Store Data from the Contoso Dataset

Source: R/data.R

This dataset contains information about stores within the Contoso dataset. It includes details about the store’s geographic location, operational status, and physical characteristics such as size and opening/closing dates. It provides insights into the store network of the company.

Usage

store

Format

A data frame with 74 rows (73 physical stores plus the online channel) and 11 columns:

store_key

double Unique identifier for each store. 999999 is the online channel sentinel.

store_code

double A code identifying the site. Not unique — a site with successive premises shares one code across several store_key values. -1 on the online row.

geo_area_key

double The geographical area, on the same numbering as customer\(geo_area_key</code>. No geo-area table ships with the package, so this resolves to nothing on its own. -1 on the online row.</p> </dd> <dt>country_code</dt> <dd> <p><code>character</code> The country code where the store is located (e.g., "US", "DE"), plus the placeholder <code>"--"</code> on the online row.</p> </dd> <dt>country_name</dt> <dd> <p><code>character</code> The full name of the country, plus the pseudo-country <code>"Online"</code>.</p> </dd> <dt>state</dt> <dd> <p><code>character</code> The state or region, always spelled out — unlike <code>customer\)state, which is often abbreviated. Literally “Online” on the online row.

open_date

Date The date when the store was opened.

close_date

Date The date when the store was closed. Missing for stores still trading.

description

character Always “Contoso Store <state>” for physical stores, and “Online store” for the online row. Carries no store-format information.

square_meters

double The physical size of the store in square meters. Missing on the online row.

status

character Exceptional status only: “Closed” or “Restructured”. Missing, never “Open”, for stores trading normally — test with is.na() rather than comparing to a string.

The online channel

store_key 999999 is the online channel, not a location. It carries sentinel values throughout: store_code and geo_area_key are -1, country_code is “–”, country_name and state are literally “Online”, and square_meters is missing. Filter it out before any geographic or floor-space aggregate. It accounts for a little over half of all sales lines.

Source

https://github.com/sql-bi/Contoso-Data-Generator-V2-Data/releases/tag/ready-to-use-data