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I've been looking into dbt for data engineering and wanted to flesh out what the similarities and differences were to just using {targets} - I learned lots about both of them!
I wrote up my findings in this post
https://jcarroll.com.au/2026/05/04/comparing-r-s-targets-and-dbt-for-data-engineering/
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I'm not sure where the #user2025 discussions are happening but I'm curious about one slide in particular from @hadleywickham's talk
https://www.youtube.com/live/ctc2kx3LxG8
why the addition of 0 for each term of the multiplication? Is it to have NA values fail early? I was hoping it would be explained but not that I saw
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Implementing small math problems can really help to cement your knowledge of fundamental operators...
https://jcarroll.com.au/2025/05/03/rotation-with-modulo/
I implemented a 'rotate digits with modulo' in #rstats :rstats: #julialang :julia: #apl :apl: and #uiua :uiua: and had a great time doing so!
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Really interesting approach from Paul Goulart at #RustSciComp23 mixing #julia and #rust with the takeaway (paraphrased)
"Julia is great for debugging the *math* problems, Rust is great for debugging the *code* problems, and supporting both of those at once Is a lot less work than doing all of it twice"
Followed by another great talk by @mo8it highlighting the footguns and benefits of Julia (check out his blog post https://mo8it.com/blog/rust-vs-julia/)
I wasn't expecting so much Julia but I'm loving it!
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I'm watching #RustSciComp23 for the #rust, but daaaaaang if that #fortran code doesn't look cool - simple, formula-based... everything that Fortran should be.
TBF, I had to write MPI code in Fortran90 back in the day.
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Do you work with #benchling ELN/LIMS and wish you could query it with #rstats :rstats:? HIBio has you covered!
We've just released {benchlingapi} which connects directly to your tenant's API endpoints to get/update/add entities, notebooks, tables, IDs, etc... Completely independent of the python SDK, this is a pure R implementation.