No packages match

loon - Interactive Statistical Data Visualization

An extendable toolkit for interactive data visualization and exploration.

Last updated

data-analysisdata-sciencedata-visualizationexploratory-analysisexploratory-data-analysishigh-dimensional-datainteractive-graphicsinteractive-visualizationsloonpythonstatistical-analysisstatistical-graphicsstatisticstcl-extensiontk

9.42 score 49 stars 4 dependents 100 scripts 329 downloads

zenplots - Zigzag Expanded Navigation Plots

Graphical tools for visualizing high-dimensional data along a path of alternating one- and two-dimensional plots. Includes optional interactive graphics via 'loon' (which uses 'tcltk' from base R). Support is provided for constructing graph structures and, when available, plotting them with Bioconductor packages (e.g., 'graph', 'Rgraphviz'); these are optional and examples/vignettes are skipped if they are not installed. For algorithms and further details, see <doi:10.18637/jss.v095.i04>.

Last updated

dimensional-datadimensional-plotsgraphical-systemspairszigzag

5.97 score 4 stars 1 dependents 13 scripts 618 downloads

eikosograms - Visualizing Probabilities, Frequencies, and Conditional Independence for Categorical Variates

An eikosogram (ancient Greek for probability picture) divides the unit square into rectangular regions whose areas, sides, and widths represent various probabilities associated with the values of one or more categorical variates. Rectangle areas are joint probabilities, widths are always marginal (though possibly joint margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles are always conditional probabilities. Eikosograms embed the rules of probability and are useful for introducing elementary probability theory, including axioms, marginal, conditional, and joint probabilities, and their relationships (including Bayes' theorem as a completely trivial consequence). They provide advantages over Venn diagrams for this purpose, particularly in distinguishing probabilistic independence, mutually exclusive events, coincident events, and associations. They also are useful for identifying and understanding conditional independence structure. Eikosograms can be thought of as mosaic plots when only two categorical variates are involved; the layout is quite different when there are more than two variates. Only one categorical variate, designated the "response", presents on the vertical axis and all others, designated the "conditioning" variates, appear on the horizontal. In this way, conditional probability appears only as height and marginal probabilities as widths. The eikosogram is ideal for response models (e.g. logistic models) but equally useful when no variate is distinguished as the response. In such cases, each variate can appear in turn as the response, which is handy for assessing conditional independence in discrete graphical models (i.e. "Bayesian networks" or "BayesNets"). The eikosogram and its value over Venn diagrams in teaching probability is described in W.H. Cherry and R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>, its value in exploring conditional independence structure and relation to graphical and log-linear models is described in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>, and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/examples/paper.pdf>.

Last updated

5.28 score 4 stars 16 scripts 569 downloads

qqtest - 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.

Last updated

4.82 score 1 stars 32 scripts 4.1k downloads

loon.data - Data Used to Illustrate 'Loon' Functionality

Data used as examples in the 'loon' package.

Last updated

loon

3.41 score 1 stars 1 dependents 17 scripts 612 downloads

diveR - Easily Install and Load Interactive Data Visualization Tools

A suite of 'loon' related packages providing data analytic tools for Direct Interactive Visual Exploration in R ('diveR'). These tools work with and complement those of the 'tidyverse' suite, extending the grammar of 'ggplot2' to become a grammar of interactive graphics. The suite provides many visual tools designed for moderately (100s of variables) high dimensional data analysis, through 'zenplots' and novel tools in 'loon', and extends the 'ggplot2' grammar to provide parallel coordinates, Andrews plots, and arbitrary glyphs through 'ggmulti'. The 'diveR' package gathers together and installs all these related packages in a single step.

Last updated

3.18 score 3 stars 9 scripts 249 downloads