pr2database
The protist reference database keeps widening past the rRNA gene it was built on.
A side-by-side editorial comparison of ggfootball and sits — release velocity, themes, recent moves, and the top alternatives to consider.
A football-viz package just swapped scraping for an API and broke its own output to do it.
ggfootball is a small R package for plotting expected-goals and shot data, sourced from Understat. Four releases are visible. The 0.2.x line was argument tidying and dependency pruning; 0.3.0 replaced the data-acquisition layer wholesale, moving get_match_shots() from HTML parsing onto Understat's AJAX endpoints and changing the returned column names in the process.
An R package for satellite time series just grew a Python API.
sits classifies satellite image time series — building data cubes from cloud archives, training deep learning models on them, and producing land-cover maps. The releases here are dense feature lists in a steady 1.5.x line, and two themes recur in every one: more source collections wired in, and more of the classification pipeline made parallel or chunked. Version 1.5.3 added pysits, a Python API onto the same engine.
ggfootball is a small R package for plotting expected-goals and shot data, sourced from Understat. Four releases are visible. The 0.2.x line was argument tidying and dependency pruning; 0.3.0 replaced the data-acquisition layer wholesale, moving get_match_shots() from HTML parsing onto Understat's AJAX endpoints and changing the returned column names in the process.
The direction is away from scraped HTML and toward a thinner, more defensible package: four dependencies dropped in 0.3.0 on top of qdapRegex in 0.2.1, input validation added, error messages rewritten. Both breaking changes so far were accepted rather than deferred, which reads as a maintainer treating pre-1.0 as the window to get the shape right. The package is willing to break callers for structural reasons, not cosmetic ones.
With the scraper rebuilt and the dependency surface trimmed, the next releases are likely to stabilise the new column names and extend the plotting side, which has seen nothing since 0.2.0. A 1.0 would be the signal that the data structure is now considered fixed.
sits classifies satellite image time series — building data cubes from cloud archives, training deep learning models on them, and producing land-cover maps. The releases here are dense feature lists in a steady 1.5.x line, and two themes recur in every one: more source collections wired in, and more of the classification pipeline made parallel or chunked. Version 1.5.3 added pysits, a Python API onto the same engine.
The package is positioning itself as the interface layer to Earth observation archives rather than as an algorithm library. Each release absorbs another provider — Planetary Computer, Digital Earth Africa and Australia, CDSE, TERRASCOPE, Open Geo Hub, PLANET — so the differentiator is coverage and the uniform cube abstraction over it. The Python API extends the same logic to the language most of that community actually works in. Alongside, the work is increasingly about scale: chunk parallelisation, multicores sampling, GPU classification, WebGL rendering.
With collections still being added release over release, expect more providers and continued performance work on the classification and regularisation paths. The open question the entries do not answer is how far pysits tracks the R API, since it appears once and is not mentioned again in later releases.
Other Infra & APIs 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 ggfootball or sits.
The protist reference database keeps widening past the rRNA gene it was built on.
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See all ggfootball alternatives → · See all sits alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. ggfootball and sits 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. ggfootball and sits 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 Infra & APIs products to evaluate alongside.
Top ggfootball alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ggfootball alternatives" section above for the current picks, or visit /alternatives/ggfootball for the full list with editorial commentary on each.
Top sits alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "sits alternatives" section above for the current picks, or visit /alternatives/sits for the full list with editorial commentary on each.