Right now I'm in the testing phase of this query so I'm only testing it on two Queries. I've gotten stuck on the final part where I want to left join everything (this will have to be extended to 12 separate queries). The problem is basically as the title suggests--I want to join 12 queries on the created Row_Num column using the WITH() statement, instead of creating 12 separate tables and saving them as table in a database.
WITH Jan_Table AS
(SELECT ROW_NUMBER() OVER (ORDER BY a.SALE_DATE) as Row_ID, a.SALE_DATE, sum(a.revenue) as Jan_Rev
FROM ba.SALE_TABLE a
WHERE a.SALE_DATE BETWEEN '2015-01-01' and '2015-01-31'
GROUP BY a.SALE_DATE)
SELECT ROW_NUMBER() OVER (ORDER BY a.SALE_DATE) as Row_ID, a.SALE_DATE, sum(a.revenue) as Jun_Rev, j.Jan_Rev
FROM ba.SALE_TABLE a
LEFT JOIN Jan_Table j
on "j.Row_ID" = a.Row_ID
WHERE a.SALE_DATE BETWEEN '2015-06-01' and '2015-06-30'
GROUP BY a.SALE_DATE
And then I get this error message:
ERROR: column "j.Row_ID" does not exist
I put in the "j.Row_ID" because the previous message was:
ERROR: column a.row_id does not exist Hint: Perhaps you meant to
reference the column "j.row_id".
Each query works individually without the JOIN and WITH functions. I have one for every month of the year and want to join 12 of these together eventually.
The output should be a single column with ROW_NUM and 12 Monthly Revenues columns. Each row should be a day of the month. I know not every month has 31 days. So, for example, Feb only has 28 days, meaning I'd want days 29, 30, and 31 as NULLs. The query above still has the dates--but I will remove the "SALE_DATE" column after I can just get these two queries to join.
My initially thought was just to create 12 tables but I think that'd be a really bad use of space and not the most logical solution to this problem if I were to extend this solution.
edit
Below are the separate outputs of the two qaruies above and the third table is what I'm trying to make. I can't give you the raw data. Everything above has been altered from the actual column names and purposes of the data that I'm using. And I don't know how to create a dataset--that's too above my head in SQL.
Jan_Table (first five lines)
Row_Num Date Jan_Rev
1 2015-01-01 20
2 2015-01-02 20
3 2015-01-03 20
4 2015-01-04 20
5 2015-01-05 20
Jun_Table (first five lines)
Row_Num Date Jun_Rev
1 2015-06-01 30
2 2015-06-02 30
3 2015-06-03 30
4 2015-06-04 30
5 2015-06-05 30
JOINED_TABLE (first five lines)
Row_Num Date Jun_Rev Date Jan_Rev
1 2015-06-01 30 2015-01-01 20
2 2015-06-02 30 2015-01-02 20
3 2015-06-03 30 2015-01-03 20
4 2015-06-04 30 2015-01-04 20
5 2015-06-05 30 2015-01-05 20
It seems like you can just use group by and conditional aggregation for your full query:
select day(sale_date),
max(case when month(sale_date) = 1 then sale_date end) as jan_date,
max(case when month(sale_date) = 1 then revenue end) as jan_revenue,
max(case when month(sale_date) = 2 then sale_date end) as feb_date,
max(case when month(sale_date) = 2 then revenue end) as feb_revenue,
. . .
from sale_table s
group by day(sale_date)
order by day(sale_date);
You haven't specified the database you are using. DAY() is a common function to get the day of the month; MONTH() is a common function to get the months of the year. However, those particular functions might be different in your database.
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
Below are my two tables of data
Acct BillingDate REV
101 01/05/2018 5
101 01/30/2018 4
102 01/15/2018 2
103 01/4/2018 3
103 02/05/2018 2
106 03/06/2018 5
Acct BillingDate Lease_Rev
101 01/15/2018 2
102 01/16/2018 1
103 01/19/2018 2
104 02/05/2018 3
105 04/02/2018 1
Desired Output
Acct Jan Feb Mar Apr
101 11
102 3
103 5 2
104 3
105 1
106 5
My SQL Script is Below:
SELECT [NewSalesHistory].[Region]
,[NewSalesHistory].[Account]
,SUM(case when [NewSalesHistory].[billingdate] between '6/1/2016' and '6/30/2016' then REV else 0 end ) + [X].[Jun-16] AS 'Jun-16'
FROM [NewSalesHistory]
FULL join (SELECT [Account]
,SUM(case when [BWLease].[billingdate] between '6/1/2016' and '6/30/2016' then Lease_REV else 0 end ) as 'Jun-16'
FROM [AirgasPricing].[dbo].[BWLease]
GROUP BY [Account]) X ON [NewSalesHistory].[Account] = [X].[Account]
GROUP BY [NewSalesHistory].[Region]
,[NewSalesHistory].[Account]
,[X].[Jun-16]
I am having trouble combining these tables. If there is a rev amt and lease rev amt then it will combine (sum) for that account. If there is not a lease rev amt (which is the majority of the time), it brings back NULLs for all other rev amts accounts in Table 1. Table one can have duplicate accounts with different Rev, while the Table two is one unique account only w Lease rev. The output above is how I would like to see the data.
What am I missing here? Thanks!
I would suggest union all and group by:
select acct,
sum(case when billingdate >= '2016-01-01' and billingdate < '2016-02-01' then rev end) as rev_201601,
sum(case when billingdate >= '2016-02-01' and billingdate < '2016-03-01' then rev end) as rev_201602,
. . .
from ((select nsh.acct, nsh.billingdate, nsh.rev
from NewSalesHistory
) union all
(select bl.acct, bl.billingdate, bl.rev
from AirgasPricing..BWLease bl
)
) x
group by acct;
Okay, so there are a few things going on here:
1) As Gordon Linoff mentioned you can perform a union all on the two tables. Be sure to limit your column selections and name your columns appropriately:
select
x as consistentname1,
y as consistentname2,
z as consistentname3
from [NewSalesHistory]
union all
select
a as consistentname1,
b as consistentname2,
c as consistentname3
from [BWLease]
2) Your desired result contains a pivoted month column. Generate a column with your desired granularity on the result of the union in step one. F.ex. months:
concat(datepart(yy, Date_),'-',datename(mm,Date_)) as yyyyM
Then perform aggregation using a group by:
select sum(...) as desiredcolumnname
...
group by PK1, PK2, yyyyM
Finally, PIVOT to obtain your result: https://learn.microsoft.com/en-us/sql/t-sql/queries/from-using-pivot-and-unpivot?view=sql-server-2017
3) If you have other fields/columns that you wish to present then you first need to determine whether they are measures (can be aggregated) or are dimensions. That may be best addressed in a follow up question after you've achieved what you set out for in this part.
Hope it helps
As an aside, it seems like you are preparing data for reporting. Performing these transformations can be facilitated using a GUI such as MS Power Query. As long as your end goal is not data manipulation in the DB itself, you do not need to resort to raw sql.
I have a table with data as follows
Person_ID Date Sale
1 2016-05-08 2686
1 2016-05-09 2688
1 2016-05-14 2689
1 2016-05-18 2691
1 2016-05-24 2693
1 2016-05-25 2694
1 2016-05-27 2695
and there are a million such id's for different people. Sale count is recorded only when a sale increases else it is not. Therefore data for id' 2 can be different from id 1.
Person_ID Date Sale
2 2016-05-10 26
2 2016-05-20 29
2 2016-05-18 30
2 2016-05-22 39
2 2016-05-25 40
Sale count of 29 on 5/20 means he sold 3 products on 20th, and had sold 26 till 5/10 with no sale in between these 2 dates.
Question: I want a sql/dynamic sql to calculate the daily a sales of all the agents and produce a report as follows:
ID Sale_511 Sale_512 Sale_513 -------------- Sale_519 Sale_520
2 0 0 0 --------------- 0 3
(29-26)
Question is how do I use that data to calculate a report. As I do have data between 5/20 to 5/10. SO i can just write a query saying A-B = C?
Can anyone help? Thank you.
P.S - New to SQL so learning.
Using Sql Server 2008.
Most SQL dialects support the lag() function. You can get what you want as:
select person_id, date,
(sale - lag(sale) over (partition by person_id, date)) as Daily_Sales
from t;
This produces one row per date for each person. This format is more typical for how SQL would return such results.
In SQL Server 2008, you can do:
select t.person_id, t.date,
(t.sale - t2.sale) as Daily_Sales
from t outer apply
(select top 1 t2.*
from t t2
where t2.person_id = t.person_id and t2.date < t.date
) t2
I have the below table. I want to select 30 pieces of Product Code 011A from the table. Each row contains a number of pieces, in the column PCS. I want to select the 30 pieces in FIFO order based on date, and return the number of pieces selected from each row, so I'll need to know the primary key value for each row that has pieces selected from it. For example, from this data:
Key Product Code PCS Date
1 011A 10 2015-07-01
2 011B 20 2015-07-01
3 011C 20 2015-07-01
4 011A 12 2015-07-02
5 011A 40 2015-07-03
6 011D 60 2015-07-04
7 011A 20 2015-07-04
Selecting 30 pieces of product code "011A" should give an output table like:
Key Product Code PCS DATE
1 011A 10 2015-07-01
4 011A 12 2015-07-02
5 011A 8 2015-07-03
You can see that the total number of pieces is 30, and that the maximum number of pieces were selected from the rows with primary key 1 and 4, because they're the first dates. Only 8 were selected from row #5, because it's the next in date order, and only 8 is needed to reach 30 total. Row #7 wasn't needed, so it doesn't show up in the result.
How can I write a query to accomplish this?
In SQL Server 2012, you can use cumulative sum:
select t.*
from (select t.*,
sum(pcs) over (partition by productcode order by date) as cumepcs
from thetable t
where productcode = '011A'
) t
where cumepcs - pcs < 30;
Doing a cumulative sum in SQL Server 2008 is a bit more work. Here is one way:
select t.*
from (select t.*,
(select sum(t2.pcs)
from thetable t2
where t2.productcode = t.productcode and
t2.date <= t.date
) as cumepcs
from thetable t
where productcode = '011A'
) t
where cumepcs - pcs < 30;
EDIT:
If you want the allocated amounts from each bucket, you need to tweak the size of the last bucket. Change the select to:
select t.*,
(case when cume_pcs <= 30 then pcs
else 30 - (cumepcs - pcs)
end) as allocated_pcs
. . .
I was trying to aggregate a 7 days data for FY13 (starts on 10/1/2012 and ends on 9/30/2013) in SQL Server but so far no luck yet. Could someone please take a look. Below is my example data.
DATE BREAD MILK
10/1/12 1 3
10/2/12 2 4
10/3/12 2 3
10/4/12 0 4
10/5/12 4 0
10/6/12 2 1
10/7/12 1 3
10/8/12 2 4
10/9/12 2 3
10/10/12 0 4
10/11/12 4 0
10/12/12 2 1
10/13/12 2 1
So, my desired output would be like:
DATE BREAD MILK
10/1/12 1 3
10/2/12 2 4
10/3/12 2 3
10/4/12 0 4
10/5/12 4 0
10/6/12 2 1
Total 11 15
10/7/12 1 3
10/8/12 2 4
10/9/12 2 3
10/10/12 0 4
10/11/12 4 0
10/12/12 2 1
10/13/12 2 1
Total 13 16
--------through 9/30/2013
Please note, since FY13 starts on 10/1/2012 and ends on 9/30/2012, the first week of FY13 is 6 days instead of 7 days.
I am using SQL server 2008.
You could add a new computed column for the date values to group them by week and sum the other columns, something like this:
SELECT DATEPART(ww, DATEADD(d,-2,[DATE])) AS WEEK_NO,
SUM(Bread) AS Bread_Total, SUM(Milk) as Milk_Total
FROM YOUR_TABLE
GROUP BY DATEPART(ww, DATEADD(d,-2,[DATE]))
Note: I used DATEADD and subtracted 2 days to set the first day of the week to Monday based on your dates. You can modify this if required.
Use option with GROUP BY ROLLUP operator
SELECT CASE WHEN DATE IS NULL THEN 'Total' ELSE CONVERT(nvarchar(10), DATE, 101) END AS DATE,
SUM(BREAD) AS BREAD, SUM(MILK) AS MILK
FROM dbo.test54
GROUP BY ROLLUP(DATE),(DATENAME(week, DATE))
Demo on SQLFiddle
Result:
DATE BREAD MILK
10/01/2012 1 3
10/02/2012 2 4
10/03/2012 2 3
10/04/2012 0 4
10/05/2012 4 0
10/06/2012 2 1
Total 11 15
10/07/2012 1 3
10/08/2012 4 7
10/10/2012 0 4
10/11/2012 4 0
10/12/2012 2 1
10/13/2012 2 1
Total 13 16
You are looking for a rollup. In this case, you will need at least one more column to group by to do your rollup on, the easiest way to do that is to add a computed column that groups them into weeks by date.
Take a lookg at: Summarizing Data Using ROLLUP
Here is the general idea of how it could be done:
You need a derived column for each row to determine which fiscal week that record belongs to. In general you could subtract that record's date from 10/1, get the number of days that have elapsed, divide by 7, and floor the result.
Then you can GROUP BY that derived column and use the SUM aggregate function.
The biggest wrinkle is that 6 day week you start with. You may have to add some logic to make sure that the weeks start on Sunday or whatever day you use but this should get you started.
The WITH ROLLUP suggestions above can help; you'll need to save the data and transform it as you need.
The biggest thing you'll need to be able to do is identify your weeks properly. If you don't have those loaded into tables already so you can identify them, you can build them on the fly. Here's one way to do that:
CREATE TABLE #fy (fyear int, fstart datetime, fend datetime);
CREATE TABLE #fylist(fyyear int, fydate DATETIME, fyweek int);
INSERT INTO #fy
SELECT 2012, '2011-10-01', '2012-09-30'
UNION ALL
SELECT 2013, '2012-10-01', '2013-09-30';
INSERT INTO #fylist
( fyyear, fydate )
SELECT fyear, DATEADD(DAY, Number, DATEADD(DAY, -1, fy.fstart)) AS fydate
FROM Common.NUMBERS
CROSS APPLY (SELECT * FROM #fy WHERE fyear = 2013) fy
WHERE fy.fend >= DATEADD(DAY, Number, DATEADD(DAY, -1, fy.fstart));
WITH weekcalc AS
(
SELECT DISTINCT DATEPART(YEAR, fydate) yr, DATEPART(week, fydate) dt
FROM #fylist
),
ridcalc AS
(
SELECT
ROW_NUMBER() OVER (ORDER BY yr, dt) AS rid, yr, dt
FROM weekcalc
)
UPDATE #fylist
SET fyweek = rid
FROM #fylist
JOIN ridcalc
ON DATEPART(YEAR, fydate) = yr
AND DATEPART(week, fydate) = dt;
SELECT list.fyyear, list.fyweek, p.[date], COUNT(bread) AS Bread, COUNT(Milk) AS Milk
FROM products p
JOIN #fylist list
ON p.[date] = list.fydate
GROUP BY list.fyyear, list.fyweek, p.[date] WITH ROLLUP;
The Common.Numbers reference above is a simple numbers table that I use for this sort of thing (goes from 1 to 1M). You could also build that on the fly as needed.