Counting number of transactions in the following week SQL - sql

Question

I am trying to get a count of the number of transactions that occur in the 7 days following each transaction in a dataset (including the transaction itself). For example, in the following set of transactions I want the count to be as follows.
┌─────────────┬──────────────┐
│ Txn_Date │ Count │
├─────────────┼──────────────┤
│ 2020-01-01 │ 3 │
│ 2020-01-04 │ 3 │
│ 2020-01-05 │ 2 │
│ 2020-01-10 │ 1 │
│ 2020-01-18 │ 3 │
│ 2020-01-20 │ 2 │
│ 2020-01-24 │ 2 │
│ 2020-01-28 │ 1 │
└─────────────┴──────────────┘
For each one it needs to be a rolling week, so row 1 counts all the transactions between 2020-01-01 and 2020-01-08, the second all transactions between 2020-01-04 and 2020-01-11 etc.
The code I have is:
select Txn_Date,
count(Txn_Date) over (partition by Txn_Date(column) where Txn_Date(rows) between Txn_Date and date_add('day', 14, Txn_Date) as Count
This code will not work in it's current state, but hopefully gives an idea of what I am trying to achieve. The database I am working in is Hive.

Answer 1

A good way to provide demo data is to put it into a table variable.
DECLARE #table TABLE (Txn_Date DATE, Count INT)
INSERT INTO #table (Txn_Date, Count) VALUES
('2020-01-01', 3),
('2020-01-04', 3),
('2020-01-05', 2),
('2020-01-10', 1),
('2020-01-18', 3),
('2020-01-20', 2),
('2020-01-24', 2),
('2020-01-28', 1)
If you're using TSQL you can do this using the windowed function LAG after grouping the data by week.
SELECT DATEPART(WEEK,Txn_Date) AS Week, SUM(COUNT) AS Count, LAG(SUM(COUNT),1) OVER (ORDER BY DATEPART(WEEK,Txn_Date)) AS LastWeekCount
FROM #table
GROUP BY DATEPART(WEEK,Txn_Date)
Week Count LastWeekCount
-----------------------------
1 6 NULL
2 3 6
3 3 3
4 4 3
5 1 4
Lag literally lets you go back n rows for a column in a specific order. For this we wanted to go back 1 row in week number order. To move in the opposite direction you can use LEAD the same way.
We're also using the TSQL function DATEPART to get the ISO week number for the date, and grouping by that.