nabular

Binding a shadow matrix to a regular dataframe converts it into nabular data,
which makes it easier to visualise and work with missing data.

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

nabular function

<dl><dt>data</dt>
<dd>a dataframe</dd>
<dt>only_miss</dt>
<dd>logical - if FALSE (default) it will bind a dataframe with
all of the variables duplicated with their shadow. Setting this to TRUE
will bind variables only those variables that contain missing values.
See the examples for more details.</dd>
<dt>...</dt>
<dd>extra options to pass to <code>recode_shadow()</code> - a work in progress.</dd></dl>

Arguments

Convert data into nabular form by binding shade to it — nabular

<dl>

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


<dt>only_miss</dt>
<dd>logical - if FALSE (default) it will bind a dataframe with
all of the variables duplicated with their shadow. Setting this to TRUE
will bind variables only those variables that contain missing values.
See the examples for more details.</dd>


<dt>...</dt>
<dd>extra options to pass to <code>recode_shadow()</code> - a work in progress.</dd>

</dl>

nabular: Convert data into nabular form by binding shade to it

Description

Usage

Value

Arguments

See Also

Examples