Extended Tools for Continuous Legends, Polygon Manipulation, and Visual Display of Categorical Data

Annotate plots with legends for continuous variables and colour spectra using the base graphics plotting tools; and manipulate irregular polygons. Includes palettes for colour-blind viewers.

Analysis and Visualization of Archaeological Count Data

An easy way to examine archaeological count data. This package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). It allows to easily visualize count data and statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.

Easy Analysis and Visualization of Factorial Experiments

Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.

Create Interactive Timeline Visualizations in R

Create rich and fully interactive timeline visualizations. Timelines can be included in Shiny apps or R markdown documents. 'timevis' includes an extensive API to manipulate a timeline after creation, and supports getting data out of the visualization into R. Based on the 'vis.js' Timeline JavaScript library.

Perceptual Analysis, Visualization and Organization of Spectral Colour Data

A cohesive framework for the spectral and spatial analysis of colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013) and Maia, Gruson, Endler & White (2019) .

Visualizing Panel Data

Visualizes panel data. It has three main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. For details, see .

Extending 'dendrogram' Functionality in R

Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.

A Collection of Database, Data Structure, Visualization, and Utility Functions for R

The caroline R library contains dozens of functions useful for: database migration (dbWriteTable2), database style joins & aggregation (nerge, groupBy, & bestBy), data structure conversion (nv, tab2df), legend table making (sstable & leghead), automatic legend positioning for scatter and box plots (), plot annotation (labsegs & mvlabs), data visualization (pies, sparge, confound.grid & raPlot), character string manipulation (m & pad), file I/O (write.delim), batch scripting, data exploration, and more. The package's greatest contributions lie in the database style merge, aggregation and interface functions as well as in it's extensive use and propagation of row, column and vector names in most functions.

Create Interactive 3D Visualizations of Molecular Data

Create rich and fully interactive 3D visualizations of molecular data. Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package.

Accelerating 'ggplot2'

The aim of 'ggplot2' is to aid in visual data investigations. This focus has led to a lack of facilities for composing specialised plots. 'ggforce' aims to be a collection of mainly new stats and geoms that fills this gap. All additional functionality is aimed to come through the official extension system so using 'ggforce' should be a stable experience.