I have a table with a created timestamp and id identifier.
I can get number of unique id's per week with:
SELECT date_trunc('week', created)::date AS week, count(distinct id)
FROM my_table
GROUP BY week ORDER BY week;
Now I want to have the accumulated number of created by unique id's per week, something like this:
SELECT date_trunc('week', created)::date AS week, count(distinct id),
(SELECT count(distinct id)
FROM my_table
WHERE date_trunc('week', created)::date <= week) as acc
FROM my_table
GROUP BY week ORDER BY week;
But that doesn't work, as week is not accessible in the sub select (ERROR: column "week" does not exist).
How do I solve this?
I'm using PostgreSQL
Use a cumulative aggregation. But, I don't think you need the distinct, so:
SELECT date_trunc('week', created)::date AS week, count(*) as cnt,
SUM(COUNT(*)) OVER (ORDER BY MIN(created)) as running_cnt
FROM my_table
GROUP BY week
ORDER BY week;
In any case, as you've phrased the problem, you can change cnt to use count(distinct). Your subquery is not using distinct at all.
CTEs or a temp table should fix your problem. Here is an example using CTEs.
WITH abc AS (
SELECT date_trunc('week', created)::date AS week, count(distinct id) as IDCount
FROM my_table
GROUP BY week ORDER BY week;
)
SELECT abc.week, abc.IDcount,
(SELECT count(*)
FROM my_table
WHERE date_trunc('week', created)::date <= adc.week) as acc
FROM abc
GROUP BY week ORDER BY abc.week;
Hope this helps
Related
I am trying to write a query to find month over month percent change in user registration. \
Users table has the logs for user registrations
user_id - pk, integer
created_at - account created date, varchar
activated_at - account activated date, varchar
state - active or pending, varchar
I found the number of users for each year and month. How do I find month over month percent change in user registration? I think I need a window function?
SELECT
EXTRACT(month from created_at::timestamp) as created_month
,EXTRACT(year from created_at::timestamp) as created_year
,count(distinct user_id) as number_of_registration
FROM users
GROUP BY 1,2
ORDER BY 1,2
This is the output of above query:
Then I wrote this to find the difference in user registration in the previous year.
SELECT
*
,number_of_registration - lag(number_of_registration) over (partition by created_month) as difference_in_previous_year
FROM (
SELECT
EXTRACT(month from created_at::timestamp) as created_month
,EXTRACT(year from created_at::timestamp) as created_year
,count( user_id) as number_of_registration
FROM users as u
GROUP BY 1,2
ORDER BY 1,2) as temp
The output is this:
You want an order by clause that contains created_year.
number_of_registration
- lag(number_of_registration) over (partition by created_month order by created_year) as difference_in_previous_year
Note that you don't actually need a subquery for this. You can do:
select
extract(year from created_at) as created_year,
extract(month from created_at) as created_year
count(*) as number_of_registration,
count(*) - lag(count(*)) over(partition by extract(month from created_at) order by extract(year from created_at))
from users as u
group by created_year, created_month
order by created_year, created_month
I used count(*) instead of count(user_id), because I assume that user_id is not nullable (in which case count(*) is equivalent, and more efficient). Casting to a timestamp is also probably superfluous.
These queries work as long as you have data for every month. If you have gaps, then the problem should be addressed differently - but this is not the question you asked here.
I can get the registrations from each year as two tables and join them. But it is not that effective
SELECT
t1.created_year as year_2013
,t2.created_year as year_2014
,t1.created_month as month_of_year
,t1.number_of_registration_2013
,t2.number_of_registration_2014
,(t2.number_of_registration_2014 - t1.number_of_registration_2013) / t1.number_of_registration_2013 * 100 as percent_change_in_previous_year_month
FROM
(select
extract(year from created_at) as created_year
,extract(month from created_at) as created_month
,count(*) as number_of_registration_2013
from users
where extract(year from created_at) = '2013'
group by 1,2) t1
inner join
(select
extract(year from created_at) as created_year
,extract(month from created_at) as created_month
,count(*) as number_of_registration_2014
from users
where extract(year from created_at) = '2014'
group by 1,2) t2
on t1.created_month = t2.created_month
First off, Why are you using strings to hold date/time values? Your 1st step should to define created_at, activated_at as a proper timestamps. In the resulting query I assume this correction. If this is faulty (you do not correct it) then cast the string to timestamp in the CTE generating the date range. But keep in mind that if you leave it as text you will at some point get a conversion exception.
To calculate month-over-month use the formula "100*(Nt - Nl)/Nl" where Nt is the number of users this month and Nl is the number of users last month. There are 2 potential issues:
There are gaps in the data.
Nl is 0 (would incur divide by 0 exception)
The following handles this by first generating the months between the earliest date to the latest date then outer joining monthly counts to the generated dates. When Nl = 0 the query returns NULL indication the percent change could not be calculated.
with full_range(the_month) as
(select generate_series(low_month, high_month, interval '1 month')
from (select min(date_trunc('month',created_at)) low_month
, max(date_trunc('month',created_at)) high_month
from users
) m
)
select to_char(the_month,'yyyy-mm')
, users_this_month
, case when users_last_month = 0
then null::float
else round((100.00*(users_this_month-users_last_month)/users_last_month),2)
end percent_change
from (
select the_month, users_this_month , lag(users_this_month) over(order by the_month) users_last_month
from ( select f.the_month, count(u.created_at) users_this_month
from full_range f
left join users u on date_trunc('month',u.created_at) = f.the_month
group by f.the_month
) mc
) pc
order by the_month;
NOTE: There are several places there the above can be shortened. But the longer form is intentional to show how the final vales are derived.
How can I count the new users for each category who bought in the category for the first by year? For instance, 2015-2020 by year, if someone bought in 2015 for the first it will be counted as a new uesr in 2015 but not in 2016-2020.
Table_1 (Columns: product_name, date, category, sales, user_id)
Want to get the result as bleow
You’ll want to start with a sub query to get the first date each user purchased in the category. This is a pretty straightforward group by problem:
select
user_id,
category,
min(date) as first_category_purchase
from my_table
group by user_id, category;
Next, you can use Postgres’s date_trunc function to group by year and category, using your first query as a sub query:
select
category,
date_trunc('year', first_category_purchase)
count(*)
from (
select
user_id,
category,
min(date) as first_category_purchase
from my_table
group by user_id, category
) a
group by 1, 2;
In Postgres, one method is group by after a distinct on:
select date, count(*) as num_new_users
from (select distinct on (user_id, category) t.*
from t
order by user_id, category, date asc
) d
group by date
order by date;
If date is really a date and not a year, then you need something like to_char() or date_trunc() to convert it to a year.
In my table trips , I have two columns: created_at and user_id
My goal is to count unique user_ids per month with a query in postgres. So far, I have written this - but it returns an error
SELECT user_id,
to_char(created_at, 'YYYY-MM') as t COUNT(*)
FROM (SELECT DISTINCT user_id
FROM trips) group by t;
How should I change this query?
The query is much simpler than that:
SELECT to_char(created_at, 'YYYY-MM') as yyyymm, COUNT(DISTINCT user_id)
FROM trips
GROUP BY yyyymm
ORDER BY yyyymm;
I'm attempting to find the number of new users per month by product type. However, I continue to receive an error requesting cnt to be used in an aggregate function.
SELECT EXTRACT(MONTH FROM date) AS month
FROM (SELECT users.date,
COUNT(*) OVER(PARTITION BY product_type) AS cnt FROM users) AS u
GROUP BY month
ORDER BY cnt DESC;
That seems like a very strange construct. Here is a method that doesn't use window functions:
select date_trunc('month', date) as yyyymm, product_id, count(*)
from (select distinct on (u.userid) u.*
from users u
order by u.userid, u.date
) u
group by date_trunc('month', date), product_id
order by yyyymm, product_id;
I'm working on a piece of SQL at the moment and i need to retrieve every row of a dataset with a median and an average aggregated in it.
Example
i have the following set
ID;month;value
and i would like to retrieve something like :
ID;month;value;average for this month;median for this month
without having to group by my result.
So it would be something like :
SELECT ID,month,value,
(SELECT AVG(value) FROM myTable) as "myAVG"
FROM myTable
but i would need that average to be the average for that month specifically. So, rows where the month="January" will have the average and median for "January" etc ...
Issue here is that i did not find a way to refer to the value of month in my subquery
(SELECT AVG(value) FROM myTable)
Does someone have a clue?
P.S: It's a redshift database i'm working on.
You would need to select all rows from the table, and do a left join with a select statement that does group by month. This way, you would get every row, and the group by results with them for that month.
Something like this:
SELECT * FROM myTable a
LEFT JOIN
(
SELECT Month, Sum(value being summed) as mySum
FROM myTable
GROUP BY Month
) b
ON a.Month = b.Month
Helpful?
with myavg as
(SELECT month, AVG(value) as avgval FROM myTable group by month)
, mymed as
(select month, median(value) as medval from myTable group by month)
select ID, month, value, ma.avgval, mm.medval
from mytable m left join myavg ma
on m.month = ma.month
left join mymed mm
on m.month = mm.month
You can use a cte to do this. However, you need a group by on month, as you are calculating an aggregate value.
In Redshift you can use Window Function.
select month,
avg(value) over
(PARTITION BY month rows unbounded preceding) as avg
from myTable
order by 1;