Transforming columns to row values - sql

Question

Have a table in Google Bigquery like this with 1 id column (customers) and 3 store-name columns:
id |PA|PB|Mall|
----|--|--|----|
3699|1 |1 | 1 |
1017| |1 | 1 |
9991|1 | | |
My objective is to have the option to select customers (id's) who visited for example:
ONLY PA
PA and PB
PA and Mall
PA, PB and Mall
One alternative output could be:
id |Store |
----|--------- |
3699|PA+PB+Mall|
1017|PB+Mall |
9991|PA |
However this would not give me counts of all stopping by PA regardless of other stores visited. In the example above that count would have been 2 (3699 and 9991).
A second alternative output could be:
id |Store|
----|-----|
3699|PA |
3699|PB |
3699|Mall |
1017|PB |
1017|Mall |
9991|PA |
However, this would not allow me (i think) to select/filter those who has visited for example BOTH PA and Mall (only 3699)
A third alternative output could be a combo:
id |Store| Multiple store|
----|-----|---------------|
3699|PA | PA+PB+Mall |
3699|PB | PA+PB+Mall |
3699|Mall | PA+PB+Mall |
1017|PB | PB+Mall |
1017|Mall | PB+Mall |
9991|PA | |
What option is the best and is there any other alternatives to achieve my objective? I believe alternative 3 could be best, but not sure how to achieve it.

Answer 1

It depends what you want. For instance, the third would simply be:
select t.*,
string_agg(store, '+') over (partition by id)
from t;
The second would be:
select id, string_agg(store, '+')
from t
group by id;

Answer 2

For the third option, you may try unpivoting your current table, then applying STRING_AGG to get the computed column containing all stores for each id:
WITH cte AS (
SELECT id, CASE WHEN PA = 1 THEN 'PA' END AS Store
FROM yourTable
UNION ALL
SELECT id, CASE WHEN PB = 1 THEN 'PB' END
FROM yourTable
UNION ALL
SELECT id, CASE WHEN Mall = 1 THEN 'Mall' END
FROM yourTable
)
SELECT id, Store,
STRING_AGG(store, '+') OVER (PARTITION BY id) All_Stores
FROM cte
WHERE Store IS NOT NULL
ORDER BY id, Store;

Answer 3

Consider below approaches to all three options
Assuming input data is filled with nulls when it is empty in question's sample
with `project.dataset.table` as (
select 3699 id, 1 PA, 1 PB, 1 Mall union all
select 1017, null, 1, 1 union all
select 9991, 1, null, null
)
Option #1
select id, string_agg(key, '+') as Store
from `project.dataset.table` t,
unnest(split(translate(to_json_string(t), '{}"', ''))) kv,
unnest([struct(split(kv,':')[offset(0)] as key, split(kv,':')[offset(1)] as value)])
where key !='id'
and value != 'null'
group by id
with output
Option #2
select id, key as Store
from `project.dataset.table` t,
unnest(split(translate(to_json_string(t), '{}"', ''))) kv,
unnest([struct(split(kv,':')[offset(0)] as key, split(kv,':')[offset(1)] as value)])
where key !='id'
and value != 'null'
with output
Option #3
select id, key as Store,
string_agg(key, '+') over(partition by id) as Multiple_Store
from `project.dataset.table` t,
unnest(split(translate(to_json_string(t), '{}"', ''))) kv,
unnest([struct(split(kv,':')[offset(0)] as key, split(kv,':')[offset(1)] as value)])
where key !='id'
and value != 'null'
with output