rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
ggstatsplot vs gtsummary
A side-by-side editorial comparison of ggstatsplot and gtsummary — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
ggstatsplot vs gtsummary: at a glance
What is ggstatsplot?
ggstatsplot reached 1.0 by adding tests, having outsourced its statistics years ago.
ggstatsplot produces ggplot2 graphics with statistical test results embedded in the subtitle and caption — comparisons, correlations, contingency tables, histograms. Since the 2019 refactoring that moved all statistical computation into the separate statsExpressions package, its own release notes have been dominated by upstream tracking: adapting to ggplot2, dplyr, purrr and easystats changes. The 1.0.0 release in April 2026 breaks that run with real additions to the contingency-table functions.
What is gtsummary?
gtsummary is quietly rebuilding itself around analysis results data, one table verb at a time.
gtsummary builds publication-ready summary, regression and survival tables for clinical and epidemiological work. Across this window it has grown in two directions at once: table composition primitives — splitting tables by rows and columns, stacking with labeled IDs, nested strata stacks, flexible merge columns — and a steadily deepening ARD layer, where tbl_ard_* functions, gather_ard() and the hierarchical table family expose the underlying analysis results data as a first-class object.
ggstatsplot vs gtsummary: editorial side-by-side
ggstatsplot
0.0ANALYTICS
ggstatsplot reached 1.0 by adding tests, having outsourced its statistics years ago.
◆ Current state
ggstatsplot produces ggplot2 graphics with statistical test results embedded in the subtitle and caption — comparisons, correlations, contingency tables, histograms. Since the 2019 refactoring that moved all statistical computation into the separate statsExpressions package, its own release notes have been dominated by upstream tracking: adapting to ggplot2, dplyr, purrr and easystats changes. The 1.0.0 release in April 2026 breaks that run with real additions to the contingency-table functions.
◆ Where it's heading
The architecture explains the cadence. With statistics living in statsExpressions, ggstatsplot's own releases are mostly the tax of sitting on top of a fast-moving plotting and tidyverse stack — five of the six most recent entries change nothing a user would notice. When substantive work does arrive it clusters in the plotting layer's coverage of test families, as in 1.0.0's one-sample goodness-of-fit support and pairwise contingency analyses. The maintainer is also visibly deliberate about scope, having removed the normality-curve overlay in 0.12.4 for being unrelated to the analysis in question.
◆ Prediction
Expect continued parity work across the plot family — features that exist in one function being extended to its siblings, as goodness-of-fit support moved from ggpiestats to ggbarstats — punctuated by maintenance releases tracking ggplot2 and easystats.
gtsummary
0.0ANALYTICS
gtsummary is quietly rebuilding itself around analysis results data, one table verb at a time.
◆ Current state
gtsummary builds publication-ready summary, regression and survival tables for clinical and epidemiological work. Across this window it has grown in two directions at once: table composition primitives — splitting tables by rows and columns, stacking with labeled IDs, nested strata stacks, flexible merge columns — and a steadily deepening ARD layer, where tbl_ard_* functions, gather_ard() and the hierarchical table family expose the underlying analysis results data as a first-class object.
◆ Where it's heading
The ARD work is the through-line. Table IDs exist so gather_ard() can return a named list; hierarchical tables gained per-level sorting and targeted filtering; ARD inputs are pre-processed so sorting applies to non-standard shapes. The package is becoming a structured-results engine that happens to render tables, rather than a renderer alone. Alongside that, 2.2.0 restored data pre-processing that 2.0 had removed after the reduced functionality hurt users — a maintainer willing to reverse a major-version decision.
◆ Prediction
Expect the hierarchical and ARD functions, introduced as a preview without a full deprecation cycle, to keep stabilizing toward a settled API rather than new table types appearing.
Alternatives to ggstatsplot and gtsummary
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 ggstatsplot or gtsummary.
audubon
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
affiner
affiner is quietly turning a grid transformation helper into a small computational geometry library.
ageproR
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
r-ledger
ledger adds a Rust toolchain fallback, so beancount imports work whether or not the Python tooling is installed.
gridpattern
gridpattern keeps widening its catalogue, and the newest patterns finally use the device's own line rendering.
See all ggstatsplot alternatives → · See all gtsummary alternatives →
Recent activity from ggstatsplot and gtsummary
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agogtsummaryTheme elements no longer evaluated by default
- 3mo agoggstatsplotPairwise contingency tests and one-sample goodness-of-fit
- 4mo agoggstatsplotInternal maintenance only
- 6mo agoggstatsplotAdapted to dplyr 1.2.0 and purrr 1.2.1
- 8mo agoggstatsplotContributor list updated in DESCRIPTION
- 8mo agogtsummaryARD strata functions and finer theme control
- 10mo agoggstatsplotSecondary axis label parsing fixed in gghistostats
- 11mo agoggstatsplotAdapted to the latest ggplot2 release
- 11mo agogtsummaryPer-level hierarchical sorting and labeled stacking
- 1y agogtsummaryTable splitting, ID labeling, and add_difference_row
- 1y agogtsummaryData pre-processing restored after the 2.0 removal
- 1y agogtsummarytbl_merge gains explicit merge columns
Frequently asked questions
What is the difference between ggstatsplot and gtsummary?
Both compete on the same themes — r-package — within Analytics. ggstatsplot and gtsummary 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.
Is ggstatsplot better than gtsummary?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggstatsplot and gtsummary 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 Analytics products to evaluate alongside.
What are the best alternatives to ggstatsplot?
Top ggstatsplot alternatives in Analytics are ranked by recent ship velocity. Browse the "ggstatsplot alternatives" section above for the current picks, or visit /alternatives/ggstatsplot for the full list with editorial commentary on each.
What are the best alternatives to gtsummary?
Top gtsummary alternatives in Analytics are ranked by recent ship velocity. Browse the "gtsummary alternatives" section above for the current picks, or visit /alternatives/gtsummary-r for the full list with editorial commentary on each.