BigQuery SQL for 28-day sliding window aggregate (without writing 28 lines of SQL)

Question

I\'m trying to compute a 28 day moving sum in BigQuery using the LAG function.

The top answer to this question

Bigquery SQL for sliding window aggregate

Answer 1

Here is an alternate take that I found to be flexible and effective:

WITH users AS
 (SELECT 'Isabella' as user, 1 as spend, DATE(2020, 03, 28) as date
  UNION ALL SELECT 'Isabella', 2, DATE(2020, 03, 29)
  UNION ALL SELECT 'Daniel', 3, DATE(2020, 03, 24)
  UNION ALL SELECT 'Andrew', 4, DATE(2020, 03, 23)
  UNION ALL SELECT 'Daniel', 5, DATE(2020, 03, 11)
  UNION ALL SELECT 'Jose', 6, DATE(2020, 03, 17))
SELECT 
user,
max(sum(case date_diff(date(2020,04,15), date, day) between 0 and 28
        when true then spend else 0 end)) over(partition by user) as spend_28_day_sum
FROM users
group by user

+------------------------------+
| user      | spend_28_day_sum |
+------------------------------+
| Andrew    | 4                |
| Daniel    | 3                |
| Isabella  | 3                |
| Jose      | 0                |
+------------------------------+

You could change the specified date for the "window function" to current_date() or cross join with a generated date array to see how users change over time.