Spatial data plus the power of the ggplot2 framework means easier mapping when input 
  data are already in the form of spatial objects.

Additional annotations, stats, geoms and scales for plotting 
    "light" spectra with 'ggplot2', together with specializations of ggplot()
    and autoplot() methods for spectral data and waveband definitions
    stored in objects of classes defined in package 'photobiology'. Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

A 'ggplot2' extension that provides flipped components:
    horizontal versions of 'Stats' and 'Geoms', and vertical versions
    of 'Positions'. This package is now superseded by 'ggplot2' itself
    which now has full native support for horizontal layouts. It
    remains available for backward compatibility.

To create the multiple polygonal point layer for easily discernible shapes, 
             we developed the package, it is like the 'geom_point' of 'ggplot2'.
             It can be used to draw the scatter plot.

Provides new statistics, new geometries and new positions for 
    'ggplot2' and a suite of functions to facilitate the creation of 
    statistical plots.

Extension of 'ggplot2', 'ggstatsplot' creates graphics with
    details from statistical tests included in the plots themselves. It
    provides an easier syntax to generate information-rich plots for
    statistical analysis of continuous (violin plots, scatterplots,
    histograms, dot plots, dot-and-whisker plots) or categorical (pie and
    bar charts) data. Currently, it supports the most common types of
    statistical approaches and tests: parametric, nonparametric, robust,
    and Bayesian versions of t-test/ANOVA, correlation analyses,
    contingency table analysis, meta-analysis, and regression analyses.
    References: Patil (2021) <doi:10.21105/joss.03236>.

Make smoothed stacked area charts in 'ggplot2'. Stream plots are useful to show magnitude trends over time.

Provides an extension to 'ggplot2' (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two 
    types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean.
    These plots contain multiple user-defined confidence areas with varying colours, 
    defined by the underlying t-distribution used to compute standard confidence intervals for 
    the mean of the normal distribution when the variance is unknown.
    Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the 
    shape (horizontal width) is defined by the probability density function of the t-distribution.