set-prop-n-miss

set_prop_miss

set_n_miss

Set a proportion or number of missing 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

set-prop-n-miss function

<dl><dt>x</dt>
<dd>vector of values to set missing</dd>
<dt>prop</dt>
<dd>proportion of values between 0 and 1 to set as missing</dd>
<dt>n</dt>
<dd>number of values to set missing</dd></dl>

Arguments

Set a proportion or number of missing values — set-prop-n-miss

<dl>

<dt>x</dt>
<dd>vector of values to set missing</dd>


<dt>prop</dt>
<dd>proportion of values between 0 and 1 to set as missing</dd>


<dt>n</dt>
<dd>number of values to set missing</dd>

</dl>

Set a proportion or number of missing values

set-prop-n-miss: Set a proportion or number of missing values

Description

Usage

Value

Arguments

Examples