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Comparison · Analytics
epikit vs ggcorrplot
A side-by-side editorial comparison of epikit and ggcorrplot — release velocity, themes, recent moves, and the top alternatives to consider.
epikit vs ggcorrplot: at a glance
What is epikit?
epikit narrows to field-epidemiology helpers, handing proportions to a sibling package
epikit is a set of small helpers for applied epidemiology in R — age categorisation, date reconstruction from partial records, and related field-data chores, developed in the R4Epis orbit. Version 0.2.0 moved the proportion functions out to epitabulate, improved how find_date_cause(), find_start_date() and find_end_date() handle dates falling outside the period, and added a floor argument to age_categories() so the lowest band reads as under one rather than zero to zero.
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.
epikit vs ggcorrplot: editorial side-by-side
epikit
0.0ANALYTICS
epikit narrows to field-epidemiology helpers, handing proportions to a sibling package
◆ Current state
epikit is a set of small helpers for applied epidemiology in R — age categorisation, date reconstruction from partial records, and related field-data chores, developed in the R4Epis orbit. Version 0.2.0 moved the proportion functions out to epitabulate, improved how find_date_cause(), find_start_date() and find_end_date() handle dates falling outside the period, and added a floor argument to age_categories() so the lowest band reads as under one rather than zero to zero.
◆ Where it's heading
The package is being scoped down rather than built out. The 0.1.3 restructuring and the 0.2.0 handover of proportions to epitabulate are the same move made twice: push functionality into the package where it belongs and keep epikit to the toolkit that field epidemiologists reach for directly. The rest of the history is dependency compatibility work against dplyr and tibble.
◆ Prediction
With proportions gone and dependencies trimmed, the remaining functions cluster tightly around dates and age bands, so further refinement of the date-reconstruction helpers is more likely than new capability areas.
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 epikit 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 epikit or ggcorrplot.
rjdqa
rjdqa keeps refining one screen: the seasonal adjustment quality dashboard
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 epikit alternatives → · See all ggcorrplot alternatives →
Recent activity from epikit and ggcorrplot
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 agoepikitProportion functions moved to epitabulate; date helpers warn correctly
- 3y agoggcorrplotggcorrplot 0.1.4
- 3y agoepikitFunctions rearranged across sibling packages
- 5y agoepikitRaise dplyr and tibble minimums; move CI to GitHub Actions
- 5y agoepikitCompatibility release for dplyr 1.0.0
- 6y agoepikitFirst CRAN release
- 6y agoggcorrplotggcorrplot 0.1.3
- 7y agoggcorrplotggcorrplot 0.1.2
- 10y agoggcorrplotggcorrplot's first release: correlograms in ggplot2
Frequently asked questions
What is the difference between epikit 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 epikit 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 epikit?
Top epikit alternatives in Analytics are ranked by recent ship velocity. Browse the "epikit alternatives" section above for the current picks, or visit /alternatives/epikit-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.