impute_below

extra arguments to pass - see <code><a rd-options="=shadow_shift" href="/link/shadow_shift()?package=naniar&version=0.6.1&to=%3Dshadow_shift" data-mini-rdoc="=shadow_shift::shadow_shift()">shadow_shift()</a></code> for discussion on this.

It can be useful in exploratory graphics to impute data outside the range of
the data. <code>impute_below</code> imputes all variables with missings to have values
10 percent below the range for numeric values, and for character or factor
values adds a new string or label. It is powered by <code>shadow_shift</code>, so please
see the documentation for <code>shadow_shift()</code> to full details on the different
implementations.

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 (2018) <arXiv:1809.02264>.

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

impute_below function

<dl><dt>...</dt>
<dd>extra arguments to pass - see <code>shadow_shift()</code> for discussion on this.</dd></dl>

Arguments

Impute data with values shifted 10 percent below range. — impute_below

<dl>

<dt>...</dt>
<dd>extra arguments to pass - see <code>shadow_shift()</code> for discussion on this.</dd>

</dl>

impute_below: Impute data with values shifted 10 percent below range.

Description

Usage

Arguments