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) .

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.

Freshing Up your 'ggplot2' Plots

Functions for working with legends and axis lines of 'ggplot2', facets that repeat axis lines on all panels, and some 'knitr' extensions.

'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.

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.

Graphs for Correspondence Analysis

Readable, complete and pretty graphs for correspondence analysis made with 'FactoMineR'. They can be rendered as interactive 'HTML' plots, showing useful informations at mouse hover. The interest is not mainly visual but statistical: it helps the reader to keep in mind the data contained in the cross-table or Burt table while reading the correspondence analysis, thus preventing over-interpretation. Most graphs are made with 'ggplot2', which means that you can use the + syntax to manually add as many graphical pieces you want, or change theme elements. 3D graphs are made with 'plotly'.

Style Time Series Plots Like the Wall Street Journal

Easily override the default visual choices in 'ggplot2' to make your time series plots look more like the Wall Street Journal. Specific theme design choices include omitting x-axis grid lines and displaying sparse light grey y-axis grid lines. Additionally, this allows to label the y-axis scales with your units only displayed on the top-most number, while also removing the bottom most number (unless specifically overridden). The goal is visual simplicity, because who has time to waste looking at a cluttered graph?