prop-miss-complete-var

prop_miss_var

prop_complete_var

Calculate the proportion of variables that contain a single missing or
complete values.

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

prop-miss-complete-var function

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

Arguments

Proportion of variables containing missings or complete values — prop-miss-complete-var

<dl>

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

</dl>

Proportion of variables containing missings or complete values

State of Data and AI Literacy Report 2025

prop-miss-complete-var: Proportion of variables containing missings or complete values

Description

Usage

Value

Arguments

See Also

Examples