tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of gigs and mirai — release velocity, themes, recent moves, and the top alternatives to consider.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
gigs implements international newborn and infant growth standards — INTERGROWTH-21st, WHO — converting anthropometric measurements to z-scores and centiles and classifying growth outcomes. The 0.5.0 release rewrote the public API around rOpenSci review feedback; the two releases after it change no code at all, existing purely to get the documentation site building.
mirai removed its dispatcher process and added memory backpressure to the queue.
The async evaluation framework releases roughly monthly and moves fast at the architecture level. In 2.7.0 the dispatcher stopped being a separate process and became a thread, after its loop had already been rewritten in C inside nanonext one release earlier. The same release added an opt-in memory budget for queued task payloads and try_mirai(), which returns NULL immediately rather than blocking when that budget is exhausted.
gigs implements international newborn and infant growth standards — INTERGROWTH-21st, WHO — converting anthropometric measurements to z-scores and centiles and classifying growth outcomes. The 0.5.0 release rewrote the public API around rOpenSci review feedback; the two releases after it change no code at all, existing purely to get the documentation site building.
The package has moved from vector-in, vector-out conversion helpers to a data.frame-oriented interface with a single classify_growth() entry point that computes whatever outcomes the supplied columns allow. That is a shift from library to tool — the user describes their data rather than picking the right function. The trailing releases suggest the code is settled and the remaining work is packaging and discoverability.
With the API rewrite absorbed and hosting moved to rOpenSci, the next substantive release should add growth standards or outcomes rather than reshape the interface again.
The async evaluation framework releases roughly monthly and moves fast at the architecture level. In 2.7.0 the dispatcher stopped being a separate process and became a thread, after its loop had already been rewritten in C inside nanonext one release earlier. The same release added an opt-in memory budget for queued task payloads and try_mirai(), which returns NULL immediately rather than blocking when that budget is exhausted.
Two threads of work run together: cutting overhead out of the task path — thread-based dispatcher, in-process transport for synchronous daemons, lower per-element dispatch cost in mirai_map() — and making the framework safe to embed in an event loop, where blocking the host R thread is not acceptable. Deployment reach is growing too, with http_config() launching remote daemons over HTTP APIs and auto-configuring for Posit Workbench. Each release pins a minimum nanonext version, so the two packages advance as one unit.
With backpressure in place but opt-in, the open question these notes leave is whether a default memory budget arrives; continued overhead reduction and Shiny-facing non-blocking paths are the safer bet.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either gigs or mirai.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. mirai is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mirai is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top gigs alternatives in Analytics are ranked by recent ship velocity. Browse the "gigs alternatives" section above for the current picks, or visit /alternatives/gigs for the full list with editorial commentary on each.
Top mirai alternatives in Analytics are ranked by recent ship velocity. Browse the "mirai alternatives" section above for the current picks, or visit /alternatives/mirai for the full list with editorial commentary on each.