s_data

A synthetic dataset generated for illustrative purposes.

datasets

Data practitioners regularly use the 'R' and 'Python' programming languages to
prepare data for analyses. Thus, they encode important data preprocessing decisions in
'R' and 'Python' code. The 'smallsets' package subsequently decodes these decisions into
a Smallset Timeline, a static, compact visualisation of data preprocessing decisions
(Lucchesi et al. (2022) <doi:10.1145/3531146.3533175>). The visualisation consists of
small data snapshots of different preprocessing steps. The 'smallsets' package builds this
visualisation from a user's dataset and preprocessing code located in an 'R', 'R Markdown',
'Python', or 'Jupyter Notebook' file. Users simply add structured comments with snapshot
instructions to the preprocessing code. One optional feature in 'smallsets' requires
installation of the 'Gurobi' optimisation software and 'gurobi' 'R' package, available
from <https://www.gurobi.com>. More information regarding the optional feature and
'gurobi' installation can be found in the 'smallsets' vignette.

Lydia R. Lucchesi

smallsets

Visual Documentation for Data Preprocessing

Petra M. Kuhnert

Jenny L. Davis

Lexing Xie

s_data function

A data frame with 100 rows and 8 columns:<dl>
 <dt>C1</dt>
<dd>discrete</dd> <dt>C2</dt>
<dd>binary</dd> <dt>C3</dt>
<dd>discrete</dd> <dt>C4</dt>
<dd>discrete</dd> <dt>C5</dt>
<dd>continuous</dd> <dt>C6</dt>
<dd>continuous</dd> <dt>C7</dt>
<dd>continuous</dd> <dt>C8</dt>
<dd>continuous</dd>
</dl>

Format

Synthetic dataset — s_data

A data frame with 100 rows and 8 columns:<dl>
 <dt>C1</dt>
<dd>discrete</dd>

 <dt>C2</dt>
<dd>binary</dd>

 <dt>C3</dt>
<dd>discrete</dd>

 <dt>C4</dt>
<dd>discrete</dd>

 <dt>C5</dt>
<dd>continuous</dd>

 <dt>C6</dt>
<dd>continuous</dd>

 <dt>C7</dt>
<dd>continuous</dd>

 <dt>C8</dt>
<dd>continuous</dd>


</dl>

s_data: Synthetic dataset

Description

Usage

Arguments

Format

Examples