Saves a 'ggplot' object into multiple files, each with a layer 
    added incrementally. Generally to be used in presentation slides.
    Flexible enough to allow different file types for the final complete plot,
    and intermediate builds.

A geom for ggplot to create bump plots. Can be good to use for showing rank over time.

R provides fantastic tools for changepoint
    analysis, but plots generated by the tools do 
    not have the 'ggplot2' style. This tool, however, 
    combines 'changepoint', 'changepoint.np' and 'ecp'
    together, and uses 'ggplot2' to visualize changepoints.

Streamline the creation of common charts by taking care of a lot of
    data preprocessing and plot customization for the user. Provides a
    high-level interface to create plots using 'ggplot2'.

Provides a Chernoff face geom for 'ggplot2'. Maps multivariate data
    to human-like faces. Inspired by Chernoff (1973) <doi:10.1080/01621459.1973.10482434>.

Contains the implementation of a binary large margin classifier based on Gabriel Graph. References for this method can be found in L.C.B. Torres et al. (2015) <doi:10.1049/el.2015.1644>.

William S. Cleveland's book 'Visualizing Data' is a classic piece 
	of literature on Exploratory Data Analysis. Although it was written 
	several decades ago, its content is still relevant as it proposes several 
	tools which are useful to discover patterns and relationships among the data 
	under study, and also to assess the goodness of fit o a model.  This package 
	provides functions to produce the 'ggplot2' versions of the visualization tools 
	described in this book and is thought to be used in the context of courses on 
	Exploratory Data Analysis.

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.