Whatagraph
Whatagraph keeps fixing what breaks when one account runs a thousand sources.
A side-by-side editorial comparison of gtsummary and Plotly — release velocity, themes, recent moves, and the top alternatives to consider.
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.
Plotly is turning its cloud into a metered compute platform with an enterprise on-ramp.
Plotly ships on two tracks. Plotly Studio, the desktop AI app-builder, releases every one to two weeks and has spent v0.0.80 through v0.0.86 on credential handling, reasoning transparency, personalization and now the reliability of the agent session engine itself. Plotly Cloud is the louder track: since late May it has added viewer-seat pricing, domain verification, per-app compute modes with credit-metered billing, and customer-owned domains with managed TLS.
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.
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.
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.
Plotly ships on two tracks. Plotly Studio, the desktop AI app-builder, releases every one to two weeks and has spent v0.0.80 through v0.0.86 on credential handling, reasoning transparency, personalization and now the reliability of the agent session engine itself. Plotly Cloud is the louder track: since late May it has added viewer-seat pricing, domain verification, per-app compute modes with credit-metered billing, and customer-owned domains with managed TLS.
The Cloud releases are assembling the standard pieces of a hosting business in order — identity first (domain verification, explicitly framed as the step before SSO), then billing (viewer seats, then metered compute credits), and now production-grade serving (custom domains, automatic certificate renewal). Studio is being hardened as the authoring front end that feeds it: Universal Deployment pushed beyond Dash apps, credentials saved once and reused, a Winget channel to widen Windows installs, and in v0.0.86 a rebuilt session engine plus automatic retries so agent runs survive expired tokens. The two tracks converge on one funnel — author in Studio, deploy to Cloud, pay by compute consumed.
The Domain Verification entry names SSO as the next step and places it in the Enterprise tier, so single sign-on is the most likely Cloud release next. Studio should hold its one-to-two-week cadence, with the newly added app thumbnails pointing toward more work on browsing and organizing generated apps.
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 gtsummary or Plotly.
Whatagraph keeps fixing what breaks when one account runs a thousand sources.
Lightdash keeps handing authoring to outside agents and keeping the governed layer for itself.
A 4.4.0 tag appears, but the feed carries only its release plumbing
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
Basedash keeps pushing its data out of the workspace — now to people without accounts
RStudio ships through release branches, and the notes are commit messages
See all gtsummary alternatives → · See all Plotly alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Plotly is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Plotly is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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.
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.
Top Plotly alternatives in Analytics are ranked by recent ship velocity. Browse the "Plotly alternatives" section above for the current picks, or visit /alternatives/plotly for the full list with editorial commentary on each.