mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of ggfootball and tidyprompt — 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 prompting framework hands its provider plumbing to ellmer and inherits MCP tools
tidyprompt composes LLM prompts out of stackable 'prompt wraps' — answer_as_json(), answer_as_category(), answer_using_tools() — and validates what comes back. Its recent history is one decision: stop maintaining a provider layer. llm_provider_ellmer() arrived experimental, then became the path through which structured output, tool calling and streaming are done natively. The newest release adds dataframe and numeric extraction wraps and lets send_prompt() take an ellmer chat object directly.
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.
tidyprompt composes LLM prompts out of stackable 'prompt wraps' — answer_as_json(), answer_as_category(), answer_using_tools() — and validates what comes back. Its recent history is one decision: stop maintaining a provider layer. llm_provider_ellmer() arrived experimental, then became the path through which structured output, tool calling and streaming are done natively. The newest release adds dataframe and numeric extraction wraps and lets send_prompt() take an ellmer chat object directly.
Two lines run together. One is catalogue growth — every release adds a wrap for another answer shape. The other is consolidation onto ellmer, and that is where the leverage is: because ellmer tool definitions are what mcptools::mcp_tools() returns, tidyprompt gained access to Model Context Protocol servers without writing an MCP client. Its own Gemini provider is already marked superseded. Note the feed's stamps lie — 0.1.0, 0.2.0 and 0.3.0 were all published within two hours of each other in reverse version order.
The remaining first-party providers are the obvious next thing to fold in: the Gemini one is already superseded, and the Ollama and OpenAI providers carry the same duplicated plumbing. Expect the wrap catalogue to keep growing on top of an increasingly ellmer-only base.
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 tidyprompt.
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 tidyprompt alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Infra & APIs. ggfootball and tidyprompt 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 tidyprompt 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 tidyprompt alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "tidyprompt alternatives" section above for the current picks, or visit /alternatives/tidyprompt for the full list with editorial commentary on each.