Number based on condition

I’m trying to generate a number based on a condition. When there is yes in column ‘Stop’ in the partition of a Client ordered by Start_Date, the Dense Rank has to start over. So I tried several things but it’s stil not what I want. My table with current number and expected number

+-----------+------------+------+------------+-------------+
| Client_No | Start_Date | Stop | Current_No | Expected_No |
+-----------+------------+------+------------+-------------+
|     1     |  1-1-2018  |  No  |      1     |      1      |
+-----------+------------+------+------------+-------------+
|     1     |  1-2-2018  |  No  |      2     |      2      |
+-----------+------------+------+------------+-------------+
|     1     |  1-3-2018  |  No  |      3     |      3      |
+-----------+------------+------+------------+-------------+
|     1     |  1-4-2018  |  Yes |      1     |      1      |
+-----------+------------+------+------------+-------------+
|     1     |  1-5-2018  |  No  |      4     |      2      |
+-----------+------------+------+------------+-------------+
|     1     |  1-6-2018  |  No  |      5     |      3      |
+-----------+------------+------+------------+-------------+
|     2     |  1-2-2018  |  No  |      1     |      1      |
+-----------+------------+------+------------+-------------+
|     2     |  1-3-2018  |  No  |      2     |      2      |
+-----------+------------+------+------------+-------------+
|     2     |  1-4-2018  |  Yes |      1     |      1      |
+-----------+------------+------+------------+-------------+
|     2     |  1-5-2018  |  No  |      3     |      2      |
+-----------+------------+------+------------+-------------+
|     2     |  1-6-2018  |  Yes |      2     |      1      |
+-----------+------------+------+------------+-------------+

The query I used so far:

DENSE_RANK() OVER(PARTITION BY Client_No, Stop ORDER BY Start_Date ASC)

This seems not to be the solution because it counts onwart from the value ‘no’, but I don’t no how to handle this in another way.

Answer

One way to solve such a Gaps-And-Islands puzzle is to first calculate a rank that starts with the ‘Yes’ stops.

Then calculate the row_number or dense_rank also over that rank.

For example:

create table test 
(
  Id int identity(1,1) primary key,
  Client_No int,
  Start_Date date,
  Stop varchar(3)
)

insert into test 
(Client_No, Start_Date, Stop) values
  (1,'2018-01-01','No')
 ,(1,'2018-02-01','No')
 ,(1,'2018-03-01','No')
 ,(1,'2018-04-01','Yes')
 ,(1,'2018-05-01','No')
 ,(1,'2018-06-01','No')

 ,(2,'2018-02-01','No')
 ,(2,'2018-03-01','No')
 ,(2,'2018-04-01','Yes')
 ,(2,'2018-05-01','No')
 ,(2,'2018-06-01','Yes')

select *
, row_number() over (partition by Client_no, Rnk order by start_date) as rn
from
(
  select *
  , sum(case when Stop = 'Yes' then 1 else 0 end) over (partition by Client_No order by start_date) rnk
  from test
) q
order by Client_No, start_date
GO

Id | Client_No | Start_Date          | Stop | rnk | rn
-: | --------: | :------------------ | :--- | --: | :-
 1 |         1 | 01/01/2018 00:00:00 | No   |   0 | 1 
 2 |         1 | 01/02/2018 00:00:00 | No   |   0 | 2 
 3 |         1 | 01/03/2018 00:00:00 | No   |   0 | 3 
 4 |         1 | 01/04/2018 00:00:00 | Yes  |   1 | 1 
 5 |         1 | 01/05/2018 00:00:00 | No   |   1 | 2 
 6 |         1 | 01/06/2018 00:00:00 | No   |   1 | 3 
 7 |         2 | 01/02/2018 00:00:00 | No   |   0 | 1 
 8 |         2 | 01/03/2018 00:00:00 | No   |   0 | 2 
 9 |         2 | 01/04/2018 00:00:00 | Yes  |   1 | 1 
10 |         2 | 01/05/2018 00:00:00 | No   |   1 | 2 
11 |         2 | 01/06/2018 00:00:00 | Yes  |   2 | 1 

db<>fiddle here

The difference between using this:

row_number() over (partition by Client_no, Rnk order by start_date)

versus this:

dense_rank() over (partition by Client_no, Rnk order by start_date)

is that the dense_rank would calculate the same number for the same start_date per Client_no & Rnk.