Automatic Plotting of Many Graphs

Visual exploration and presentation of networks should not be difficult. This package includes functions for plotting networks and network-related metrics with sensible and pretty defaults. It includes 'ggplot2'-based plot methods for many popular network package classes. It also includes some novel layout algorithms, and options for straightforward, consistent themes.

The Composer of Plots

The 'ggplot2' package provides a strong API for sequentially building up a plot, but does not concern itself with composition of multiple plots. 'patchwork' is a package that expands the API to allow for arbitrarily complex composition of plots by, among others, providing mathematical operators for combining multiple plots. Other packages that try to address this need (but with a different approach) are 'gridExtra' and 'cowplot'.

Command Line Interface Plotting

The 'plotcli' package provides terminal-based plotting in R. It supports colored scatter plots, line plots, bar plots, boxplots, histograms, density plots, and more. The 'ggplotcli()' function is a universal converter that renders any 'ggplot2' plot in the terminal using Unicode Braille characters or ASCII. Features include support for 15+ geom types, faceting (facet_wrap/facet_grid), automatic theme detection, legends, optimized color mapping, and multiple canvas types.

'ggplot2' Pattern Geoms

Provides 'ggplot2' geoms filled with various patterns. Includes a patterned version of every 'ggplot2' geom that has a region that can be filled with a pattern. Provides a suite of 'ggplot2' aesthetics and scales for controlling pattern appearances. Supports over a dozen builtin patterns (every pattern implemented by 'gridpattern') as well as allowing custom user-defined patterns.

Multiple Fill and Colour Scales in 'ggplot2'

Use multiple fill and colour scales in 'ggplot2'.

Tidy Tools for Paleoenvironmental Archives

Provides a set of functions with a common framework for age-depth model management, stratigraphic visualization, and common statistical transformations. The focus of the package is stratigraphic visualization, for which 'ggplot2' components are provided to reproduce the scales, geometries, facets, and theme elements commonly used in publication-quality stratigraphic diagrams. Helpers are also provided to reproduce the exploratory statistical summaries that are frequently included on stratigraphic diagrams. See Dunnington et al. (2021) .

Improved Text Rendering Support for 'ggplot2'

A 'ggplot2' extension that enables the rendering of complex formatted plot labels (titles, subtitles, facet labels, axis labels, etc.). Text boxes with automatic word wrap are also supported.

KMunicate-Style Kaplan–Meier Plots

Produce Kaplan–Meier plots in the style recommended following the KMunicate study by Morris et al. (2019) . The KMunicate style consists of Kaplan-Meier curves with confidence intervals to quantify uncertainty and an extended risk table (per treatment arm) depicting the number of study subjects at risk, events, and censored observations over time. The resulting plots are built using 'ggplot2' and can be further customised to a certain extent, including themes, fonts, and colour scales.

R Templates for Reproducible Data Analyses

A collection of R Markdown templates for nicely structured, reproducible data analyses in R. The templates have embedded examples on how to write citations, footnotes, equations and use colored message/info boxes, how to cross-reference different parts/sections in the report, provide a nice table of contents (toc) with a References section and proper R session information as well as examples using DT tables and ggplot2 graphs. The bookdown Lite template theme supports code folding.

An Implementation of Grammar of Graphics for Graphs and Networks

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.