Fill in blank dates for rolling average - CTE in Snowflake - sql

Question

I have two tables – activity and purchase
Activity table:
user_id date videos_watched
1 2020-01-02 3
1 2020-01-04 5
1 2020-01-07 5
Purchase table:
user_id purchase_date
1 2020-01-01
2 2020-02-02
What I would like to do is to get a 30 day rolling average since purchase on how many videos has been watched.
The base query is like this:
SELECT
DATEDIFF(DAY, p.purchase_date, a.date) AS day_since_purchase,
AVG(A.VIDEOS_VIEWED)
FROM PURCHASE P
LEFT OUTER JOIN ACTIVITY A ON P.USER_ID = A.USER_ID AND
A.DATE >= P.PURCHASE_DATE AND A.DATE <= DATEADD(DAY, 30, P.PURCHASE_DATE)
GROUP BY 1;
However, the Activity table only has records for each day a video has been logged. I would like to fill in the blanks for days a video has not been viewed.
I have started to look into using a CTE like this:
WITH cte AS (
SELECT date('2020-01-01') as fdate
UNION ALL
SELECT CAST(DATEADD(day,1,fdate) as date)
FROM cte
WHERE fdate < date('2020-04-01')
) select * from cte
cross join purchases p
left outer join activity a
on p.user id = a.user_id
and a.fdate = p.purchase_date
and a.date >= p.purchase_date and a.date <= dateadd(day, 30, p.purchase_date)
The end goal is to have something like this:
days_since_purchase videos_watched
1 3
2 0 --CTE coalesce inserted value
3 0
4 5
Been trying for the last couple of hours to get it right, but still can't really get the hang of it.

Answer 1

If you want to fill in the gaps in the result set, then I think you should be generating integers rather than dates:
WITH cte AS (
SELECT 1 as day_since_purchase
UNION ALL
SELECT 1 + day_since_purchase
FROM cte
WHERE day_since_purchase < 4
)
SELECT cte.day_since_purchase, COALESCE(avg_videos_viewed, 0)
FROM cte LEFT JOIN
(SELECT DATEDIFF(DAY, p.purchase_date, a.date) AS day_since_purchase,
AVG(A.VIDEOS_VIEWED) as avg_videos_viewed
FROM purchases p JOIN
activity a
ON p.user id = a.user_id AND
a.fdate = p.purchase_date AND
a.date >= p.purchase_date AND
a.date <= dateadd(day, 30, p.purchase_date)
GROUP BY 1
) pa
ON pa.day_since_purchase = cte.day_since_purchase;

Answer 2

You can use a recursive query to generate the 30 days following each purchase, then bring the activity table:
with cte as (
select
purchase_date,
client_id,
0 days_since_purchase,
purchase_date dt
from purchases
union all
select
purchase_date,
client_id,
days_since_purchase + 1
dateadd(day, days_since_purchase + 1, purchase_date)
from cte
where days_since_purchase < 30
)
select
c.days_since_purchase,
avg(colaesce(a. videos_watch, 0)) avg_ videos_watch
from cte c
left join activity a
on a.client_id = c.client_id
and a.fdate = c.purchase_date
and a.date = c.dt
group by c.days_since_purchase
Your question is unclear on whether you have a column in the activity table that stores the purchase date each row relates to. Your query has column fdate but not your sample data. I used that column in the query (without such column, you might end up counting the same activity in different purchases).