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Display and Analyze ROC Curves
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Visualizing Association Rules and Frequent Itemsets
Extends package 'arules' with various visualization
techniques for association rules and itemsets. The package also
includes several interactive visualizations for rule exploration.
Michael Hahsler (2017)
Explore and Visualize Your Data Interactively
A 'shiny' gadget to create 'ggplot2' figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot.
Some Useful Functions for Statistics and Visualization
Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with alphabets group for multiple comparison group.
High Dimensional Data Visualization
It provides materials (i.e. 'serial axes' objects, Andrew's plot, various glyphs for scatter plot) to visualize high dimensional data.
Clustering and Visualizing Distance Matrices
Defines the classes used to explore, cluster and
visualize distance matrices, especially those arising from binary
data. See Abrams and colleagues, 2021,
Analysis and Visualization Likert Items
An approach to analyzing Likert response items, with an emphasis on visualizations. The stacked bar plot is the preferred method for presenting Likert results. Tabular results are also implemented along with density plots to assist researchers in determining whether Likert responses can be used quantitatively instead of qualitatively. See the likert(), summary.likert(), and plot.likert() functions to get started.
Visualization using Graph Traversal
Improving graphics by ameliorating order effects, using Eulerian tours
and Hamiltonian decompositions of graphs. References for the methods presented
here are C.B. Hurley and R.W. Oldford (2010)
Create Waffle Chart Visualizations
Square pie charts (a.k.a. waffle charts) can be used to communicate parts of a whole for categorical quantities. To emulate the percentage view of a pie chart, a 10x10 grid should be used with each square representing 1% of the total. Modern uses of waffle charts do not necessarily adhere to this rule and can be created with a grid of any rectangular shape. Best practices suggest keeping the number of categories small, just as should be done when creating pie charts. Tools are provided to create waffle charts as well as stitch them together, and to use glyphs for making isotype pictograms.
Analysis and Visualization of Macroevolutionary Dynamics on Phylogenetic Trees
Provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more.