Generate Alluvial Plots with a Single Line of Code

Alluvial plots are similar to sankey diagrams and visualise categorical data over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in Large Networks. PLoS ONE 5(1): e8694. 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 options 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.

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

Installation

install.packages('easyalluvial')

Development Version

devtools::install_github("erblast/easyalluvial")

Tutorials

In order to learn about all the features an how they can be useful check out the following tutorials:

• Data exploration with alluvial plots

Examples

Alluvial from data in wide format

Prepare sample data

 
suppressPackageStartupMessages( require(tidyverse) )
suppressPackageStartupMessages( require(easyalluvial) )
 
data = as_tibble(mtcars)
categoricals = c('cyl', 'vs', 'am', 'gear', 'carb')
numericals = c('mpg', 'cyl', 'disp', 'hp', 'drat', 'wt', 'qsec')
 
data = data %>%
  mutate_at( vars(categoricals), as.factor )

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 = data
                , max_variables = 5
                , fill_by = 'first_variable' )

Alluvial from data in long format

Sample Data

 
knitr::kable( head(quarterly_flights) )

Plot

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