A quantile-quantile plot can be used to compare a sample of p-values 
  to the uniform distribution.  But when the dataset is big (i.e. > 1e4 p-values),
  plotting the quantile-quantile plot can be slow.  geom_QQ uses all the data to
  calculate the quantiles, but thins it out in a way that focuses on points near zero 
  before plotting to speed up plotting and decrease file size, when vector graphics are stored.

Quickly and easily perform exploratory data analysis by uploading your
     data as a 'csv' file. Start generating insights using 'ggplot2' plots and
     'table1' tables with descriptive stats, all using an easy-to-use point and click 
     'Shiny' interface.

An extension of 'ggplot2' to provide quiver plots to visualise vector fields. 
    This functionality is implemented using a geom to produce a new graphical layer, which
    allows aesthetic options. This layer can be overlaid on a map to improve visualisation
    of mapped data.

Extend 'ggplot2' facets to panel layouts arranged in a grid
    with ragged edges. facet_ragged_rows() groups panels into rows that
    can vary in length, facet_ragged_cols() does the same but for columns.
    These can be useful, for example, to represent nested or partially
    crossed relationships between faceting variables.

The 'geom_rain()' function adds different geoms together using 'ggplot2' to create raincloud plots.

Graphic elements for exploring Random Forests using the
    'randomForest' or 'randomForestSRC' package for survival, regression
    and classification forests and 'ggplot2' package plotting. Implements
    visualisations of the methods described in Breiman (2001)
    <doi:10.1023/A:1010933404324> and Ishwaran, Kogalur, Blackstone, and
    Lauer (2008) <doi:10.1214/08-AOAS169>.

The grammar of graphics as implemented in ggplot2 is a poor fit for
    graph and network visualizations due to its reliance on tabular data input.
    ggraph is an extension of the ggplot2 API tailored to graph visualizations
    and provides the same flexible approach to building up plots layer by layer.