parcats v0.0.1

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Interactive Parallel Categories Diagrams for 'easyalluvial'

Complex graphical representations of data are best explored using interactive elements. 'parcats' adds interactive graphing capabilities to the 'easyalluvial' package. The 'plotly.js' parallel categories diagrams offer a good framework for creating interactive flow graphs that allow manual drag and drop sorting of dimensions and categories, highlighting single flows and displaying mouse over information. The 'plotly.js' dependency is quite heavy and therefore is outsourced into a separate package.

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Create ‘plotly.js’ Parallel Categories Diagrams Using this Htmlwidget and ‘easyalluvial’

Complex graphical representations of data are best explored using interactive elements. ‘parcats’ adds interactive graphing capabilities to the ‘easyalluvial’ package. The ‘plotly.js’ parallel categories diagrams offer a good framework for creating interactive flow graphs that allow manual drag and drop sorting of dimensions and categories, highlighting single flows and displaying mouse over information. The ‘plotly.js’ dependency is quite heavy and therefore is outsourced into a separate package.

Installation

Development Version


# install.packages("devtools")
devtools::install_github("erblast/parcats")

easyalluvial

parcats requires an alluvial plot created with easyalluvial to create an interactive parrallel categories diagram.

Examples

suppressPackageStartupMessages( require(tidyverse) )
suppressPackageStartupMessages( require(easyalluvial) )
suppressPackageStartupMessages( require(parcats) )

Live Widget

The Htmlwidgets cannot be embedded in the README.md file. Check out the Live Widget here.

Parcats from alluvial from data in wide format

p = alluvial_wide(mtcars2, max_variables = 5)

parcats(p, marginal_histograms = TRUE, data_input = mtcars2)

Parcats from model response alluvial

Machine Learning models operate in a multidimensional space and their response is hard to visualise. Model response and partial dependency plots attempt to visualise ML models in a two dimensional space. Using alluvial plots or parrallel categories diagrams we can increase the number of dimensions.

Here we see the response of a random forest model if we vary the three variables with the highest importance while keeping all other features at their median/mode value.

df = select(mtcars2, -ids )
m = randomForest::randomForest( disp ~ ., df)
imp = m$importance
dspace = get_data_space(df, imp, degree = 3)
pred = predict(m, newdata = dspace)
p = alluvial_model_response(pred, dspace, imp, degree = 3)

parcats(p, marginal_histograms = TRUE, imp = TRUE, data_input = df)

Functions in parcats

Name Description
parcats create plotly parallel categories diagram from alluvial plot
parcats-shiny Shiny bindings for parcats
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Details

URL https://erblast.github.io/parcats/
License MIT + file LICENSE
Encoding UTF-8
LazyData true
RoxygenNote 7.0.0
Language en-US
NeedsCompilation no
Packaged 2019-11-29 09:19:13 UTC; koneswab
Repository CRAN
Date/Publication 2019-12-02 16:10:03 UTC

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