mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of ggfootball and qol — 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.
A SAS-to-R comfort layer that has quietly grown into its own dialect.
qol is a one-maintainer R package aimed at analysts moving from SAS: SAS-shaped verbs (compute., if./else_if., retain_value, do_if blocks), format-driven tabulation through any_table()/summarise_plus(), and styled Excel output as the default destination. Releases land roughly monthly and each one is large. The recent line has shifted from adding verbs to letting conditions be written as parsed character strings, which is the closest the package gets to reproducing SAS syntax inside R.
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.
qol is a one-maintainer R package aimed at analysts moving from SAS: SAS-shaped verbs (compute., if./else_if., retain_value, do_if blocks), format-driven tabulation through any_table()/summarise_plus(), and styled Excel output as the default destination. Releases land roughly monthly and each one is large. The recent line has shifted from adding verbs to letting conditions be written as parsed character strings, which is the closest the package gets to reproducing SAS syntax inside R.
Three threads are visible across these releases. Syntax fidelity is the newest: ifelse_multi() introduced character-string conditions with SAS-style writing, and if./else_if. immediately picked the style up. Tabulation flexibility is the constant — any_table() gains per-variable statistic selection, nested variable combinations in brackets, vector order_by, compute support. The third is ecosystem plumbing the maintainer builds when a gap appears: file I/O in 1.3.0, a console message system, global style options, macro variables, and in 1.3.2 a code_statistics() script scanner. Renames to dodge data.table and dplyr masking recur often enough to be a pattern.
The maintainer flagged the new percentile behaviour as a first iteration that only works with few grouping variables, so a performance pass on it is the clearest outstanding item. Beyond that the character-condition syntax has reached three functions in two releases and looks likely to spread to the remaining filter-bearing verbs.
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 qol.
mice can finally predict, not just estimate, from multiply imputed data.
A market-microstructure toolkit that keeps adding estimators as the papers land.
A vowel-analysis package trimming dependencies after an email address got it archived.
The R half of the EMU speech database system, fixing what was quietly broken.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
tidyplots keeps rebuilding its own foundations rather than layering around them.
See all ggfootball alternatives → · See all qol alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ggfootball and qol 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 qol 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 qol alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "qol alternatives" section above for the current picks, or visit /alternatives/qol for the full list with editorial commentary on each.