Add mean comparison annotations to a 'ggplot'. 
             This package provides an easy way to indicate if two or more groups are significantly different in a 'ggplot'. 
             Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. 
             For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric).

Create correlation heatmaps with 'ggplot2' and customise them
    with flexible annotation and clustering. Symmetric heatmaps can use
    triangular or mixed layouts, removing redundant information or displaying
    complementary information in the two halves. There is also support for
    general heatmaps not displaying correlations.

The 'ggcorrplot' package can be used to visualize easily a
    correlation matrix using 'ggplot2'. It provides a solution for
    reordering the correlation matrix and displays the significance level
    on the plot. It also includes a function for computing a matrix of
    correlation p-values.

A 'ggplot2' extension for creating 3D figures.
    Provides 3D geoms, stats, and a coord_3d() coordinate
    system supporting rotation, perspective, and lighting.

Tidy, analyze, and plot directed acyclic graphs (DAGs).
    'ggdag' is built on top of 'dagitty', an R package that uses the
    'DAGitty' web tool (<https://dagitty.net/>) for creating and analyzing
    DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using
    'ggplot2' and 'ggraph', as well as common analytic and graphical
    functions, such as determining adjustment sets and node relationships.

It provides a custom 'ggplot2' geom to add day/night patterns to plots.
    It visually distinguishes daytime and nighttime periods. It is useful for 
    visualizing data that spans multiple days and for highlighting diurnal patterns.