[This article was first published on R on datistics, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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.

demogif

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.

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$importancedspace = 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)

var vglnk = { key: '949efb41171ac6ec1bf7f206d57e90b8' }; (function(d, t) {var s = d.createElement(t); s.type = 'text/javascript'; s.async = true;s.src = '//cdn.viglink.com/api/vglnk.js';var r = d.getElementsByTagName(t)[0]; r.parentNode.insertBefore(s, r); }(document, 'script'));

To leave a comment for the author, please follow the link and comment on their blog: R on datistics.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

na.omit	idvs	outlier_tment_style	model_type	feffect	cluster	est	lb	ub
yes	gdp_only	win	level-level	none	none	0.362	0.3466	0.3783
no	gdp_only	win	level-level	none	none	0.386	0.3737	0.3983
yes	gdp_school	win	level-level	none	none	0.131	0.1159	0.1452
no	gdp_school	win	level-level	none	none	0.075	0.0644	0.0855
yes	gdp_ue	win	level-level	none	none	0.370	0.3543	0.3860
no	gdp_ue	win	level-level	none	none	0.325	0.3116	0.3378

Example Code:

What it can do

easyalluvial

Demo

Examples

Parcats from alluvial from data in wide format

Parcats from model response alluvial

SatRdays

useR / R-Ladies Groups

About the JavaScript code

Instructions

R solution

Part one

Part two

JS solution

Part one & Two

Creating Email

Sending Custom Emails with SMTP

Sending Custom Emails with RStudio Connect

Lines 1-4

Line 5

Lines 6 – 7

Line 8

Lines 9 – end

The big reveal

Instructions

R solution

Part one

Part two

JS solution

Important dates

Installation

Searching for Athletes

Athlete Details

Future

Intro + hackathon description

How we organised our work

Our delivered solution

Results – lessons learned

Closing thoughts

Follow Appsilon Data Science on Social Media

Changes in RcppArmadillo version 0.9.800.3.0 (2019-12-04)

Appendix

Lines `1-4`