fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of rphylopic and spEDM — 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.
Spatial causal discovery in R, one exposed method per release
spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.
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.
spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.
The cadence is steady and predictable: each release surfaces one more EDM method as an R-level API with a vignette, then spends the rest of its notes on parameter-handling consistency across the generics. Breaking changes are frequent and deliberate — argument renames, parameter reordering, NA-handling defaults — which reads as a package still settling its interface while the method surface expands. Shared changes appear in tEDM within days, so interface churn lands on both packages at once.
Expect the next release to expose another causality variant at the R level with an accompanying vignette, and to continue renaming or reordering parameters toward consistency across the spatial and temporal packages.
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 spEDM.
Pattern fills for ggplot2, hardened against the ways users write sizes
gcube's recent releases are all packaging metadata, not simulation code
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats keeps widening what a coefficient or Likert plot can be
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
State-space data simulation for R, filled in one function at a time
See all rphylopic alternatives → · See all spEDM alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. rphylopic and spEDM 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 spEDM 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 spEDM alternatives in Analytics are ranked by recent ship velocity. Browse the "spEDM alternatives" section above for the current picks, or visit /alternatives/spedm for the full list with editorial commentary on each.