tidy_imp

returns dataframe with exactly two columns, vars and imp and
 aggregates dummy encoded variables. Helper function called by all functions
 that take an imp parameter. Can be called manually if formula for
 aggregating dummy encoded variables must be modified.

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. <doi:10.1371/journal.pone.0008694>
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.

Bjoern Koneswarakantha

easyalluvial

Generate Alluvial Plots with a Single Line of Code

tidy_imp function

<dl><dt>imp</dt>
<dd>dataframe or matrix with feature importance information</dd>
<dt>df</dt>
<dd>dataframe, modeling training data</dd>
<dt>.f</dt>
<dd>window function, Default: max</dd>
<dt>resp_var</dt>
<dd>character, prediction variable, can usually be inferred from 
imp and df. It does not work for all models and needs to be specified in those 
cases.</dd></dl>

Arguments

tidy up dataframe containing model feature importance — tidy_imp

<dl>

<dt>imp</dt>
<dd>dataframe or matrix with feature importance information</dd>


<dt>df</dt>
<dd>dataframe, modeling training data</dd>


<dt>.f</dt>
<dd>window function, Default: max</dd>


<dt>resp_var</dt>
<dd>character, prediction variable, can usually be inferred from 
imp and df. It does not work for all models and needs to be specified in those 
cases.</dd>

</dl>

tidy_imp: tidy up dataframe containing model feature importance

Description

Usage

Value

Arguments

Examples