n_miss_row

Substitute for <code>rowSums(is.na(data))</code>, but it also checks if input is NULL or
is a dataframe

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

n_miss_row function

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

Arguments

Return a vector of the number of missing values in each row — n_miss_row

<dl>

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

</dl>

n_miss_row: Return a vector of the number of missing values in each row

Description

Usage

Value

Arguments

See Also

Examples