TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
A side-by-side editorial comparison of nswgeo and sdsfun — release velocity, themes, recent moves, and the top alternatives to consider.
NSW boundary data for R, refreshed as the official sources move
nswgeo packages New South Wales geographic boundaries for R — suburbs, postcodes, local government areas, Primary Health Networks and Local Health Districts — as ready-to-plot sf datasets. The 0.6.0 release refreshes nearly all of them against new upstream sources, moving postcodes to 2021 ABS boundaries and taking LHD boundaries from a new official feed. It is maintained by cidm-ph alongside the mapping packages that consume it, including ggmapinset.
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.
nswgeo packages New South Wales geographic boundaries for R — suburbs, postcodes, local government areas, Primary Health Networks and Local Health Districts — as ready-to-plot sf datasets. The 0.6.0 release refreshes nearly all of them against new upstream sources, moving postcodes to 2021 ABS boundaries and taking LHD boundaries from a new official feed. It is maintained by cidm-ph alongside the mapping packages that consume it, including ggmapinset.
Every release is dictated by an upstream release calendar rather than a roadmap: the 2023 ASGS, then 2024, then the 2021 ABS postcode boundaries and the new LHD source. That makes field-name churn the package's defining hazard — LGA_NAME_2021 to LGA_NAME_2023 to LGA_NAME_2024, and now lhd_name carrying a Local Health District suffix. The maintainer's habit of registering compatibility aliases through cartographer shows an awareness that these renames break downstream code silently.
Expect the next release to track the following ASGS edition with another round of field renames, and any new content to stay in the health-geography area the package's users work in.
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 nswgeo 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 nswgeo 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. nswgeo 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. nswgeo 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 nswgeo alternatives in Analytics are ranked by recent ship velocity. Browse the "nswgeo alternatives" section above for the current picks, or visit /alternatives/nswgeo 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.