Interactive Plots with parcats • easyalluvial

parcats

parcats is an html widget for easyalluvial that creates interactive alluvial plots.

Install

install.packages('parcats')

Examples

suppressPackageStartupMessages( require(dplyr) )
suppressPackageStartupMessages( require(easyalluvial) )
suppressPackageStartupMessages( require(parcats) )

Parcats from alluvial from data in wide format

p = alluvial_wide(mtcars2, max_variables = 5)

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `mpg = (function (f, na_level = "(Missing)") ...`.
## Caused by warning:
## ! `fct_explicit_na()` was deprecated in forcats 1.0.0.
## ℹ Please use `fct_na_value_to_level()` instead.
## ℹ The deprecated feature was likely used in the easyalluvial package.
##   Please report the issue to the authors.

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 parralel categories diagrams we can increase the number of dimensions.

Visualise model response with alluvial plots

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)