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Category: R

Sharing Data between Shiny Modules

Published 2026-03-22 by Kevin Feasel

Colin Fay explains an architecture:

Some people have recently been vocal about misuses of the "stratégie du petit r", a mechanism for sharing data across {shiny} modules that was detailed both in the Engineering Production-Grade Shiny Apps book and in an older post written in 2019 on this blog.
And yes, if you’re wondering, I did feel old when I realized this blog post is almost 7 years old now

I’m always happy to be proven wrong, to challenge the way I build software, and to become a better software engineer. But given that we weren’t contacted to discuss the ideas behind this strategy, I thought the moment was perfect to give y’all an update on the latest approaches I’ve been using to share data across {shiny} modules, along with some thoughts and comments on the "stratégie du petit r".

Click through to learn more about Shiny modules, the challenge of passing information between modules, and some high-level ideas of how to pass information between these modules without everything falling apart. H/T R-Bloggers.

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Loading Multiple CSV Files in R

Published 2026-03-13 by Kevin Feasel

Stephen Royle has a set of challenges:

In a previous post, I described how to run a session to teach R to cell biologists. In this post we’ll look in a bit more detail at one of the steps: how to load data into R.

As a reminder, a typical analysis task in cell biology follows these steps:

  • do the experiment(s)
  • collect the data – e.g. microscopy images
  • analyse and get a plain text (csv) output – e.g. using Fiji
  • load the data into R
  • crunch some numbers and plot

Click through for some explanation, as well as a few exercises (and solutions) for people trying to learn the language. H/T R-Bloggers.

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When Order Matters

Published 2026-03-12 by Kevin Feasel

The Rtask people tell a story:

You have inherited (or written) a data pipeline originally coded in SAS. It processes administrative billing records: matching line items against reference tables, applying time-varying coefficients, deduplicating based on business identifiers, computing running counters. Classic ETL work.

The migration to R goes well. You use {DBI} to open a DuckDB connection, load your source files as lazy tables via {arrow} or dplyr::tbl(), build the transformations with {dbplyr}, and collect the result at the very end. Your code is readable, your tests compare the R output to the SAS reference, and they pass (maybe using {datadiff}).

Then you run the pipeline again.

The numbers are different.

Give yourself 100 points if you answered “Because you need an ORDER BY clause” during the explanation. They also cover a few other places where DuckDB interactions in R can cause issues. Most of this is straightforward for data platform people, but can cause consternation for developers. H/T R-Bloggers.

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Dockerizing Shiny and RMarkdown Apps

Published 2026-03-04 by Kevin Feasel

Thomas Williams builds a container image:

Running R Markdown via Docker is a big help with deploying those analysis and reports for others to use.

While there are alternatives for deploying R Markdown — the most popular being Shiny Server — they add the hassle of matching R and package versions to the machine where the code was developed, may require IT help, and mean sharing disk, CPU, and memory with other apps on the same server. And there’s also licensing requirements & limitations for the free version of Shiny Server.

R Markdown on Docker avoids some of these issues; a single Docker container = a single app.

Click through for a simple RMarkdown file and Dockerfile.

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Tracking Typing Speed with R

Published 2026-02-26 by Kevin Feasel

Tomaz Kastrun is pushing aside Mavis Beacon:

Did you ever wonder how fast and with accuracy your typing is?

For this instance, we will introduce some random pangrams, code samples and random strings sotrted by level of difficulty.

This was kind of fun. I could hit about 80 or so WPM on the easy code examples and about 120 on the pangrams (with consistency between difficulties). Also, “Sphinx of black quartz judge my vow” is a pretty awesome thing to shout at the most opportune time.

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Viewing Nested Lists in R with xfun::tabset()

Published 2026-02-10 by Kevin Feasel

Yihui Xie wants to document the contents of nested lists:

Complex nested lists in R can be difficult to explore and understand at a glance. The str() function is helpful for examining structure, but large nested lists can quickly become overwhelming.

While I was writing the documentation for tabsets in litedown, I almost laughed at myself for the support for nested tabsets, because I had no idea why anyone would want this feature. However, I suddenly realized that it can be a very useful tool for exploring nested lists in an interactive way, so I wrote a quick implementation: xfun::tabset().

Click through to see how it works.

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NYC Open Data R Package

Published 2026-02-02 by Kevin Feasel

Antoine Soetewey announces a package:

I am pleased to announce the release of nycOpenData, an R package providing convenient, tidy access to dozens of datasets from the New York City Open Data platform.

The package is designed as part of an open-science and reproducible-research effort, with the goal of lowering the friction between public data and statistical analysis—especially for teaching, exploratory research, and applied civic work.

It is available on CRAN, so it should be easy to grab. H/T R-Bloggers.

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Operating on Distributions in R with distionary

Published 2026-01-23 by Kevin Feasel

Vincenzo Cola announces a new R package:

After passing through rOpenSci peer review, the distionary package is now newly available on CRAN. It allows you to make probability distributions quickly – either from a few inputs or from its built-in library – and then probe them in detail.

These distributions form the building blocks that piece together advanced statistical models with the wider probaverse ecosystem, which is built to release modelers from low-level coding so production pipelines stay human-friendly. Right now, the other probaverse packages are distplyr, allowing you to morph distributions into new forms, and famish, allowing you to tune distributions to data. Developed with risk analysis use cases like climate and insurance in mind, the same tools translate smoothly to simulations, teaching, and other applied settings.

Click through for an overview of the package.

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Randomly Moving the Mouse Cursor in R

Published 2026-01-07 by Kevin Feasel

Tomaz Kastrun has been so busy, his screensaver never comes on, even when he’s out at lunch:\

New R Package called LazyMouse with single function for randomly moving mouse cursor in your favorite R IDE.

For every R developer, R data scientists and all those everyday R users, that also need a break and do not want the computer to go into sleep mode.

Read on to see how it works. And jokes aside, there have been times in which I’ve wanted something like this to keep the screen from locking up or drives going to sleep when running heavy work overnight on a device I can physically control (i.e., not a workstation I’m leaving on at the office).

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