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Category: R

Writing Tests with shinytest2

Published 2023-01-13 by Kevin Feasel

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.

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Ways to Convert a List to DataFrame in R

Published 2023-01-11 by Kevin Feasel

Tomaz 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.

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End-to-End Testing via shinytest2

Published 2023-01-06 by Kevin Feasel

Russ 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 input and output variables that are stored by a shiny app at any given time, the inputs 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.”

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Throwing MRAN a Retirement Party

Published 2023-01-04 by Kevin Feasel

Umachandar 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.

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Frames and Tiles in mapBliss (R)

Published 2022-12-28 by Kevin Feasel

Benjamin Smith updates an R package:

The mapBliss package 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 the leaflet and 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.

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Interpreting Linear Models with SHAP

Published 2022-12-27 by Kevin Feasel

Michael 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.

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Working with R in AML v2

Published 2022-12-23 by Kevin Feasel

Tomaz 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.

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Coloring Tree Branches in R

Published 2022-12-20 by Kevin Feasel

Ali Oghabian marks up a tree:

After 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.

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An Overview of R7

Published 2022-12-19 by Kevin Feasel

Nicola Rennie explains the purpose of a new standard for object-oriented programming in R:

The two main OOP systems in R, S3 and S4, both have their advantages and their limitations. For example, in S3 there’s no systematic object validation to make sure an object’s class is correct. In S4, the syntax for defining classes is rather unusual and relies on side effects. Issues such as these mean that, unlike other programming languages, there isn’t a dominant approach to OOP in R.

Now imagine you could take the best bits of S3 and the best bits of S4. That’s where R7 comes in. 

Read on to learn more about how R7 compares to other object-oriented paradigms in R, such as S3, S4, and R6.

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Running Python Code from R via Reticulate

Published 2022-12-12 by Kevin Feasel

Rick Pack crosses the streams:

I wanted a REPL (read-evaluate-print-loop) so that I could quickly experiment with Python without, for the moment, leaping over what some consider one of the biggest hurdles to Python usage: Work environment set up.

The reticulate R package by Posit enables the use of Python while working within the R Studio IDE. One can find a Posit tutorial here.

Read on for Rick’s notes.

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