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Category: R

Comparing {targets} in R to dbt for Data Engineering

Published 2026-05-06 by Kevin Feasel

Jonathan Carroll compares two approaches:

Thinking of a real-world project I could take for a spin, I decided to build some ingestion for my personal finances. I’ve used Quickbooks previously which connects up to my bank and helps categorise personal and business (as a freelance contractor) expenses. I decided I’ll build my own ‘slowbooks’ processing workflow based on some manual exports (I don’t think my bank has an API).

Both of the approaches I’ll compare here build on the idea of a Makefile which connects up commands to run based on dependencies, and only runs what is needed; if all the input dependencies of a step have not changed, there’s no need to re-run that step. From what I understand, you could largely get away with just writing some Makefiles (or the newer implementation just (just.systems)) but these two approaches help to better structure how that’s constructed.

Read on for Jonathan’s discovery process and ultimate findings. H/T R-Bloggers.

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A Verbose Pipe Operator for dplyr Pipelines

Published 2026-04-30 by Kevin Feasel

Guillaume Pressiat shows off logrittr:

In SAS, every DATA step prints a log:

NOTE: There were 120000 observations read from WORK.SALES.
NOTE: 7153 observations were deleted.
NOTE: The data set WORK.RESULT has 112847 observations and 11 variables.

R’s dplyr pipelines are silent. logrittr fills that gap with %>=%, a drop-in pipe that logs row counts, column counts, added/dropped columns, and timing at every step, with no function masking.

Click through to see how logrittr helps. Back when I was using R heavily, I would have really enjoyed this package. H/T R-Bloggers.

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A Desk Plant Simulator in R

Published 2026-04-28 by Kevin Feasel

Tomaz Kastrun grows a plant:

And how this set of functions really work?

Storing the daily progress in an RDS. Before running set of functions, you will prepare a location to store the RDS file (environment variables and game play)

Given how my plants tend to look, I probably shouldn’t try this.

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Support for Typst in knitr

Published 2026-04-28 by Kevin Feasel

Yihui Xie makes an announcement:

A few weeks ago I added preliminary support for Typst to knitr. The way it works is simple: if your file has the extension .Rtyp, knitr will recognize it as a Typst document, knit it, and produce a .typ output file. The chunk syntax follows the same Markdown-style fenced code block convention: ```{r} to start a chunk and ``` to end it, with inline R expressions written as `r expr`. A minimal example (hello.Rtyp):

Click through for that example, as well as some notes on Typst and HTML.

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Maestro Now 1.1.0

Published 2026-04-23 by Kevin Feasel

Will Hipson lays out an update:

maestro has officially graduated to stable release with version 1.0.0 back in January 2026 and now its latest version 1.1.0. This marks a commitment to maintaining a stable API and increased reliance on using maestro in production. In our environment alone, maestro has orchestrated millions of pipeline executions over the course of a year, effectively making it the heartbeat of our entire data stack.

If you haven’t heard of maestro, it’s a pipeline orchestration package. You can learn more about it here.

Click through to see what’s changed between the 1.0.0 release and now. H/T R-Bloggers.

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What’s New in R 4.6.0

Published 2026-04-16 by Kevin Feasel

Russ Hyde has a summary:

R 4.6.0 (“Because it was There”) is set for release on April 24th 2026. Here we summarise some of the more interesting changes that have been introduced. In previous blog posts, we have discussed the new features introduced in R 4.5.0 and earlier versions (see the links at the end of this post).

Once R 4.6.0 is released, the full changelog will be available at the r-release ‘NEWS’ page. If you want to keep up to date with developments in base R, have a look at the r-devel ‘NEWS’ page.

Click through for the highlights.

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Diagnosing a textConnection() Slowdown in R

Published 2026-04-02 by Kevin Feasel

Yihui Xie looks into an issue:

Running quarto render on a document with that single chunk took 35 seconds. The equivalent rmarkdown::render() finished in under half a second. As a side note in the issue, the reporter pinged me that the same problem existed in litedown. litedown is independent of both Quarto and knitr; it executes R code through xfun::record(). That is where I started looking.

Click through for the discovery process, explanation, and fix.

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Compressing Images in R

Published 2026-04-01 by Kevin Feasel

Yihui Xie announces a new package:

Last month, @bastistician opened an issue on the litedown repo pointing out that knitr has a hook_pngquant() function for compressing PNG plots from code chunks, but litedown lacks such a feature. He included a reasonable workaround—calling system2("pngquant", ...) with litedown::get_context("plot_files") in a chunk at the end of the vignette. It shrank his vignette from 80 KB to 54 KB, which is a 33% reduction. Not bad.

The catch, of course, is that it requires pngquant to be installed on the system. For R users, installing a system binary is more friction than it sounds: it is brew install pngquant on macOS, a separate package manager invocation on Linux, and hunting down a standalone executable on Windows. If you maintain a package that others will build, you are now asking all of them to do this—for every machine they use. By contrast, install.packages("tinyimg") works the same way everywhere, which is the kind of simplicity that makes a tool actually get used.

This is why I created tinyimg.

Read on for more details about how tinyimg works, how well it compresses, and how it integrates with litedown.

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