I have the following dataset:
id | date | state
-----------------------
1 | 01/01/17 | high
1 | 02/01/17 | high
1 | 03/01/17 | high
1 | 04/01/17 | miss
1 | 05/01/17 | high
2 | 01/01/17 | miss
2 | 02/01/17 | high
2 | 03/01/17 | high
2 | 04/01/17 | miss
2 | 05/01/17 | miss
2 | 06/01/17 | high
I want to create a column rank_state
which ranks, within groups of id
, the entries as per increasing date
(starting from rank 0) which do not have the state
of "miss". Furthermore, the rank repeats itself if the entry has a state
of "miss". The output should look like:
id | date | state | rank_state
------------------------------------
1 | 01/01/17 | high | 0
1 | 02/01/17 | high | 1
1 | 03/01/17 | high | 2
1 | 04/01/17 | miss | 2
1 | 05/01/17 | high | 3
2 | 01/01/17 | miss | 0
2 | 02/01/17 | high | 0
2 | 03/01/17 | high | 1
2 | 04/01/17 | miss | 1
2 | 05/01/17 | miss | 1
2 | 06/01/17 | high | 2
For example, the 4th row has a rank of 2 since it's state
is "miss", i.e. it repeats the rank of row 3 (the same applies to rows 9 and 10). Please note that rows 6 and 7 should have rank 0.
I have tried the following:
,(case when state is not in ('miss') then (rank() over (partition by id order by date desc) - 1) end) as state_rank
and
,rank() over (partition by id order by case when state is not in ('miss') then date end) as state_rank
but neither give me the desired result. Any ideas would be very helpful.
Just add frame_clause to vol7ron's answer since Redshift requires it :
More than likely you want:
SQL Fiddle
Basically:
id
'miss'
GREATEST
to use 0 (preventing negatives)