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.

Corset plots are a visualization technique used strictly to visualize repeat measures 
  at 2 time points (such as pre- and post- data). The distribution of measurements are visualized at 
  each time point, whilst the trajectories of individual change are visualized by connecting the pre- 
  and post- values linearly. These lines can be coloured to represent the magnitude of change, or 
  other user-defined value. This method of visualization is ideal for showing the heterogeneity of 
  data, including differences by sub-groups. The package relies on 'ggplot2' allowing for easy 
  integration so that users can customize their visualizations as required. Users can create corset 
  plots using data in either wide or long format using the functions gg_corset() or gg_corset_elongated(), respectively.

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.

Activate dark mode on your favorite 'ggplot2' theme 
             with dark_mode() or use the dark versions of 
             'ggplot2' themes, including dark_theme_gray(),
             dark_theme_minimal(), and others. When a dark theme 
             is applied, all geom color and geom fill defaults 
             are changed to make them visible against a dark 
             background. To restore the defaults to their original 
             values, use invert_geom_defaults().