Visualization and Analytics for the Cryptoverse

Providing data to quickly visualize and analyze data from several cryptocurrencies.

RNAseq Visualization Automation

Automate downstream visualization & pathway analysis in RNAseq analysis. 'RVA' is a collection of functions that efficiently visualize RNAseq differential expression analysis result from summary statistics tables. It also utilize the Fisher's exact test to evaluate gene set or pathway enrichment in a convenient and efficient manner.

Visualize Probability Distributions

Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions.

Rt Visualization in D3

Create interactive visualisations of Rt estimates using 'D3.js' (Gibbs et al. (2020) ). Developed primarily targeting Rt estimates generated by the 'EpiNow2' package, 'RtD3' aims to make simple, beautiful visualisations that help researchers explore their results and share them with others.

Interface to 'D3' Visualizations

Suite of tools for using 'D3', a library for producing dynamic, interactive data visualizations. Supports translating objects into 'D3' friendly data structures, rendering 'D3' scripts, publishing 'D3' visualizations, incorporating 'D3' in R Markdown, creating interactive 'D3' applications with Shiny, and distributing 'D3' based 'htmlwidgets' in R packages.

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 .

Visualizations of Distributions and Uncertainty

Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) < https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) , quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) , complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) , and fit curves with multiple uncertainty ribbons.

Visualizes a Matrix as Heatmap

Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.

Preliminary Visualisation of Data

Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.

Multi-Collinearity Visualization

Visualize the relationship between linear regression variables and causes of multi-collinearity. Implements the method in Lin et. al. (2020) .