add_shadow

data

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.

As an alternative to <code>bind_shadow()</code>, you can add specific individual shadow
columns to a dataset. These also respect the 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 (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

add_shadow 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></dl>

Arguments

Add a shadow column to dataframe — add_shadow

<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>

</dl>

add_shadow: Add a shadow column to dataframe

Description

Usage

Value

Arguments

See Also

Examples