BC Basket

Data Collection

Prices are collected once per week from selected grocery stores. The goal is to record prices as they appear in-store so the data reflects what consumers actually see on the shelf.

For each item, we aim to keep product selection consistent by matching the same brand, size, and type across all stores whenever possible. If an exact match is not available, the closest equivalent product is selected and documented.

Loyalty-card-only prices are not included in the official data because they are not available to all customers. However, temporary sale prices may be recorded separately for context.

Data Consistency

To ensure fair comparisons between stores, all prices are recorded as unit prices when necessary, for example price per ounce or per item. This allows items of slightly different sizes to still be compared accurately.

Any substitutions or inconsistencies are noted to maintain transparency in the dataset.

The final tracker table is produced from a reverse-pivot-style spreadsheet process: raw store-level prices are organized by item, averaged across stores, and displayed as one row per item with month columns across the page.

Average Calculation

Weekly prices are recorded for each item, and a monthly average is calculated using those values.

The monthly average is computed by adding all weekly prices for an item and dividing by the number of weeks recorded.

If a price is missing for a given week, only the available weeks are used in the calculation.

Output and Continuation

The main output of this project is an average price per item across the selected stores. These averages are used to compare grocery price trends over time. Weeks 1–2 were partially recorded because one store had not been added yet. Weeks 3–14 contain the complete five-store dataset, and missing values are shown transparently rather than estimated.

This project is designed to be continued by future groups. The standardized data collection process allows new data to be added consistently, making it possible to track long-term price trends.