easyalluvial

Alluvial plots are similar to sankey diagrams and visualise categorical data over multiple dimensions as flows. Rosval et. al. 2010 Their graphical grammar however is a bit more complex then that of a regular x/y plots. The ggalluvial package made a great job of translating that grammar into ggplot2 syntax and gives you many option to tweak the appearance of an alluvial plot, however there still remains a multi-layered complexity that makes it difficult to use ‘ggalluvial’ for explorative data analysis. ‘easyalluvial’ provides a simple interface to this package that allows you to produce a decent alluvial plot from any dataframe in either long or wide format from a single line of code while also handling continuous data. It is meant to allow a quick visualisation of entire dataframes with a focus on different colouring options that can make alluvial plots a great tool for data exploration.

Features

• plot alluvial graph with a single line of code of a given dataframe
• support for wide and long data format (wiki, wide vs. long/narrow data)
• automatically transforms numerical to categorical data
• helper functions for variable selection
• convenient parameters for coloring and ordering
• marginal histograms
• model agnostic partial dependence and model response alluvial plots with 4 dimensions
• interactive plots with easyalluvial and parcats

Installation

CRAN

install.packages('easyalluvial')

Development Version

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

Documentation

• pkgdown website
  • Data Exploration with Alluvial Plots
  • Visualising Model Response
  • Interactive Plots with parcats

Examples

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

Alluvial from data in wide format

Sample Data

knitr::kable( head(mtcars2) )

mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb	ids
21.0	6	160	110	3.90	2.620	16.46	V	manual	4	4	Mazda RX4
21.0	6	160	110	3.90	2.875	17.02	V	manual	4	4	Mazda RX4 Wag
22.8	4	108	93	3.85	2.320	18.61	S	manual	4	1	Datsun 710
21.4	6	258	110	3.08	3.215	19.44	S	automatic	3	1	Hornet 4 Drive
18.7	8	360	175	3.15	3.440	17.02	V	automatic	3	2	Hornet Sportabout
18.1	6	225	105	2.76	3.460	20.22	S	automatic	3	1	Valiant

Plot

Continuous Variables will be automatically binned as follows.

• High, High (HH)
• Medium, High (MH)
• Medium (M)
• Medium, Low (ML)
• Low, Low (LL)

alluvial_wide( data = mtcars2
                , max_variables = 5
                , fill_by = 'first_variable' )

Alluvial from data in long format

Sample Data

knitr::kable( head(quarterly_flights) )

tailnum	carrier	origin	dest	qu	mean_arr_delay
N0EGMQ LGA BNA MQ	MQ	LGA	BNA	Q1	on_time
N0EGMQ LGA BNA MQ	MQ	LGA	BNA	Q2	on_time
N0EGMQ LGA BNA MQ	MQ	LGA	BNA	Q3	on_time
N0EGMQ LGA BNA MQ	MQ	LGA	BNA	Q4	on_time
N11150 EWR MCI EV	EV	EWR	MCI	Q1	late
N11150 EWR MCI EV	EV	EWR	MCI	Q2	late

Plot

alluvial_long( quarterly_flights
               , key = qu
               , value = mean_arr_delay
               , id = tailnum
               , fill = carrier )

Marginal Histograms

alluvial_wide( data = mtcars2
                , max_variables = 5
                , fill_by = 'first_variable' ) %>%
  add_marginal_histograms(mtcars2)

Interactive Graphs

suppressPackageStartupMessages( require(parcats) )

p = alluvial_wide(mtcars2, max_variables = 5)

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

demo

• Live Widget

Partial Dependence Alluvial Plots

Alluvial plots are capable of displaying higher dimensional data on a plane, thus lend themselves to plot the response of a statistical model to changes in the input data across multiple dimensions. The practical limit here is 4 dimensions while conventional partial dependence plots are limited to 2 dimensions.

Briefly the 4 variables with the highest feature importance for a given model are selected and 5 values spread over the variable range are selected for each. Then a grid of all possible combinations is created. All none-plotted variables are set to the values found in the first row of the training data set. Using this artificial data space model predictions are being generated. This process is then repeated for each row in the training data set and the overall model response is averaged in the end. Each of the possible combinations is plotted as a flow which is coloured by the bin corresponding to the average model response generated by that particular combination.

• more on partial dependence plots (ebook)
• Tutorial

easyalluvial contains wrappers for parsnip and caret models. Custom Wrappers for other models can easily be created.

df = select(mtcars2, -ids)

m = parsnip::rand_forest(mode = "regression") %>%
  parsnip::set_engine("randomForest") %>%
  parsnip::fit(disp ~ ., df)

p = alluvial_model_response_parsnip(m, df, degree = 4, method = "pdp")
#> Getting partial dependence plot preditions. This can take a while. See easyalluvial::get_pdp_predictions() `Details` on how to use multiprocessing

p_grid = add_marginal_histograms(p, df, plot = F) %>%
  add_imp_plot(p, df)

Interactive Partial Dependence Plot

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

- Live Widget

ClinicoPath {jamovi} Module

ClinicoPath jamovi Module (thanks to Serdar Balci) adds easyalluvial plots to jamovia spreadsheet interface for doing statistics with R.

Similar Packages

• ggalluvial
• alluvial
• networkD3
• ggbump