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APCalign vs ggcorrplot

A side-by-side editorial comparison of APCalign and ggcorrplot — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r

APCalign vs ggcorrplot: at a glance

FeatureAPCalignggcorrplot
SectorAnalyticsAnalytics
Velocity score2.52.5
Sparks · 30d00
Top themestaxonomy, r, australian flora, biodiversitycorrelation, r, ggplot2, visualization
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is APCalign?

APCalign spent this year fixing the counts it had been quietly getting wrong

APCalign standardises Australian plant names against the APC and APNI taxonomic resources and derives state-level native/introduced status from them. The feed is GitHub releases tagged by resource download date rather than semantic version, so titles carry no information about content. The two 2026 releases are the only substantive ones in the window: infrataxa support in the diversity functions, then a fix for a grep that had been matching the wrong columns.

Read the full APCalign trajectory →

What is ggcorrplot?

ggcorrplot came back after four years and found its significance markers had been lying

ggcorrplot draws correlation matrices in ggplot2 with optional significance marking and hierarchical reordering. It sat untouched from late 2022 until mid-2026, then shipped 0.2.0 and 0.3.0 sixteen days apart. Between them they added the display options users had been requesting since 2016 and repaired a set of bugs where hc.order = TRUE silently changed which cells were marked significant.

Read the full ggcorrplot trajectory →

APCalign vs ggcorrplot: editorial side-by-side

A
APCalign
ANALYTICS
2.5

APCalign spent this year fixing the counts it had been quietly getting wrong

◆ Current state

APCalign standardises Australian plant names against the APC and APNI taxonomic resources and derives state-level native/introduced status from them. The feed is GitHub releases tagged by resource download date rather than semantic version, so titles carry no information about content. The two 2026 releases are the only substantive ones in the window: infrataxa support in the diversity functions, then a fix for a grep that had been matching the wrong columns.

◆ Where it's heading

Development is driven by users reporting that outputs do not match what they expect, and the fixes keep landing in the same two functions — create_species_state_origin_matrix() and native_anywhere_in_australia(). The infrataxa parameter and the reordered output columns came from user requests; the guard-ordering and grep fixes came from a filed issue. What is emerging is that the origin-matrix logic was written loosely and is now being tightened case by case, with tests and state diversity benchmarks added alongside.

◆ Prediction

Both recent releases touched the same pair of functions and the fixes were found by inspection rather than by tests failing, so more corrections in the native-status path are the likely next content — the benchmarks added in March are the mechanism that would surface them.

G
ggcorrplot
ANALYTICS
2.5

ggcorrplot came back after four years and found its significance markers had been lying

◆ Current state

ggcorrplot draws correlation matrices in ggplot2 with optional significance marking and hierarchical reordering. It sat untouched from late 2022 until mid-2026, then shipped 0.2.0 and 0.3.0 sixteen days apart. Between them they added the display options users had been requesting since 2016 and repaired a set of bugs where hc.order = TRUE silently changed which cells were marked significant.

◆ Where it's heading

Both releases chase the same target: parity with the older corrplot package inside a ggplot2 object. Significance stars appended to coefficient labels, circle scaling, decimal control, then boxed cells and glyphs sized by absolute correlation — these are corrplot's visual vocabulary reimplemented where they can be composed with other ggplot2 layers. The bug fixes point the other way, at foundations: p-values matched to cells by name rather than row position, clustering computed on the unrounded matrix, tl.col actually applied.

◆ Prediction

With the corrplot look largely reproduced and the correctness backlog cleared, the remaining gap is the mixed upper/lower display corrplot supports; that is the natural next argument if the current release pace holds.

Alternatives to APCalign and ggcorrplot

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 APCalign or ggcorrplot.

See all APCalign alternatives → · See all ggcorrplot alternatives →

Recent activity from APCalign and ggcorrplot

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 16d agoAPCalignAPCalign fixes a grep that scanned taxon names for native status
  2. 21d agoggcorrplotggcorrplot 0.3.0 adds boxed cells and correlation-sized squares
  3. 1mo agoggcorrplotggcorrplot 0.2.0 fixes significance markers broken by hc.order
  4. 4mo agoAPCalignAPCalign adds infrataxa to the state origin and diversity functions
  5. 1y agoAPCalignTaxonomic resource snapshot from APC and APNI
  6. 1y agoAPCalignRelease tags switch to APC download date
  7. 1y agoAPCalignFix for the quiet option
  8. 3y agoggcorrplotggcorrplot 0.1.4
  9. 6y agoggcorrplotggcorrplot 0.1.3
  10. 7y agoggcorrplotggcorrplot 0.1.2
  11. 10y agoggcorrplotggcorrplot's first release: correlograms in ggplot2

Frequently asked questions

What is the difference between APCalign and ggcorrplot?

Both compete on the same themes — r — within Analytics. APCalign and ggcorrplot are shipping at a similar cadence (velocity 2.5 vs 2.5, 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.

Is APCalign better than ggcorrplot?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. APCalign and ggcorrplot are shipping at a similar cadence (velocity 2.5 vs 2.5, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to APCalign?

Top APCalign alternatives in Analytics are ranked by recent ship velocity. Browse the "APCalign alternatives" section above for the current picks, or visit /alternatives/apcalign-r for the full list with editorial commentary on each.

What are the best alternatives to ggcorrplot?

Top ggcorrplot alternatives in Analytics are ranked by recent ship velocity. Browse the "ggcorrplot alternatives" section above for the current picks, or visit /alternatives/ggcorrplot for the full list with editorial commentary on each.