#fastverse v0.3.0 is out, lighter than before, with an ability to install development versions from r-universe. The README (github.com/fastverse/fast…) was updated to reflect the state of high-performance in R. Those leaving twitter can also follow fosstodon.org/@sebkrantz #RStats

#rcollapse 1.9 has been released to CRAN, providing greater performance and versatility in almost every domain, alongside new functionality such as grouped & weighted sample quantiles (in C) pushing the frontiers of #rstats. News: sebkrantz.github.io/collapse/news/… #fastverse #DataScience

Released a C/C++ patch (v1.9.2), which includes a noteworthy addition: function set_collapse(nthreads = [int], na.rm = [TRUE|FALSE]) can be used to globally set argument defaults. This is worthwhile on larger projects (e.g. M1 Mac + 4 threads = >10Gb's data crunching). #rstats

I've created a small video tutorial about the new global argument default settings and OpenMP multithreading in {collapse}: youtube.com/watch?v=ne4Es2… #rstats #rcollapse #fastverse

{collapse} v1.9.5 is released - with a limited set of SIMD instructions. As of last week, collapse has been downloaded 1 million times from CRAN. I thus wrote a post reflecting on the past, present state and future of #rcollapse and #fastverse in #rstats: sebkrantz.github.io/Rblog/2023/04/…

Released a minor update {collapse} v1.9.6, which, notably, includes a new vignette on how {collapse} handles R objects - a quick view behind the scenes of its class-agnostic R programming framework: sebkrantz.github.io/collapse/artic… #rcollapse #rstats

As I'm slowly moving towards the release of collapse 2.0, you have again opportunities to explore features in the development version and provide valuable feedback (API, performance, bugs etc.). In particular join() and pivot() are major innovations and likely of interest.

I’m thrilled to announce the release of {collapse} 2.0, adding blazing fast joins, pivots, a flexible namespace, and many other features. It is a remarkable piece of R software and capable of enhancing the workflow of all R users. Spread the word #rstats sebkrantz.github.io/Rblog/2023/10/…

{collapse} has been benchmarked in the DuckDB benchmark: duckdblabs.github.io/db-benchmark/, and is pretty competitive on 0.5-5Gb (laptop-grade) operations. A surprise is that it seems to be the only framework next to DuckDB to be able perform large data joins (50Gb) efficiently. #rstats

An article on {collapse} is available on arXiv: arxiv.org/abs/2403.05038 (submitted to Journal of Statistical Software). It highlights the aims and added value of collapse and its cutting-edge performance for many complex statistical tasks in #rstats. Please consider sharing it.

{collapse} v2.0.15, already available via install.packages("collapse", repos = "fastverse.r-universe.dev"), adds wide/recast pivot()'s with aggregation, including some hard-coded internal functions. A game changer for pivot tables in R. More at sebkrantz.github.io/collapse/refer…. #rstats

New independent benchmark by Adrian Antico: github.com/AdrianAntico/B… Setup: - large local Windows machine - real data - broad range of tasks - scripts executed inside Rstudio and VScode -> shows that {collapse} is an absolute top performer in this setting #rstats #DataScience

{collapse} v2.0.15, with fast aggregation pivots, has just reached CRAN. A minor but neat feature worth pointing out in this release is enhanced join verbosity. In addition to the join success rates, the join relationship is now determined and reported - at no extra cost #rstats

Check out the latest package to be granted the Seal of Approval: {collapse} by Sebastian Krantz! {collapse} is a partner package, that implements various data transformation and statistical analysis tasks using ultra fast C/C++ implementations. rdatatable-community.github.io/The-Raft/posts…

There is now a #fastverse benchmark wiki (github.com/fastverse/fast…) where users can freely contribute benchmarks. If you have benchmarks involving {fastverse} packages ({collapse}, {data.table}, etc., including extensions) please contribute them (takes 1 min) #rstats #DataScience

The {collapse} arXiv paper has just been updated - following extensive revision: arxiv.org/abs/2403.05038. I believe it is a great resource for anyone doing scientific computing with #rstats.

{collapse} 2.1.0 is out! It introduces a new fslice() function (sebkrantz.github.io/collapse/refer…), a new theory-consistent weighted quantile algorithm (sebkrantz.github.io/collapse/refer…) with excellent properties. And some convenience features such as join requirements: #rstats #DataScience