atlas.co(my original inspiration), TheLittlePen, Mapiful and MaptracksMe (among many other such businesses).
It’s an interesting-looking package.
Comments closedMichael Mayer answers a question:
XGBoost models are often interpreted with SHAP (Shapley Additive eXplanations): Each of e.g. 1000 randomly selected predictions is fairly decomposed into contributions of the features using the extremely fast TreeSHAP algorithm, providing a rich interpretation of the model as a whole. TreeSHAP was introduced in the Nature publication by Lundberg and Lee (2020).
Can we do the same for non-tree-based models like a complex GLM or a neural network? Yes, but we have to resort to slower model-agnostic SHAP algorithms:
Read on for examples of those algorithms and an example of interpretation and analysis.
Comments closedTomaz Kastrun ends the advent of Azure ML on a downer:
R language and Azure Machine Learning SDK for R was deprecated a year ago (end of 2021). But R can be still used for training and deployment by using Azure Machine learning CLI 2.0!
Furthermore, R language can be used in Machine Learning Designer, for data preparation, data wrangling and statistical analysis.
You can work with R but they make sure everything is more difficult.
Comments closedAfter running Hierarchical clustering we can cut the result binary tree at a certain depth or request that it be cut in a manner that would result a certain number of clusters. Here, I request that the resulted binary tree be cut in away that would result to 2 sample clusters. Furthermore, I convert the resulted tree to a “dendogram” object and colour the branches and the labels of the tree to visualize the 2 clusters. One can use color_branches and color_labels functions to cut and colour the trees.
Read on for a demonstration. H/T R-Bloggers.
Comments closed