shadow_shift.numeric

Shift (impute) numeric values for graphical exploration

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

shadow_shift.numeric function

<dl><dt>x</dt>
<dd>a variable of interest to shift</dd>
<dt>prop_below</dt>
<dd>the degree to shift the values. default is</dd>
<dt>jitter</dt>
<dd>the amount of jitter to add. default is 0.05</dd>
<dt>seed_shift</dt>
<dd>a random seed to set, if you like</dd>
<dt>...</dt>
<dd>extra arguments to pass</dd></dl>

Arguments

Shift (impute) numeric values for graphical exploration — shadow_shift.numeric

<dl>

<dt>x</dt>
<dd>a variable of interest to shift</dd>


<dt>prop_below</dt>
<dd>the degree to shift the values. default is</dd>


<dt>jitter</dt>
<dd>the amount of jitter to add. default is 0.05</dd>


<dt>seed_shift</dt>
<dd>a random seed to set, if you like</dd>


<dt>...</dt>
<dd>extra arguments to pass</dd>

</dl>

shadow_shift.numeric: Shift (impute) numeric values for graphical exploration

Description

Usage

Arguments