n_miss_row

data

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 (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_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>

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

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

Description

Usage

Value

Arguments

See Also

Examples