n_miss

Substitute for <code>sum(is.na(data))</code>

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

n_miss function

<dl><dt>x</dt>
<dd>a vector</dd></dl>

Arguments

Return the number of missing values — n_miss

<dl>

<dt>x</dt>
<dd>a vector</dd>

</dl>

n_miss: Return the number of missing values

Description

Usage

Value

Arguments

Examples