add_shadow_shift

Shadow shift missing values using only the selected variables in a dataset,
by specifying variable names or use dplyr <code>vars</code> and dplyr verbs
<code>starts_with</code>, <code>contains</code>, <code>ends_with</code>, etc.

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

add_shadow_shift function

<dl><dt>data</dt>
<dd>data.frame</dd>
<dt>...</dt>
<dd>One or more unquoted variable names separated by commas. These also
respect the dplyr verbs <code>starts_with</code>, <code>contains</code>, <code>ends_with</code>, etc.</dd>
<dt>suffix</dt>
<dd>suffix to add to variable, defaults to "shift"</dd></dl>

Arguments

Add a shadow shifted column to a dataset — add_shadow_shift

<dl>

<dt>data</dt>
<dd>data.frame</dd>


<dt>...</dt>
<dd>One or more unquoted variable names separated by commas. These also
respect the dplyr verbs <code>starts_with</code>, <code>contains</code>, <code>ends_with</code>, etc.</dd>


<dt>suffix</dt>
<dd>suffix to add to variable, defaults to "shift"</dd>

</dl>

add_shadow_shift: Add a shadow shifted column to a dataset

Description

Usage

Value

Arguments

See Also

Examples