TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
A side-by-side editorial comparison of rphylopic and sdsfun — release velocity, themes, recent moves, and the top alternatives to consider.
The R package that puts organism silhouettes on plots keeps widening where they can be drawn.
rphylopic fetches PhyloPic silhouettes and places them into R graphics — base plots, ggplot2 layers, legends, and now phylogenetic trees and igraph networks. The 1.x line has been consistent about two things: adding a new plotting context per release, and steadily replacing its early sizing vocabulary with explicit width and height arguments. Attribution handling is unusually developed for a package this size, with permalinks and per-image credit built into the retrieval functions.
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.
rphylopic fetches PhyloPic silhouettes and places them into R graphics — base plots, ggplot2 layers, legends, and now phylogenetic trees and igraph networks. The 1.x line has been consistent about two things: adding a new plotting context per release, and steadily replacing its early sizing vocabulary with explicit width and height arguments. Attribution handling is unusually developed for a package this size, with permalinks and per-image credit built into the retrieval functions.
Development is expanding the set of places a silhouette can appear rather than changing what the package does. Base plots came first, then ggplot2 aesthetics and legend glyphs, then trees, then network vertices via an igraph shape registered automatically when both packages load. The other running thread is defensive maintenance against upstream churn: retries on failed API calls, fixes for ggplot2 4.0.0, and now an in-memory cache so repeated calls stop hammering the PhyloPic API. The ysize and size deprecation, opened in 1.5.0, is now complete and the arguments are scheduled for removal.
The deprecated ysize and size arguments look set to be removed in the next release, and on the pattern of the last four, another plotting context is a likelier addition than a change to the retrieval layer.
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 rphylopic 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 rphylopic alternatives → · See all sdsfun alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. rphylopic 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. rphylopic 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 rphylopic alternatives in Analytics are ranked by recent ship velocity. Browse the "rphylopic alternatives" section above for the current picks, or visit /alternatives/rphylopic 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.