Seven-Day Ridership Trend

Problem

Table: `Tap`

```text
+------------+---------+
| Column | Type |
+------------+---------+
| rider_id | int |
| rider_name | varchar |
| tapped_on | date |
| fare_cents | int |
+------------+---------+
(rider_id, tapped_on) is the primary key. Each row is one fare a rider paid
when tapping into a transit station on a given day. There is at least one tap
every day.
```

You run a transit network and want a smoothed view of revenue. For every day that has **six full days before it** in the data, compute:

- `day_total` — the total fares collected over the seven-day window ending on that day (the day itself plus the six prior days),
- `window_average` — `day_total` divided by 7, rounded to two decimal places.

Return `tapped_on`, `day_total`, and `window_average`, ordered by `tapped_on`. The earliest six days of the dataset never have a full window and are omitted.

**Example**

```text
For seven consecutive days with daily totals 100, 100, 100, 100, 100, 100, 100
the first qualifying day is the seventh, with day_total = 700 and
window_average = 100.00.
```

Tables

Example rows — the live problem includes the full dataset.

Expected output

Your answer should return 2 rows with the columns tapped_on, day_total, window_average.

Starter code (SQL)

SELECT *
FROM Tap;

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