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Comparison · Infra & APIs

ggpointless vs prova

A side-by-side editorial comparison of ggpointless and prova — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-packages

ggpointless vs prova: at a glance

Featureggpointlessprova
SectorInfra & APIsInfra & APIs
Velocity score0.06.3
Sparks · 30d01
Top themesggplot2-extensions, data-visualization, pictogram-charts, alpha-gradientsr-packages, bayesian-inference, decision-analysis, api-consolidation
Last editorial update39m ago1h ago
WebsiteVisit →Visit →

What is ggpointless?

ggpointless keeps adding the ggplot2 layers nobody else bothered to write.

ggpointless is a small ggplot2 extension collecting geoms that sit outside the standard set — Lexis diagrams, Chaikin-smoothed paths, hanging chains, Fourier reconstructions — and it has grown steadily rather than changed shape. The May release is the largest yet: isotype and pictogram bar charts as stacks of discrete unit cells, a family of geoms that fade paths, segments, curves and reference lines along their length, and geom_gridline(), which draws grid lines as a layer on top of the data rather than beneath it.

Read the full ggpointless trajectory →

What is prova?

prova adds expected-utility calculation on top of its Bayesian inference core.

prova does Bayesian nonparametric inference in R — probabilities through Pr() and qPr(), mutual information, quantile plots. Five releases in about two weeks renamed its central argument, collapsed two plotting functions into one, and then in v2.3.0 introduced exputility() for expected utilities and their revisability, with plot() and print() methods attached from the start.

Read the full prova trajectory →

ggpointless vs prova: editorial side-by-side

G
ggpointless
INFRA · APIS
0.0

ggpointless keeps adding the ggplot2 layers nobody else bothered to write.

◆ Current state

ggpointless is a small ggplot2 extension collecting geoms that sit outside the standard set — Lexis diagrams, Chaikin-smoothed paths, hanging chains, Fourier reconstructions — and it has grown steadily rather than changed shape. The May release is the largest yet: isotype and pictogram bar charts as stacks of discrete unit cells, a family of geoms that fade paths, segments, curves and reference lines along their length, and geom_gridline(), which draws grid lines as a layer on top of the data rather than beneath it.

◆ Where it's heading

Two patterns are visible. Ideas get generalized rather than left as one-offs: geom_area_fade() in the previous release established alpha gradients via grid::linearGradient(), and the recent release spreads that treatment across paths, lines, steps, segments, curves and the three reference-line geoms, each with the same fade_direction and alpha_fade_to arguments. And each new geom is expected to survive real plots — the unit charts work under coord_equal, coord_polar, coord_radial, coord_flip and faceting, and geom_gridline reads positions from trained scales and inherits styling from the theme's panel grid. The package also tracks ggplot2 closely, requiring 4.0.0 and using make_constructor() and gg_par() internally, and it dropped its bundled datasets outright rather than maintain stale copies.

◆ Prediction

The fade treatment now covers most path-like geoms but not the area and ribbon family beyond geom_area_fade(), which is where the pattern has room left to run. The unit-cell charts arrive with a label helper and no fill or grouping variants, so those are the plausible next additions.

P
prova
INFRA · APIS
6.3

prova adds expected-utility calculation on top of its Bayesian inference core.

◆ Current state

prova does Bayesian nonparametric inference in R — probabilities through Pr() and qPr(), mutual information, quantile plots. Five releases in about two weeks renamed its central argument, collapsed two plotting functions into one, and then in v2.3.0 introduced exputility() for expected utilities and their revisability, with plot() and print() methods attached from the start.

◆ Where it's heading

Two arcs run in parallel. One compresses the API: learnt= became K=, flexiplot() and plotquantiles() merged into pplot(), and omitting arguments such as Y=, X= and K= got simpler. The other extends reach — mutualinfoF() for finite-domain variates, quantile accuracy reported alongside mutual information, and now a decision-theoretic layer sitting on the inference the package already did.

◆ Prediction

exputility() shipping with print() and plot() methods matches how the probability and mutual-information classes were treated, so utilities are likely to get the same class-based handling as they mature. The notes do not say whether decision analysis extends beyond expected utility.

Alternatives to ggpointless and prova

Other Infra & APIs 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 ggpointless or prova.

See all ggpointless alternatives → · See all prova alternatives →

Recent activity from ggpointless and prova

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 13d agoprovaexputility() brings decision analysis into prova
  2. 20d agoprovaCumulative 2.x notes, plus hist() and mutual-information changes
  3. 23d agoprovalearnt= becomes K=; pplot() replaces two plot functions
  4. 27d agoprovaFix for pre-existing parallel clusters
  5. 28d agoprovaextraDistr dropped; mutual-information objects get a class
  6. 3mo agoggpointlessPictogram unit charts, gridline layers and a family of fading geoms
  7. 5mo agoggpointlessFourier and arch geoms, area fades and glowing points
  8. 2y agoggpointlessgeom_catenary() draws a hanging chain
  9. 3y agoggpointlessgeom_chaikin() adds corner-cutting path smoothing
  10. 4y agoggpointlessgeom_lexis() and the female_leaders dataset

Frequently asked questions

What is the difference between ggpointless and prova?

Both compete on the same themes — r-packages — within Infra & APIs. prova 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.

Is ggpointless better than prova?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. prova 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 Infra & APIs products to evaluate alongside.

What are the best alternatives to ggpointless?

Top ggpointless alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ggpointless alternatives" section above for the current picks, or visit /alternatives/ggpointless for the full list with editorial commentary on each.

What are the best alternatives to prova?

Top prova alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "prova alternatives" section above for the current picks, or visit /alternatives/prova for the full list with editorial commentary on each.