A collection of Geoms for R's 'ggplot2' library. geom_shadowpath(), geom_shadowline(), 
    geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more 
    aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a 
    neon glow around lines to get a steampunk style.

The grammar of graphics as shown in 'ggplot2' has provided
  an expressive API for users to build plots. 'ggside' extends 'ggplot2'
  by allowing users to add graphical information about one of the main panel's
  axis using a familiar 'ggplot2' style API with tidy data. This package is
  particularly useful for visualizing metadata on a discrete axis, or summary
  graphics on a continuous axis such as a boxplot or a density distribution.

Enrich your 'ggplots' with group-wise comparisons.
    This package provides an easy way to indicate if two groups are
    significantly different. Commonly this is shown by a bracket on top
    connecting the groups of interest which itself is annotated with the
    level of significance (NS, *, **, ***).  The package provides a single
    layer (geom_signif()) that takes the groups for comparison and the
    test (t.test(), wilcox.text() etc.) as arguments and adds the
    annotation to the plot.

Provides 'ggplot2' geoms that render with a hand-drawn, sketchy
    aesthetic: roughened strokes, double-pass lines, and hachure, cross-hatch,
    zigzag, and dots fill patterns. Implemented as pure-R 'grid' grobs wrapped
    in 'ggproto' geoms, composable with aes(), stats, scales, and faceting.
    Works on every R graphics device (PDF, PNG, SVG, screen) with no browser
    dependency. Algorithms are reimplemented from the published 'rough.js'
    algorithm description (Shihn, 2020,
    <https://shihn.ca/posts/2020/roughjs-algorithms/>) and Wood and others
    (2012, <doi:10.1109/TVCG.2012.262>); see the NOTICE file in the package
    sources for attribution.

Provides 'ggplot2' extensions for creating skewed boxplots
    using several statistical methods (Kimber, 1990 <doi:10.2307/2347808>; Hubert and Vandervieren, 2008 <doi:10.1016/j.csda.2007.11.008>; Adil et al., 2015 <doi:10.18187/pjsor.v11i1.500>; Babura et al., 2017 <doi:10.1063/1.4982872>; Walker et al., 2018 <doi:10.1080/00031305.2018.1448891>). The package implements custom
    statistical transformations and geometries to visualize data
    distributions with an emphasis on skewness.