if anyone else has papers or projects that use rix, please let me know! in open source, people open issues to tell you when things don't work (which is fine) but sometimes, getting positive feedback is nice as well :D

new package alert! {tidychain} is a #rstats packaged inspired by the below authors experience in showing how an excel file was changed / manipulated by looking at the underlying xml files to prove fraud in research datacolada.org/109package usrbinr.codeberg.page/tidychain/

bracketeer is now in CRAN!!

#rstats Can the people complaining about R pipes not being "functional" enough please go hang out in the SQL forums? If any language needs help with ease of debugging and interpreting it's SQL. Thank you and please.

Agree completely. This is why I implemented glimpse style printing as the default for tbl_gpus in cuplyr

Trying to stay neutral in the #rstats flame wars but my side projects are: * model tournament structures using |> * running tibbles on gpus

Started building {bracketeer} because I needed to run a pétanque tournament for friends and couldn't believe this wasn't solved already. Turns out tournament modeling is harder than it first looks. bbtheo.github.io/bracketeer/ #rstats

Pub quiz. Office ping pong. 10,000 simulated World Cups. A Champions League bracket for your fantasy league. tournament(teams) |> swiss("open", rounds = 5) |> single_elim("playoffs", take = top_n(8)) bracketeer 0.1.0 tournament modeling in base #Rstats github.com/bbtheo/bracketeer

cuplyr version 0.1.0 is now out! A GPU-accelerated dplyr backend for R, powered by RAPIDS cuDF. Write familiar tidyverse code, execute on GPU. Lazy eval with AST optimization. In my benchmarks 60x faster than dplyr on 50M rows. github.com/bbtheo/cuplyr #rstats #cuda #DataScience

New post: the design behind secretbase, a 40KB streaming hash package I maintain. The entire source package is smaller than most hex sticker PNGs. Hash large objects with constant memory. Powers targets pipelines. Plus CBOR encoding comes to R. shikokuchuo.net/posts/28-int... #RStats #OpenSource

The first data science book that has a chapter on monads reproducible-data-science.dev Learn how to build robust #DataScience pipelines with #RStats, #Python , #Julia and #Nix !

What if your dplyr pipelines ran on GPU? That's what I built with cuplyr! A CUDA-powered backend for #rstats data manipulation. Looking for testers and feedback! github.com/bbtheo/cuplyr