# parcats 0.0.1 released

**R on datistics**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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`parcats`

was released on CRAN. It is an htmlwidget providing bindings to the `plotly.js`

parcats trace, which is not supported by the `plotly`

R package. Also adds marginal histograms for numerical variables.

# What it can do

I wanted to add interactivity to `easyalluvial`

plots for a while now and found that the parcats trace of `plotly.js`

would be perfect because brushing with the mouse highlights the entire flow and not just everything flowing in and out of a specific node as in most `D3`

Sankey chart implementations. Unfortunately the parcats trace was not available in the `plotly`

R package so I decided to build a new html widget to create R bindings for specifically this trace.

- converts any
`easyalluvial`

plot to an interactive parallel categories diagram - interactive marginal histograms
- multidimensional partial dependency and model response plots

# easyalluvial

`parcats`

requires an alluvial plot created with `easyalluvial`

to create an interactive parrallel categories diagram.

# Demo

## Examples

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

### 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)

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