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Comparison · Analytics
ggcorrplot vs rjdqa
A side-by-side editorial comparison of ggcorrplot and rjdqa — release velocity, themes, recent moves, and the top alternatives to consider.
ggcorrplot vs rjdqa: at a glance
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.
What is rjdqa?
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
rjdqa builds quality assessment dashboards for seasonal adjustment models produced by JDemetra+, aimed at official statisticians reviewing adjusted series. Essentially all development goes into two functions, simple_dashboard() and its denser variant simple_dashboard2(). Version 0.1.6 adds parameters to append observations to the forecast and to control whether the residual trading-days test is printed, defaulting to monthly series only, plus outlier table layout work and user-defined calendar regressor support in sc_dashboard().
ggcorrplot vs rjdqa: editorial side-by-side
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.
rjdqa
0.0ANALYTICS
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
◆ Current state
rjdqa builds quality assessment dashboards for seasonal adjustment models produced by JDemetra+, aimed at official statisticians reviewing adjusted series. Essentially all development goes into two functions, simple_dashboard() and its denser variant simple_dashboard2(). Version 0.1.6 adds parameters to append observations to the forecast and to control whether the residual trading-days test is printed, defaulting to monthly series only, plus outlier table layout work and user-defined calendar regressor support in sc_dashboard().
◆ Where it's heading
The package has converged on a single deliverable and is tuning it against reviewer practice. Each release adds a parameter that lets the analyst include or exclude one element of the dashboard, or adjusts how densely information is packed into the fixed space of the layout. The td_effect default — print the test only for monthly series — is characteristic: the knowledge about when a diagnostic is meaningful is being encoded into the tool rather than left to the reader.
◆ Prediction
The pattern of adding one toggle per diagnostic per release points at the same thing again, most likely another test given a conditional default, rather than a new dashboard function alongside the two that exist.
Alternatives to ggcorrplot and rjdqa
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 ggcorrplot or rjdqa.
epikit
epikit narrows to field-epidemiology helpers, handing proportions to a sibling package
SimInf
SimInf 10.0 turns an epidemic simulator into a tool that fits models to real time series
mutagen
A young package porting Stata's egen row-wise helpers to the tidyverse, one function per release
broman
A statistician's personal toolbox, growing one plotting utility at a time
qtl
R/qtl is in pure custodial mode: every recent release answers a compiler, not a user
See all ggcorrplot alternatives → · See all rjdqa alternatives →
Recent activity from ggcorrplot and rjdqa
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 21d agoggcorrplotggcorrplot 0.3.0 adds boxed cells and correlation-sized squares
- 1mo agoggcorrplotggcorrplot 0.2.0 fixes significance markers broken by hc.order
- 9mo agorjdqaForecast observations and conditional trading-days test in dashboards
- 1y agorjdqaFix tail() usage on ts objects
- 2y agorjdqaFix dependency minimums and outlier ordering
- 2y agorjdqasimple_dashboard2() added; deprecated sa_dashboard() removed
- 2y agorjdqasimple_dashboard() introduced; sa_dashboard() deprecated
- 3y agoggcorrplotggcorrplot 0.1.4
- 6y agoggcorrplotggcorrplot 0.1.3
- 7y agorjdqaFirst CRAN release
- 7y agoggcorrplotggcorrplot 0.1.2
- 10y agoggcorrplotggcorrplot's first release: correlograms in ggplot2
Frequently asked questions
What is the difference between ggcorrplot and rjdqa?
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 ggcorrplot better than rjdqa?
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 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.
What are the best alternatives to rjdqa?
Top rjdqa alternatives in Analytics are ranked by recent ship velocity. Browse the "rjdqa alternatives" section above for the current picks, or visit /alternatives/rjdqa-r for the full list with editorial commentary on each.