retailers

Anonymized and distorted data on revenue and cost structure for 60 retailers. 
Currency is in thousands (of dollars or euros or guilders, ...)<ul>
<li> size: Size class
 </li>
<li> incl.prob: Probability of inclusion in the sample
 </li>
<li> staff: Number of staff
 </li>
<li> turnover: Amount of turnover
 </li>
<li> other.rev: Amount of other revenue
 </li>
<li> total.rev: Total revenue
 </li>
<li> staff.costs: Number of staff employed
 </li>
<li> total.costs: Total costs made
 </li>
<li> profit: Amount of profit
 </li>
<li> vat: Turnover reported for Value Added Tax
</li>
</ul>

Declare data validation rules and data quality indicators; confront
data with them and analyze or visualize the results. The package supports
rules that are per-field, in-record, cross-record or cross-dataset. Rules
can be automatically analyzed for rule type and connectivity.

Mark der Loo

validate

Data Validation Infrastructure

retailers function

Format

Anonymized and distorted data on revenue and cost structure for 60 retailers. 
Currency is in thousands (of dollars or euros or guilders, ...)

<ul>
<li> size: Size class
 </li>
<li> incl.prob: Probability of inclusion in the sample
 </li>
<li> staff: Number of staff
 </li>
<li> turnover: Amount of turnover
 </li>
<li> other.rev: Amount of other revenue
 </li>
<li> total.rev: Total revenue
 </li>
<li> staff.costs: Number of staff employed
 </li>
<li> total.costs: Total costs made
 </li>
<li> profit: Amount of profit
 </li>
<li> vat: Turnover reported for Value Added Tax
</li>
</ul>

retailers: data on retailers

Description

Arguments

Format