pr2database
The protist reference database keeps widening past the rRNA gene it was built on.
A side-by-side editorial comparison of ggpointless and goat — release velocity, themes, recent moves, and the top alternatives to consider.
ggpointless keeps adding the ggplot2 layers nobody else bothered to write.
ggpointless is a small ggplot2 extension collecting geoms that sit outside the standard set — Lexis diagrams, Chaikin-smoothed paths, hanging chains, Fourier reconstructions — and it has grown steadily rather than changed shape. The May release is the largest yet: isotype and pictogram bar charts as stacks of discrete unit cells, a family of geoms that fade paths, segments, curves and reference lines along their length, and geom_gridline(), which draws grid lines as a layer on top of the data rather than beneath it.
A gene-set enrichment package that outgrew its human-only origins, then went quiet.
GOAT is a CRAN-published R package for gene set enrichment testing, now at 1.1.4. The visible arc runs from a 2024 beta through a first public CRAN release to a 1.1 line that broadened the package past human gene sets and added persistence for completed analyses. Recent releases are small: the newest ships an igraph handle on plot_network() plus bug fixes.
ggpointless is a small ggplot2 extension collecting geoms that sit outside the standard set — Lexis diagrams, Chaikin-smoothed paths, hanging chains, Fourier reconstructions — and it has grown steadily rather than changed shape. The May release is the largest yet: isotype and pictogram bar charts as stacks of discrete unit cells, a family of geoms that fade paths, segments, curves and reference lines along their length, and geom_gridline(), which draws grid lines as a layer on top of the data rather than beneath it.
Two patterns are visible. Ideas get generalized rather than left as one-offs: geom_area_fade() in the previous release established alpha gradients via grid::linearGradient(), and the recent release spreads that treatment across paths, lines, steps, segments, curves and the three reference-line geoms, each with the same fade_direction and alpha_fade_to arguments. And each new geom is expected to survive real plots — the unit charts work under coord_equal, coord_polar, coord_radial, coord_flip and faceting, and geom_gridline reads positions from trained scales and inherits styling from the theme's panel grid. The package also tracks ggplot2 closely, requiring 4.0.0 and using make_constructor() and gg_par() internally, and it dropped its bundled datasets outright rather than maintain stale copies.
The fade treatment now covers most path-like geoms but not the area and ribbon family beyond geom_area_fade(), which is where the pattern has room left to run. The unit-cell charts arrive with a label helper and no fill or grouping variants, so those are the plausible next additions.
GOAT is a CRAN-published R package for gene set enrichment testing, now at 1.1.4. The visible arc runs from a 2024 beta through a first public CRAN release to a 1.1 line that broadened the package past human gene sets and added persistence for completed analyses. Recent releases are small: the newest ships an igraph handle on plot_network() plus bug fixes.
The substantive expansion happened in the 1.1 cycle; everything since has been maintenance and plotting ergonomics. Each release since 1.1 touches one function and returns something callers previously had to reconstruct, which reads as a package settling into a stable API and responding to individual user requests rather than pursuing new scope. The 13-month gap between 1.1.2 and 1.1.4 puts it firmly in low-cadence maintenance.
Expect continued point releases that expose internals from the plotting functions or refresh the bundled GO release, not new analysis capability. The entries give no signal of a planned 1.2.
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 ggpointless or goat.
The protist reference database keeps widening past the rRNA gene it was built on.
Composable aligned layouts, rebuilt on S7 while ggplot2 4.0 lands underneath.
Conservation planning absorbs the literature's target-setting rules as code.
Joint species distribution models in Gibbs-sampled C++, quiet since 2023.
An ecosystem model starts tracking carbon isotopes and land-use change.
Ten years in, US mapping splits its data out and finally adds Puerto Rico.
See all ggpointless alternatives → · See all goat alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ggpointless and goat 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. ggpointless and goat 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 ggpointless alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ggpointless alternatives" section above for the current picks, or visit /alternatives/ggpointless for the full list with editorial commentary on each.
Top goat alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "goat alternatives" section above for the current picks, or visit /alternatives/goat for the full list with editorial commentary on each.