tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of Athlytics and datasetjson — release velocity, themes, recent moves, and the top alternatives to consider.
A Strava analytics package spent its 1.0 cycle surviving rOpenSci review, not adding features.
Athlytics computes endurance-training metrics — ACWR, EWMA load, efficiency factor, decoupling, personal bests — from Strava exports. Every release in view is review-driven: test-suite consolidation, dataset renames, styler passes, and a substantial robustness pass over the metric calculations and stream parsers. 1.0.6 explicitly changes nothing but packaging metadata.
datasetjson rebuilt its object model to track the CDISC Dataset-JSON 1.1 schema.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
Athlytics computes endurance-training metrics — ACWR, EWMA load, efficiency factor, decoupling, personal bests — from Strava exports. Every release in view is review-driven: test-suite consolidation, dataset renames, styler passes, and a substantial robustness pass over the metric calculations and stream parsers. 1.0.6 explicitly changes nothing but packaging metadata.
The package is optimising for credibility rather than surface area. It completed rOpenSci peer review, moved to an offline ZIP export workflow with hardened TCX/GPX parsing, corrected the EWMA half-life mapping, and deliberately softened its ACWR language away from injury-risk claims. Version numbers are also being published out of order, which makes the feed a poor guide to what shipped when.
With review complete and packaging metadata frozen for archival, the next substantive release is more likely to extend metric coverage or data sources than to continue polishing; nothing in these entries points to a specific new metric.
datasetjson reads and writes CDISC Dataset-JSON, the JSON replacement for SAS transport files in clinical-trial submissions. The package went from a thin reader in 2023 to a redesigned interface in 0.3.0 that targets the 1.1.0 schema, uses yyjsonr as its JSON backend, and exposes column metadata as first-class arguments. Development is contributor-driven inside the Atorus and pharmaverse orbit.
The package's roadmap is not its own — it tracks a CDISC standard that is still moving, and 0.3.0 is what happens when the standard revises: object model, read and write paths, and JSON backend all changed together. Performance was addressed in the same pass, which matters because submission datasets are large enough that a slow serialiser is a real constraint.
The next significant release will most likely follow the next Dataset-JSON schema revision rather than an internal roadmap, given that 0.3.0 was driven entirely by the 1.1.0 update.
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 Athlytics or datasetjson.
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.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all Athlytics alternatives → · See all datasetjson alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. Athlytics and datasetjson are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). 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. Athlytics and datasetjson are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top Athlytics alternatives in Analytics are ranked by recent ship velocity. Browse the "Athlytics alternatives" section above for the current picks, or visit /alternatives/athlytics for the full list with editorial commentary on each.
Top datasetjson alternatives in Analytics are ranked by recent ship velocity. Browse the "datasetjson alternatives" section above for the current picks, or visit /alternatives/datasetjson for the full list with editorial commentary on each.