qqman
The Manhattan-plot package for GWAS results, finished and dormant since 2017.
Comparison · Analytics
constants vs ggcorrplot
A side-by-side editorial comparison of constants and ggcorrplot — release velocity, themes, recent moves, and the top alternatives to consider.
constants vs ggcorrplot: at a glance
What is constants?
The R package for CODATA constants rebuilt its symbol table on NIST's naming so future updates stop being hand work.
constants exposes the CODATA recommended values of the physical constants to R, as a data frame plus symbol lists that carry units, uncertainties, or both. The package reached 1.0.0 on the 2018 CODATA release and has shipped once since, purely to track a units package update. Its surface is small and its release cadence is bound to CODATA, which revises every few years.
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.
constants vs ggcorrplot: editorial side-by-side
constants
0.0ANALYTICS
The R package for CODATA constants rebuilt its symbol table on NIST's naming so future updates stop being hand work.
◆ Current state
constants exposes the CODATA recommended values of the physical constants to R, as a data frame plus symbol lists that carry units, uncertainties, or both. The package reached 1.0.0 on the 2018 CODATA release and has shipped once since, purely to track a units package update. Its surface is small and its release cadence is bound to CODATA, which revises every few years.
◆ Where it's heading
The direction set at 1.0.0 was to stop being a curated convenience wrapper and become a mechanical mirror of NIST. Hand-crafted symbol names were replaced with NIST's own ASCII symbols, categories adopted NIST's, and uncertainty switched from relative to absolute — all framed by the maintainer as necessary to make future CODATA updates routine. On top of that the package gained a correlation matrix and optional integration with the quantities package, moving it from a lookup table toward something that can propagate uncertainty.
◆ Prediction
Having rebuilt the symbol table specifically so CODATA revisions become mechanical, the next substantive release most likely tracks a new CODATA dataset rather than adding API. The experimental correlated-value support, disabled by default at 1.0.0, is the one part these entries flag as unfinished.
ggcorrplot
2.5ANALYTICS
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 constants 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 constants or ggcorrplot.
austraits
The R client for AusTraits spends its releases chasing the dataset it reads.
ggdemetra
A ggplot2 layer for seasonal adjustment output, filling in one plot type at a time.
fossilsim
A fossil-record simulator that quietly grew a trait-evolution engine.
tern.rbmi
Reference-based multiple imputation tables, shipping only what CRAN checks demand.
tern.mmrm
An MMRM tabulation package that has published nothing since its 2024 CRAN releases.
See all constants alternatives → · See all ggcorrplot alternatives →
Recent activity from constants and ggcorrplot
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 22d agoggcorrplotggcorrplot 0.3.0 adds boxed cells and correlation-sized squares
- 1mo agoggcorrplotggcorrplot 0.2.0 fixes significance markers broken by hc.order
- 3y agoggcorrplotggcorrplot 0.1.4
- 5y agoconstantsCompatibility fix for units 0.7-0
- 5y agoconstantsconstants 1.0.0 ⚡
- 6y agoggcorrplotggcorrplot 0.1.3
- 7y agoggcorrplotggcorrplot 0.1.2
- 8y agoconstantsUnit handling fixes ahead of the 1.0.0 rebuild
- 10y agoggcorrplotggcorrplot's first release: correlograms in ggplot2
Frequently asked questions
What is the difference between constants and ggcorrplot?
They serve adjacent needs but don't currently overlap on shipped themes. ggcorrplot is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is constants better than ggcorrplot?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggcorrplot is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to constants?
Top constants alternatives in Analytics are ranked by recent ship velocity. Browse the "constants alternatives" section above for the current picks, or visit /alternatives/constants-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.