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.

Hacks for 'ggplot2'

A 'ggplot2' extension that does a variety of little helpful things. The package extends 'ggplot2' facets through customisation, by setting individual scales per panel, resizing panels and providing nested facets. Also allows multiple colour and fill scales per plot. Also hosts a smaller collection of stats, geoms and axis guides.

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.

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'.

Visualization of a Correlation Matrix using 'ggplot2'

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.

Manage Branding and Accessibility of R Projects

A tool for building projects that are visually consistent, accessible, and easy to maintain. It provides functions for managing branding assets, applying organization-wide themes using 'brand.yml', and setting up new projects with accessibility features and correct branding. It supports 'quarto', 'shiny', and 'rmarkdown' projects, and integrates with 'ggplot2'. The accessibility features are based on the Web Content Accessibility Guidelines < https://www.w3.org/WAI/WCAG22/quickref/?versions=2.1> and Accessible Rich Internet Applications (ARIA) specifications < https://www.w3.org/WAI/ARIA/apg/>. The branding framework implements the 'brand.yml' specification < https://posit-dev.github.io/brand-yml/>.

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?

A Grammar of Data Manipulation

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.

Add Marginal Histograms to 'ggplot2', and More 'ggplot2' Enhancements

Collection of functions and layers to enhance 'ggplot2'. The flagship function is 'ggMarginal()', which can be used to add marginal histograms/boxplots/density plots to 'ggplot2' scatterplots.