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ggdist: Visualizations of distributions and uncertainty

Authors: Matthew Kay;

ggdist: Visualizations of distributions and uncertainty

Abstract

Major changes: The geom_slabinterval() and geom_dotsinterval() families gain "sub-guides", which can be passed to the subguide parameter to create axis annotations for the thickness aesthetic (for slabs) and the dot count (for dots) (#183). The weight aesthetic is now supported in stat_slabinterval(), including weighted calculations for densities, CDFs, all interval types (quantile intervals, highest density intervals, and highest density continuous intervals), and all point summaries (mean, median, and mode) (#41). This includes support for the upcoming weighted random variable type in the posterior package. Blurry dotplots are now supported using geom_blur_dots(), which accepts an sd aesthetic to set the standard deviation of the blur on each dot. Intervals can also be used in place of blur by passing blur = "interval". This geom is used by the new stat_mcse_dots() to show quantiles along with their error using blur (#63). The new breaks_quantiles() histogram breaks function allows the construction of quantile histograms with density_histogram(), stat_histinterval(), etc. The color ramp scales (e.g. scale_colour_ramp_continuous(), ...) now use an explicit data type, partial_colour_ramp(), to encode color ramps and their origin colors, and provide the ramp_colours() function for applying colour ramps. This should make it easier to pass explicit color ramps without using scale functions, and for packages building on {ggdist} to use the colour ramp scales (#209). Minor changes: The default histogram bin selection algorithm is now "Scott" instead of "Sturges", as "Sturges" tends to be too conservative (#214). The at parameter to stat_spike() (or its names) now determines values of an at computed variable, which can be mapped onto aesthetics via after_stat() to more easily label spikes. (#203; thanks @mattansb for the suggestion). The arrow parameter is now supported for intervals in geom_slabinterval() (#206; thanks to @ASKurz for the suggestion). The default value of overflow in geom_dotsinterval() is now the new "warn" mode, which works the same as "keep" except that it warns users if the dots will overflow the geometry bounds and suggests solutions (#213). Optional arguments to automatically partially-applied functions can now be passed a waiver() to use their default value (see auto_partial()). Several dependency reductions: removed {cowplot}, {purrr}, {forcats}, {palmerpenguins}, and {modelr} from Suggests; moved {tidyselect} and {dplyr} from Imports to Suggests. The latter two are only strictly necessary for curve_interval() due to its use of grouped data frames and tidy selection to specify which columns are conditional and which are joint (the use of grouped data frames with point_interval() is less strictly necessary, and not used by stats, so is easier to avoid as an absolute dependency). Documentation: The pkgdown documentation now includes an online article on the thickness aesthetic with comprehensive examples of how slab scaling works (#205). Bug fixes: Ensure Mode() works on analytical constant distributions. Various fixes to ensure compatibility with {ggplot2} 3.5.0.

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    25
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
25
Top 10%
Average
Top 10%