datefixR
The messy-date parser rewrote its core in Rust and came out 300x faster.
A side-by-side editorial comparison of Athlytics and sftime — 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.
The spatiotemporal companion to sf, moving at the pace of the packages around it.
sftime extends sf with an active time column, giving R a data frame class for data that is both spatial and temporal. Its recent history is almost entirely integration work: 0.3.0 added conversion methods from spatstat point patterns, sftrack and sftraj movement objects and cubble data frames, plus dedicated tidyr::drop_na() and dplyr::dplyr_reconstruct() methods. The two releases since are a namespace version-check correction and a switch from the magrittr pipe to the native pipe in examples.
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.
sftime extends sf with an active time column, giving R a data frame class for data that is both spatial and temporal. Its recent history is almost entirely integration work: 0.3.0 added conversion methods from spatstat point patterns, sftrack and sftraj movement objects and cubble data frames, plus dedicated tidyr::drop_na() and dplyr::dplyr_reconstruct() methods. The two releases since are a namespace version-check correction and a switch from the magrittr pipe to the native pipe in examples.
The package's job is to be interoperable, so its releases follow whatever the surrounding spatial and tidyverse packages do. The dplyr_reconstruct() work is the clearest example of why that matters: inheriting sf's method caused column binding to silently return an sf object where an sftime object was expected, which is the kind of class-preservation bug that only surfaces two steps downstream. Development is sparse, roughly one release a year.
Expect further conversion methods as new spatiotemporal classes appear in the R spatial ecosystem, and continued tracking of dplyr and tidyr generics. The entries do not indicate any planned change to the sftime class itself.
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 sftime.
The messy-date parser rewrote its core in Rust and came out 300x faster.
The legend engine mapsf spun out, now covering legend types the parent map package can draw.
R help pages translated on demand by whichever LLM you point it at.
Thematic mapping in base R that finally got a theming system, then spent two years polishing legends.
qualtRics moved its contact functions onto XM Directory days before the old endpoints died.
The tidyverts forecasting core rebuilt model combination on full residual covariance.
See all Athlytics alternatives → · See all sftime alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. Athlytics and sftime 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 sftime 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 sftime alternatives in Analytics are ranked by recent ship velocity. Browse the "sftime alternatives" section above for the current picks, or visit /alternatives/sftime for the full list with editorial commentary on each.