example_na

A df with different types of missingness

datasets

A system built on 'tidymodels' for generating synthetic tabular
data. We provide tools for ordering a sequential synthesis, feature and
target engineering, sampling, hyperparameter tuning, enforcing constraints,
and adding extra noise during a synthesis.

Aaron Williams

tidysynthesis

A Common API for Synthesizing Data

Aaron R. Williams

Kyle Ueyama

Gabe Morrison

Jeremy Seeman

Elyse McFalls

Claire Morton

Livia Mucciolo

Madeline Pickens

Noah Zwiefel

The Urban Institute 

example_na function

A tibble with 200 observations
and 6 variables:<dl>
<dt>age</dt>
<dd>Age of respondent</dd><dt>sex</dt>
<dd>Sex of respondent with missingness at random</dd><dt>labor_force</dt>
<dd>Labor force status of respondent with structural missingness</dd><dt>hours</dt>
<dd>Hours work of respondent with missingness at random</dd><dt>wages</dt>
<dd>Wages earned with structural missingness</dd>
</dl>

Format

A df with different types of missingness — example_na

A tibble with 200 observations
and 6 variables:<dl>
<dt>age</dt>
<dd>Age of respondent</dd>

<dt>sex</dt>
<dd>Sex of respondent with missingness at random</dd>

<dt>labor_force</dt>
<dd>Labor force status of respondent with structural missingness</dd>

<dt>hours</dt>
<dd>Hours work of respondent with missingness at random</dd>

<dt>wages</dt>
<dd>Wages earned with structural missingness</dd>


</dl>

A df with different types of missingness

example_na: A df with different types of missingness

Description

Usage

Arguments

Format