I have 2 tables on Amazon Redshift and they look as follows:
Tablename: Groups
GroupID Created
1 2016-08-04
2 2017-05-24
3 2017-06-12
Tablename: GroupActivities
GroupID CreationTime ActivityType
1 2016-08-13 Assign
1 2016-09-13 Assign
2 2017-05-25 Create
2 2017-05-27 Assign
3 2017-06-24 Create
3 2017-06-28 Assign
I would like to count the number of activities within each 30 day period from group creation. For example, I would like the output to be the following:
GroupID Period ActivityCount
1 Period1 1
1 Period2 1
2 Period1 2
3 Period1 2
I could do this if the dates were not relative, but I'm not sure how to achieve this when the dates are relative. Any help would be much appreciated.
TIA.
join the tables by group id, use integer division of date difference to identify the period and aggregate:
select
group_id
,'Period'||((a.creationtime::date-g.created::date)/30+1)::varchar as period
,count(1) as activity_count
from groups g
join activities a
on g.groupid=a.groupid
group by 1,2
Related
Suppose I have patient admission/claim wise data like the sample below. Data type of patient_id and hosp_id columns is VARCHAR
Table name claims
rec_no
patient_id
hosp_id
admn_date
discharge_date
1
1
1
01-01-2020
10-01-2020
2
2
1
31-12-2019
11-01-2020
3
1
1
11-01-2020
15-01-2020
4
3
1
04-01-2020
10-01-2020
5
1
2
16-01-2020
17-01-2020
6
4
2
01-01-2020
10-01-2020
7
5
2
02-01-2020
11-01-2020
8
6
2
03-01-2020
12-01-2020
9
7
2
04-01-2020
13-01-2020
10
2
1
31-12-2019
10-01-2020
I have another table wherein bed strength/max occupancy strength of hospitals are stored.
table name beds
hosp_id
bed_strength
1
3
2
4
Expected Results I want to find out hospital-wise dates where its declared bed-strength has exceeded on any day.
Code I have tried Nothing as I am new to SQL. However, I can solve this in R with the following strategy
pivot_longer the dates
tidyr::complete() missing dates in between
summarise or aggregate results for each date.
Simultaneously, I also want to know that whether it can be done without pivoting (if any) in sql because in the claims table there are 15 million + rows and pivoting really really slows down the process. Please help.
You can use generate_series() to do something very similar in Postgres. For the occupancy by date:
select c.hosp_id, gs.date, count(*) as occupanyc
from claims c cross join lateral
generate_series(admn_date, discharge_date, interval '1 day') gs(date)
group by c.hosp_id, gs.date;
Then use this as a subquery to get the dates that exceed the threshold:
select hd.*, b.strength
from (select c.hosp_id, gs.date, count(*) as occupancy
from claims c cross join lateral
generate_series(c.admn_date, c.discharge_date, interval '1 day') gs(date)
group by c.hosp_id, gs.date
) hd join
beds b
using (hosp_id)
where h.occupancy > b.strength
This is my table:
id user_id date balance
1 1 2016-05-10 10
2 1 2016-05-10 30
3 2 2017-04-24 5
4 2 2017-04-27 10
5 3 2017-11-10 40
I want to group the rows by user_id and sum the balance, but so that the sum is equal or less than 30. Moreover, I need to display the minimum date in the group. It should look like this:
id balance date_start
1-1 10 2016-05-10
1-2 30 2016-05-10
2-1 15 2017-04-24
Excuse for my language. Thanks.
You should be able to do so by using group by & having, here is an example of what may solve your case :
SELECT id, user_id, SUM(balance) as balance, data_start
FROM your_table
GROUP BY user_id
HAVING SUM(balance) >= 30
AND MIN(date_start)
This is a good way to do it with one query, but it is a complex query and you should be careful if using it on a very large tables.
In my table trips , I have two columns: created_at and user_id
Unique users take many different trips. My goal is to count the very first trip made unique per each user_ids per year-month. I understand that in this case the min() function should be applied.
In a previous query, all unique users per year-month were aggregated:
SELECT to_char(created_at, 'YYYY-MM') as yyyymm, COUNT(DISTINCT user_id)
FROM trips
GROUP BY yyyymm
ORDER BY yyyymm;
Where in the above query should min() be integrated? In other words, instead of counting all unique user id's per month, I only need to count the first occurrence of unique user id per month.
The sample input would look like:
> routes
user_id created_at
1 1 2015-08-07 07:18:21
2 2 2015-05-06 20:43:52
3 3 2015-05-06 20:53:54
4 1 2015-03-30 20:09:07
5 2 2015-10-01 18:28:32
6 3 2015-08-07 07:29:29
7 1 2015-08-28 13:45:44
8 2 2015-08-07 07:37:31
9 3 2015-03-30 20:14:04
10 1 2015-08-07 07:08:50
And the output would be:
count Y-m
1 0 2015-01
2 0 2015-02
3 2 2015-03
4 0 2015-04
5 1 2015-05
Because the first occurrences of user_id 1 and 3 were in March and the first occurrence of user_id 2 was in May
You can do this with 2 levels of aggregation. Get the min time per user_id and then count.
SELECT to_char(first_time, 'YYYY-MM'),count(*)
from (
SELECT user_id,MIN(created_at) as first_time
FROM trips
GROUP BY user_id
) t
GROUP BY to_char(first_time, 'YYYY-MM')
I have a raw table recording customer ids coming to a store over a particular time period. Using Impala, I would like to calculate the number of distinct customer IDs coming to the store until each day. (e.g., on day 3, 5 distinct customers visited so far)
Here is a simple example of the raw table I have:
Day ID
1 1234
1 5631
1 1234
2 1234
2 4456
2 5631
3 3482
3 3452
3 1234
3 5631
3 1234
Here is what I would like to get:
Day Count(distinct ID) until that day
1 2
2 3
3 5
Is there way to easily do this in a single query?
Not 100% sure if will work on impala
But if you have a table days. Or if you have a way of create a derivated table on the fly on impala.
CREATE TABLE days ("DayC" int);
INSERT INTO days
("DayC")
VALUES (1), (2), (3);
OR
CREATE TABLE days AS
SELECT DISTINCT "Day"
FROM sales
You can use this query
SqlFiddleDemo in Postgresql
SELECT "DayC", COUNT(DISTINCT "ID")
FROM sales
cross JOIN days
WHERE "Day" <= "DayC"
GROUP BY "DayC"
OUTPUT
| DayC | count |
|------|-------|
| 1 | 2 |
| 2 | 3 |
| 3 | 5 |
UPDATE VERSION
SELECT T."DayC", COUNT(DISTINCT "ID")
FROM sales
cross JOIN (SELECT DISTINCT "Day" as "DayC" FROM sales) T
WHERE "Day" <= T."DayC"
GROUP BY T."DayC"
try this one:
select day, count(distinct(id)) from yourtable group by day
I'm very new to SQL and am having trouble figuring out how to find some average values. Basically I have table with 3 columns, Date, ID, and Value. I'm trying to find the average for each ID on a daily basis. The actual table has thousands of entries with a varrying number of values logged for each ID on each day. Sample table below-
SAMPLE TABLE
Date ID Value
1-1-14 12:45 1 5
1-1-14 13:45 1 6
1-1-14 08:45 2 8
1-1-14 10:45 2 8
1-2-14 01:45 1 2
1-2-14 04:45 1 4
1-2-14 01:45 2 20
1-2-14 04:45 2 24
SAMPLE RESULTS
Date ID AvgValue
1-1-14 1 5.5
1-1-14 2 8
1-2-14 1 3
1-2-14 2 22
I would greatly appreciate any help! Thanks!
The basic query is simple:
select date, id, avg(value)
from your_table
group by date, id, avg
However, as you just want the date part of the datetime column you can cast/convert that to a narrower date type, and also, if the value that you're averaging is an int you might have to cast it to a floating point type first. For SQL Server the query could look like this:
select
cast(date as date) as date,
id,
avg(cast(value as decimal(10,5))) as avg
from table1
group by cast(date as date), id
order by 1
Sample SQL Fiddle