Visualization of Functional Analysis Data

Implementation of multilayered visualizations for enhanced graphical representation of functional analysis data. It combines and integrates omics data derived from expression and functional annotation enrichment analyses. Its plotting functions have been developed with an hierarchical structure in mind: starting from a general overview to identify the most enriched categories (modified bar plot, bubble plot) to a more detailed one displaying different types of relevant information for the molecules in a given set of categories (circle plot, chord plot, cluster plot, Venn diagram, heatmap).

Network Dynamic Temporal Visualizations

Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.

Self Calibrating Quantile-Quantile Plots for Visual Testing

Provides the function qqtest which incorporates uncertainty in its qqplot display(s) so that the user might have a better sense of the evidence against the specified distributional hypothesis. qqtest draws a quantile quantile plot for visually assessing whether the data come from a test distribution that has been defined in one of many ways. The vertical axis plots the data quantiles, the horizontal those of a test distribution. The default behaviour generates 1000 samples from the test distribution and overlays the plot with shaded pointwise interval estimates for the ordered quantiles from the test distribution. A small number of independently generated exemplar quantile plots can also be overlaid. Both the interval estimates and the exemplars provide different comparative information to assess the evidence provided by the qqplot for or against the hypothesis that the data come from the test distribution (default is normal or gaussian). Finally, a visual test of significance (a lineup plot) can also be displayed to test the null hypothesis that the data come from the test distribution.

Visualisation of High-Throughput Behavioural (i.e. Ethomics) Data

Extension of 'ggplot2' providing layers, scales and preprocessing functions useful to represent behavioural variables that are recorded over multiple animals and days. This package is part of the 'rethomics' framework < https://rethomics.github.io/>.

Interactive Viewing of Spatial Data in R

Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.

Color-Based Plots for Multivariate Visualization

Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps. Lower-level functions map numeric values to colors, display a matrix as an array of colors, and draw color keys. Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.

Your Advanced 3D Brain Visualization

A fast, interactive cross-platform, and easy to share 'WebGL'-based 3D brain viewer that visualizes 'FreeSurfer' and/or 'AFNI/SUMA' surfaces. The viewer widget can be either standalone or embedded into 'R-shiny' applications. The standalone version only require a web browser with 'WebGL2' support (for example, 'Chrome', 'Firefox', 'Safari'), and can be inserted into any websites. The 'R-shiny' support allows the 3D viewer to be dynamically generated from reactive user inputs. Please check the publication by Wang, Magnotti, Zhang, and Beauchamp (2023, ) for electrode localization. This viewer has been fully adopted by 'RAVE' < https://openwetware.org/wiki/RAVE>, an interactive toolbox to analyze 'iEEG' data by Magnotti, Wang, and Beauchamp (2020, ). Please check 'citation("threeBrain")' for details.

Projection Pursuit Classification Tree Visualization

Tools for exploring projection pursuit classification tree using various projection pursuit indexes.

Kernel Smoothing

Kernel smoothers for univariate and multivariate data, with comprehensive visualisation and bandwidth selection capabilities, including for densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Chacon & Duong (2018) .

A Toolbox for Manipulating and Assessing Colors and Palettes

Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at < https://colorspace.R-Forge.R-project.org/> and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, ).