ggtern, an extension to ggplot2
specifically for the plotting of ternary diagrams.
ggExtra, a
collection of functions and layers to enhance ggplot2. The main function
is ggMarginal, which can be used to add marginal
histograms/boxplots/density plots to ggplot2 scatterplots.
ggthemes,
some extra themes, geoms, and scales for ‘ggplot2’. Provides ‘ggplot2’
themes and scales that replicate the look of plots by Edward Tufte,
Stephen Few, ‘Fivethirtyeight’, ‘The Economist’, ‘Stata’, ‘Excel’, and
‘The Wall Street Journal’, among others. Provides ‘geoms’ for Tufte’s
box plot and range frame.
GGally extends
‘ggplot2’ by adding several functions to reduce the complexity of
combining geometric objects with transformed data. Some of these
functions include a pairwise plot matrix, a two group pairwise plot
matrix, a parallel coordinates plot, a survival plot, and several
functions to plot networks.
sjPlot:Data
Visualization for Statistics in Social Science
ggstatsplot is
an extension of ggplot2 package for creating graphics with details from
statistical tests included in the plots themselves and targeted
primarily at behavioral sciences community to provide a one-line code to
produce information-rich plots.
ggside
allows the user to add graphical information about one of the main
panel’s axis. This is particularly useful for metadata for discrete
axis, or summary graphics on a continuous axis such as a boxplot or a
density distribution. These vignette
and article
provide useful introduction.
Patchwork is
a package designed to make plot composition in R extremely simple and
powerful. It is mainly intended for users of ggplot2 and goes to great
lengths to make sure ggplots are properly aligned no matter the
complexity of your composition. The Getting
Started page explains the main features of the package.
gganimate extends the
grammar of graphics as implemented by ggplot2 to include the description
of animation. It does this by providing a range of new grammar classes
that can be added to the plot object in order to customise how it should
change with time.
ggdist is
an R package that provides a flexible set of ggplot2 geoms and stats
designed especially for visualizing distributions and uncertainty. It is
designed for both frequentist
and [https://mjskay.github.io/ggdist/articles/freq-uncertainty-vis.html]
visualization, taking the view that uncertainty visualization can be
unified through the perspective of distribution visualization: for
frequentist models, one visualizes confidence distributions or bootstrap
distributions (see vignette(“freq-uncertainty-vis”)); for Bayesian
models, one visualizes probability distributions (see the tidybayes
package, which builds on top of ggdist).
Interactive Data
Visualisation with R
plotly R
plotly:
Create Interactive Web Graphics via ‘plotly.js’
corrplot.
A graphical display of a correlation matrix or general matrix. It also
contains some algorithms to do matrix reordering. In addition, corrplot
is good at details, including choosing color, text labels, color labels,
layout, etc.
vcd,
Visualization techniques, data sets, summary and inference procedures
aimed particularly at categorical data. Special emphasis is given to
highly extensible grid graphics.
tmap
offers a flexible, layer-based, and easy to use approach to create
thematic maps, such as choropleths and bubble maps.
Shiny:
Web-based Visual Analytics Development tool in R
Getting Started
Hadley Wickham (2020) Mastering Shiny. Everything you
need to know about Shiny can be found here. It is not an easy to read
book but worth investing time and effort to read.