as_shadow

Return a tibble in shadow matrix form, where the variables are the same but
have a suffix _NA attached to distinguish them.

Missing values are ubiquitous in data and need to be explored and
handled in the initial stages of analysis. 'naniar' provides data
structures and functions that facilitate the plotting of missing values and
examination of imputations. This allows missing data dependencies to be
explored with minimal deviation from the common work patterns of 'ggplot2'
and tidy data. The work is fully discussed at Tierney & Cook (2023)
<doi:10.18637/jss.v105.i07>.

Nicholas Tierney

naniar

Data Structures, Summaries, and Visualisations for Missing Data

Di Cook

Miles McBain

Colin Fay

Mitchell O'Hara-Wild

Jim Hester

Luke Smith

Andrew Heiss

as_shadow function

<dl><dt>data</dt>
<dd>dataframe</dd>
<dt>...</dt>
<dd>selected variables to use</dd></dl>

Arguments

Create shadows — as_shadow

<dl>

<dt>data</dt>
<dd>dataframe</dd>


<dt>...</dt>
<dd>selected variables to use</dd>

</dl>

as_shadow: Create shadows

Description

Usage

Value

Arguments

Details

Examples