shadow_long

shadow_data

bare name of variables that you want to focus on

logical - do you want to filter down to main variables?

only_main_vars

Once data is in <code>nabular</code> form, where the shadow is bound to the data, it
can be useful to reshape it into a long format with the columns

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

shadow_long function

<dl><dt>shadow_data</dt>
<dd>a data.frame</dd>
<dt>...</dt>
<dd>bare name of variables that you want to focus on</dd>
<dt>only_main_vars</dt>
<dd>logical - do you want to filter down to main variables?</dd></dl>

Arguments

Reshape shadow data into a long format — shadow_long

<dl>

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


<dt>...</dt>
<dd>bare name of variables that you want to focus on</dd>


<dt>only_main_vars</dt>
<dd>logical - do you want to filter down to main variables?</dd>

</dl>

shadow_long: Reshape shadow data into a long format

Description

Usage

Value

Arguments

Examples