TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
A side-by-side editorial comparison of ggmapinset and sdsfun — release velocity, themes, recent moves, and the top alternatives to consider.
A ggplot2 inset-map extension that is now infrastructure for other packages
ggmapinset adds magnified inset panels to ggplot2 sf maps, handling the coordinate transformation, the inset frame and the sf-related stat layers that have to follow it. The 0.5.0 release is aimed less at end users than at extension authors: coerce_centre() is a new extension point required by sibling package ggautomap, and the inset parameter drops NA in favour of waiver() as its default. It comes from cidm-ph, alongside nswgeo.
A spatial-statistics utility package exists to be depended on, and is built accordingly.
sdsfun collects spatial data science utilities — neighbour lists, spatial constrained clustering, discretization, dummy variable generation, geographical detector statistics and projection helpers — with the computationally heavy parts implemented in Rcpp. It was assembled quickly across late 2024, adding a function set roughly every three weeks, and has slowed since to a couple of releases a year. The most recent work is corrective: no longer initializing the RNG state at load, fixing matrix inputs misread as vectors, and clearing an Armadillo deprecation.
ggmapinset adds magnified inset panels to ggplot2 sf maps, handling the coordinate transformation, the inset frame and the sf-related stat layers that have to follow it. The 0.5.0 release is aimed less at end users than at extension authors: coerce_centre() is a new extension point required by sibling package ggautomap, and the inset parameter drops NA in favour of waiver() as its default. It comes from cidm-ph, alongside nswgeo.
The package has moved steadily from feature to foundation. 0.3.0 replaced confusing parameter names and rebuilt everything on stat_sf_inset() so coordinate limits stayed correct, then exposed transform_to_inset() explicitly for extension developers. 0.4.0 generalised inset shapes beyond circles to rectangles and arbitrary sf geometries. 0.5.0 continues in that direction, changing defaults in ways that require downstream extensions to adapt — the cost of being depended upon.
Expect further extension points driven by what ggautomap and the other cidm-ph mapping packages need, with the user-facing inset API staying largely settled after the shape generalisation.
sdsfun collects spatial data science utilities — neighbour lists, spatial constrained clustering, discretization, dummy variable generation, geographical detector statistics and projection helpers — with the computationally heavy parts implemented in Rcpp. It was assembled quickly across late 2024, adding a function set roughly every three weeks, and has slowed since to a couple of releases a year. The most recent work is corrective: no longer initializing the RNG state at load, fixing matrix inputs misread as vectors, and clearing an Armadillo deprecation.
This is infrastructure for a family of packages rather than an end-user tool, and the changelog says so directly — functions were added to support gdverse and sesp, and moran_test was migrated in from geocomplexity. That migration pattern is the defining move: capability consolidates here so the downstream packages can share it instead of each carrying its own copy. Growth has slowed as that consolidation completed, leaving correctness and dependency upkeep.
Given the package moves when its dependents need something, the next release most likely brings in another shared function or responds to a downstream requirement rather than following its own plan. Armadillo and CRAN check changes remain the reliable source of maintenance work.
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 ggmapinset or sdsfun.
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
College football's open data client hit v2 — and now reports how many API calls you have left.
The USA phenology data client rebuilt its entire stack and stopped handing users -9999 as a number.
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.
The area-proportional Euler diagram package is finished software, and maintained like it.
See all ggmapinset alternatives → · See all sdsfun alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ggmapinset and sdsfun 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. ggmapinset and sdsfun 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 ggmapinset alternatives in Analytics are ranked by recent ship velocity. Browse the "ggmapinset alternatives" section above for the current picks, or visit /alternatives/ggmapinset for the full list with editorial commentary on each.
Top sdsfun alternatives in Analytics are ranked by recent ship velocity. Browse the "sdsfun alternatives" section above for the current picks, or visit /alternatives/sdsfun for the full list with editorial commentary on each.