I am trying to get a cumulative SUM of a DATEDIFF into a percentage from some basic data I have, here is a small snapshot:
ID IIn IOut
AB123 2015-11-06 15:24:44.057 2015-11-14 01:00:00.000
QA565 2015-10-27 20:12:19.753 2015-11-06 03:00:00.000
UN555 2015-12-29 06:29:23.417 2016-01-03 08:00:00.000
LG602 2015-08-07 16:52:13.573 2015-08-11 03:00:00.000
ETC ETC
I then use DATEDIFF to get a number of days:
SELECT ID, DATEDIFF(hour, IIn, IOut)/24.0 IDays
FROM TimeTable
Which gives me:
ID IDays
AB123 7.416666
QA565 9.291666
UN555 5.083333
LG602 3.458333
What I want is a count of ID'S split by their IDay's (rounded down) with a cumulative % from lowest IDay's to highest like so:
ID IDays IDaysPer
LG602 3 12.5
UN555 5 33.33
AB123 7 62.49
QA565 9 100
You can do this using a couple of windowed aggregates, placing your original query in a CTE for convenience (A subquery would also work):
declare #timeTable table (ID char(5) not null, IIn datetime not null,
IOut datetime not null)
insert into #timeTable(ID,IIn,IOut) values
('AB123','2015-11-06T15:24:44.057','2015-11-14T01:00:00.000'),
('QA565','2015-10-27T20:12:19.753','2015-11-06T03:00:00.000'),
('UN555','2015-12-29T06:29:23.417','2016-01-03T08:00:00.000'),
('LG602','2015-08-07T16:52:13.573','2015-08-11T03:00:00.000')
;With Diffs as (
SELECT ID, DATEDIFF(hour, IIn, IOut)/24.0 IDays
FROM #timeTable
)
select
*,
(
SUM(IDays) OVER (ORDER BY IDays, ID)
/
SUM(IDays) OVER ()
) * 100 as IDaysPer
from
Diffs
order by IDays
Note that I couldn't quite make sense of your "rounded down" requirement but you should be able to use any common rounding technique wrapped around the appropriate calculation. So my outputs don't quite match yours:
ID IDays IDaysPer
----- --------------------------------------- ---------------------------------------
LG602 3.458333 13.696300
UN555 5.083333 33.828300
AB123 7.416666 63.201300
QA565 9.291666 100.000000
Consider TimeTable has already the data
WITH t1 (ID, IDays)
AS (
SELECT ID, DATEDIFF(hour, IIn, IOut) / 24.0 AS IDays
FROM TimeTable
)
SELECT
ID, FLOOR(IDays),
(FLOOR(IDays) / (SELECT SUM(FLOOR(IDays)) FROM t1 t2 WHERE t1.IDays <= t2.IDays)) * 100.0 AS IDaysPer
FROM t1
ORDER BY 2 ASC
Here you go : Output matches with yours...
create table #TEMp
(ID VARCHAR(100)
,IIn datetime
,IOut datetime
)
insert into #temp(ID,IIn,IOut) values
('AB123','2015-11-06T15:24:44.057','2015-11-14T01:00:00.000'),
('QA565','2015-10-27T20:12:19.753','2015-11-06T03:00:00.000'),
('UN555','2015-12-29T06:29:23.417','2016-01-03T08:00:00.000'),
('LG602','2015-08-07T16:52:13.573','2015-08-11T03:00:00.000')
select ID,IDays AS Idays,ROUND(CAST(SUM(IDays) OVER(ORDER BY IDays) AS FLOAT)/CAST(SUM(IDays)OVER() AS FLOAT) * 100,2) AS IdaysPer
from
(
select *,ROUND(DATEDIFF(hour, IIn, IOut)/24,0) IDays
from #TEMP
)T
Related
Let's assume a table has many columns and a Temporal table is logging its history. There is one field I need to know when it changes.
Number
VersionStartDate
991281
2021-11-12 08:27:11
991281
2021-11-12 08:20:11
NULL
2021-11-12 07:20:11
NULL
2021-11-12 06:20:11
771281
2021-11-11 08:26:11
NULL
2021-11-11 08:25:11
661281
2021-11-10 08:24:11
NULL
2021-11-10 08:22:11
661281
2021-11-10 08:21:11
551281
2021-11-09 08:20:11
I need to get each value, and the moment it changed. I also need to know if it's been set NULL so this query is not giving what I need.
SELECT
Number,
MIN(VersionStartDate) [Date]
FROM _TABLE_
GROUP BY
Number
ORDER BY
[Date] DESC
The result should be
Number
VersionStartDate
991281
2021-11-12 08:20:11
NULL
2021-11-12 06:20:11
771281
2021-11-11 08:26:11
NULL
2021-11-11 06:25:11
661281
2021-11-10 08:24:11
NULL
2021-11-10 08:22:11
661281
2021-11-10 08:21:11
551281
2021-11-09 08:20:11
Quite similar to JMabee's which appeared after I started working on it, but perhaps a bit simpler:
CREATE TABLE #d (Number INT, VersionStartDate DATETIME);
INSERT INTO #d(Number, VersionStartDate)
VALUES
(991281 ,'2021-11-12T08:27:11'),
(991281 ,'2021-11-12T08:20:11'),
(NULL ,'2021-11-12T07:20:11'),
(NULL ,'2021-11-12T06:20:11'),
(771281 ,'2021-11-11T08:26:11'),
(NULL ,'2021-11-11T08:25:11'),
(661281 ,'2021-11-10T08:24:11'),
(NULL ,'2021-11-10T08:22:11'),
(661281 ,'2021-11-10T08:21:11'),
(551281 ,'2021-11-09T08:20:11');
WITH cte AS
(
SELECT Number,
VersionStartDate,
LAG(Number, 1) OVER (ORDER BY VersionStartDate) AS PrevNumber
FROM #d
)
SELECT cte.Number,
cte.VersionStartDate
FROM cte
WHERE ISNULL(cte.Number, -1) <> ISNULL(cte.PrevNumber, -1)
ORDER BY cte.VersionStartDate DESC;
Well this is one way to do it, I am sure there is a more eloquent way to write it, but it gets you started:
CREATE TABLE #T(Number int, VersionStartDate datetime)
INSERT INTO #T vALUES
(991281,'2021-11-12 08:27:11'),
(991281,'2021-11-12 08:20:11'),
(NULL,'2021-11-12 07:20:11'),
(NULL,'2021-11-12 06:20:11'),
(771281,'2021-11-11 08:26:11'),
(NULL,'2021-11-11 08:25:11'),
(661281,'2021-11-10 08:24:11'),
(NULL,'2021-11-10 08:22:11'),
(661281,'2021-11-10 08:21:11'),
(551281,'2021-11-09 08:20:11')
SELECT Number, MIN(VersionStartDate) VersionStartDate
FROM
(
SELECT *, SUM(CASE WHEN ISNULL(Number,-1) <> ISNULL(LG,-1) THEN 1 ELSE 0 END) OVER(ORDER BY VersionStartDate desc) GRP
FROM
(
SELECT *, LAG(Number,1,-1) OVER(ORDER BY VersionStartDate desc) LG
FROM #T
) X
) Y
GROUP BY GRP,Number
ORDER BY VersionStartDate desc
Add VersionStartDate in your group by.
SELECT
Number,
MIN(VersionStartDate)
FROM _TABLE_
GROUP BY
Number, VersionStartDate
ORDER BY
VersionStartDate DESC
I'm working on a table looks like this. The actual dataset contains thousand of Guest_ID, and I'm just showing few sample lines here.
Guest_ID
Visit_ID
Collection_time
Value
6
a178
2007-11-09 11:28:00
2.6
6
a188
2007-11-10 20:28:00
6.6
12
a278
2008-11-11 10:28:00
2.7
12
a278
2008-11-11 11:38:00
3.2
12
a278
2008-11-12 11:48:00
6.8
12
c348
2009-10-12 11:38:00
3.8
15
e179
2013-01-15 09:25:00
1.8
15
e179
2013-01-15 10:26:00
1.6
15
e179
2013-01-15 12:15:00
3.8
15
e179
2013-01-17 09:25:00
3.6
What I'm trying to do here is to find out the values that had increased by at least 3 within the past 48hr, and these values need to be under the same visit_id. In this case, result should only return
Guest_ID
Visit_ID
Collection_time
Value
12
a278
2008-11-12 11:48:00
6.8
I have some vague ideas of creating islands and gaps in SQL Server, but not sure how to approach it. Conceptually, for each value X, I need to extract all the previous value meets the conditions (within past 48hr AND under the same Visit_ID), then check if X - min(previous value) >= 3. And if yes, keep or label X as 1, and repeat the procedure.
I read a lot of posts like using lag() or row_number() over (partition by ... order by ...), but still unsure about what to do. Any help is appreciated!
This would have been a good spot to use window functions with a date range specification. Alas, SQL Server does not support that (yet?).
The simplest approach might be exists and a correlated subquery:
select t.*
from mytable t
where exists (
select 1
from mytable t1
where
t1.visit_id = t.visit_id
and t1.collection_time >= dateadd(day, -2.collection_time)
and t1.collection_time < t.collection_time
and t1.value < t.value - 3
)
Or you can use cross apply:
select t.*
from mytable t
cross apply (
select min(t1.value) as min_value
from mytable t1
where
t1.visit_id = t.visit_id
and t1.collection_time >= dateadd(day, -2.collection_time)
and t1.collection_time < t.collection_time
) t1
where t1.min_value < t.value - 3
I used a CTE to filter out the qualifying rows first and then just join it back up to the original table to grab those rows:
CREATE TABLE #tmp(Guest_ID int, Visit_ID varchar(10), Collection_time datetime, Value decimal(10,1))
INSERT INTO #tmp VALUES
(6, 'a178', '2007-11-09 11:28:00', 2.6),
(6, 'a188', '2007-11-10 20:28:00', 6.6),
(12, 'a278', '2008-11-11 10:28:00', 2.7),
(12, 'a278', '2008-11-11 11:38:00', 3.2),
(12, 'a278', '2008-11-12 11:48:00', 6.8),
(12, 'c348', '2009-10-12 11:38:00', 3.8),
(15, 'e179', '2013-01-15 09:25:00', 1.8),
(15, 'e179', '2013-01-15 10:26:00', 1.6),
(15, 'e179', '2013-01-15 12:15:00', 3.8),
(15, 'e179', '2013-01-17 09:25:00', 3.6)
;WITH CTE AS(
SELECT MAX(Collection_time) MaxCollection_Time, Max(Value) - Min(Value) DiffInValue ,Visit_ID
FROM #tmp
GROUP BY Visit_ID
HAVING Max(Value) - Min(Value) >= 3
)
SELECT t1.*
FROM #tmp t1
INNER JOIN CTE t2 on t1.Visit_ID = t2.Visit_ID and T1.Collection_time = t2.MaxCollection_Time
I have the following table :
Customer_ID PurchaseDatetime
309 2/3/2014 12:29:00
309 2/27/2014 17:11:00
309 4/15/2014 13:24:00
I want to write a query which would calculate the difference between the datetime field of two consecutive rows. Ideally the output should be like
Customer_ID PurchaseDatetime
309 0
309 2/27/2014 17:11:00 - 2/3/2014 12:29:00 // The exact time difference in hours
309 4/15/2014 13:24:00 - 2/27/2014 17:11:00 // The exact time difference in hours
How do I write such a query?
Try this...
CREATE TABLE #Purchases
(
CustomerID INT,
PurchaseDate DATETIME
)
INSERT INTO #Purchases
VALUES
(100004,'2016-05-16 08:00:00'),
(100005,'2016-05-16 09:05:00'),
(100006,'2016-05-16 10:08:40'),
(32141 ,'2016-05-16 11:18:00'),
(84230 ,'2016-05-16 12:25:10'),
(23444 ,'2016-05-16 13:40:00'),
(100001,'2016-05-16 14:50:00')
;WITH CTE AS
(
SELECT
CustomerID,
PurchaseDate,
ROW_NUMBER() OVER (ORDER BY PurchaseDate) AS Seq
FROM #Purchases
)
SELECT
p.CustomerID,
p.PurchaseDate,
pl.PurchaseDate,
DATEADD(SECOND,DATEDIFF(SECOND, pl.PurchaseDate,p.PurchaseDate),0) AS DiffDT,
DATEDIFF(HOUR, pl.PurchaseDate,p.PurchaseDate) HourDiff
FROM CTE AS p
LEFT OUTER JOIN CTE AS pl ON pl.Seq = p.Seq - 1 -- Last batch
ORDER BY p.PurchaseDate
Try This Query...
;with cte as
(select row_number() over(order by (select 100)) Id.Customer_ID,PurchaseDatetime from Table)
select a.Customer_ID,b.PurchaseDatetime-a.PurchaseDatetime from cte a inner join cte b on a.id=b.id-1
Thanks
SELECT *,
PURCHASEDATETIME =
CASE CUSTOMER_ID
WHEN CUSTOMER_ID THEN
DATEDIFF(HH, LAG(PURCHASEDATETIME, 1) OVER(ORDER BY CUSTOMER_ID, PURCHASEDATETIME), PURCHASEDATETIME)
ELSE
NULL
END
FROM table
I have a Table in which there are multiple columns but I need to find the Difference Between Current Row of StartTime Column And Previous Row of EndTime Column.
Example is the Following output.
Batch Number Start Time End Time Difference
100004 8:00:00 8:03:30
100005 8:05:00 8:07:00 00:01:30
100006 8:08:40 8:15:00 00:01:40
32141 8:18:00 8:22:45 00:03:00
84230 8:25:10 8:33:42 00:02:25
23444 8:40:00 8:43:00 00:06:18
100001 8:50:00 8:52:00 00:07:00
I am new to SQL and am using SQL SERVER 2008 R2.
Please help me to get the output in Simple Select Query.
CREATE TABLE #Batches
(
BatchID INT,
StartTime Datetime,
EndTime Datetime,
)
INSERT INTO #Batches
VALUES
(100004,'2016-05-16 08:00:00','2016-05-16 08:03:30'),
(100005,'2016-05-16 08:05:00','2016-05-16 08:07:00'),
(100006,'2016-05-16 08:08:40','2016-05-16 08:15:00'),
(32141 ,'2016-05-16 08:18:00','2016-05-16 08:22:45'),
(84230 ,'2016-05-16 08:25:10','2016-05-16 08:33:42'),
(23444 ,'2016-05-16 08:40:00','2016-05-16 08:43:00'),
(100001,'2016-05-16 08:50:00','2016-05-16 08:52:00')
;WITH CTE AS
(
SELECT
BatchID,
StartTime,
EndTime,
ROW_NUMBER() OVER (ORDER BY StartTime) AS Seq
FROM #Batches
)
SELECT
b.BatchID,
b.StartTime,
b.EndTime,
CONVERT(VARCHAR(20), DATEADD(SECOND,DATEDIFF(SECOND, bl.EndTime,b.StartTime),0),108) AS Diff,
DATEADD(SECOND,DATEDIFF(SECOND, bl.EndTime,b.StartTime),0) AS DiffDT
FROM CTE AS b
LEFT OUTER JOIN CTE AS bl ON bl.Seq = b.Seq - 1 -- Last batch
ORDER BY b.StartTime
I follow this link http://blog.sqlauthority.com/2011/11/24/sql-server-solution-to-puzzle-simulate-lead-and-lag-without-using-sql-server-2012-analytic-function/
This will give you the difference in seconds.
;with cteMain as (
select *, ROW_NUMBER() over (order by Start_time) sn
from table)
select m.batch_number, sLag.End_date, m.Start_time, convert(varchar,DateAdd(Second,DATEDIFF(SECOND, sLag.End_date, m.Start_time),0),108) as time_diff
from cteMain as m LEFT OUTER JOIN cteMain AS sLag ON sLag.sn = m.sn-1
order by m.batch_number
How do you create a moving average in SQL?
Current table:
Date Clicks
2012-05-01 2,230
2012-05-02 3,150
2012-05-03 5,520
2012-05-04 1,330
2012-05-05 2,260
2012-05-06 3,540
2012-05-07 2,330
Desired table or output:
Date Clicks 3 day Moving Average
2012-05-01 2,230
2012-05-02 3,150
2012-05-03 5,520 4,360
2012-05-04 1,330 3,330
2012-05-05 2,260 3,120
2012-05-06 3,540 3,320
2012-05-07 2,330 3,010
This is an Evergreen Joe Celko question.
I ignore which DBMS platform is used. But in any case Joe was able to answer more than 10 years ago with standard SQL.
Joe Celko SQL Puzzles and Answers citation:
"That last update attempt suggests that we could use the predicate to
construct a query that would give us a moving average:"
SELECT S1.sample_time, AVG(S2.load) AS avg_prev_hour_load
FROM Samples AS S1, Samples AS S2
WHERE S2.sample_time
BETWEEN (S1.sample_time - INTERVAL 1 HOUR)
AND S1.sample_time
GROUP BY S1.sample_time;
Is the extra column or the query approach better? The query is
technically better because the UPDATE approach will denormalize the
database. However, if the historical data being recorded is not going
to change and computing the moving average is expensive, you might
consider using the column approach.
MS SQL Example:
CREATE TABLE #TestDW
( Date1 datetime,
LoadValue Numeric(13,6)
);
INSERT INTO #TestDW VALUES('2012-06-09' , '3.540' );
INSERT INTO #TestDW VALUES('2012-06-08' , '2.260' );
INSERT INTO #TestDW VALUES('2012-06-07' , '1.330' );
INSERT INTO #TestDW VALUES('2012-06-06' , '5.520' );
INSERT INTO #TestDW VALUES('2012-06-05' , '3.150' );
INSERT INTO #TestDW VALUES('2012-06-04' , '2.230' );
SQL Puzzle query:
SELECT S1.date1, AVG(S2.LoadValue) AS avg_prev_3_days
FROM #TestDW AS S1, #TestDW AS S2
WHERE S2.date1
BETWEEN DATEADD(d, -2, S1.date1 )
AND S1.date1
GROUP BY S1.date1
order by 1;
One way to do this is to join on the same table a few times.
select
(Current.Clicks
+ isnull(P1.Clicks, 0)
+ isnull(P2.Clicks, 0)
+ isnull(P3.Clicks, 0)) / 4 as MovingAvg3
from
MyTable as Current
left join MyTable as P1 on P1.Date = DateAdd(day, -1, Current.Date)
left join MyTable as P2 on P2.Date = DateAdd(day, -2, Current.Date)
left join MyTable as P3 on P3.Date = DateAdd(day, -3, Current.Date)
Adjust the DateAdd component of the ON-Clauses to match whether you want your moving average to be strictly from the past-through-now or days-ago through days-ahead.
This works nicely for situations where you need a moving average over only a few data points.
This is not an optimal solution for moving averages with more than a few data points.
select t2.date, round(sum(ct.clicks)/3) as avg_clicks
from
(select date from clickstable) as t2,
(select date, clicks from clickstable) as ct
where datediff(t2.date, ct.date) between 0 and 2
group by t2.date
Example here.
Obviously you can change the interval to whatever you need. You could also use count() instead of a magic number to make it easier to change, but that will also slow it down.
General template for rolling averages that scales well for large data sets
WITH moving_avg AS (
SELECT 0 AS [lag] UNION ALL
SELECT 1 AS [lag] UNION ALL
SELECT 2 AS [lag] UNION ALL
SELECT 3 AS [lag] --ETC
)
SELECT
DATEADD(day,[lag],[date]) AS [reference_date],
[otherkey1],[otherkey2],[otherkey3],
AVG([value1]) AS [avg_value1],
AVG([value2]) AS [avg_value2]
FROM [data_table]
CROSS JOIN moving_avg
GROUP BY [otherkey1],[otherkey2],[otherkey3],DATEADD(day,[lag],[date])
ORDER BY [otherkey1],[otherkey2],[otherkey3],[reference_date];
And for weighted rolling averages:
WITH weighted_avg AS (
SELECT 0 AS [lag], 1.0 AS [weight] UNION ALL
SELECT 1 AS [lag], 0.6 AS [weight] UNION ALL
SELECT 2 AS [lag], 0.3 AS [weight] UNION ALL
SELECT 3 AS [lag], 0.1 AS [weight] --ETC
)
SELECT
DATEADD(day,[lag],[date]) AS [reference_date],
[otherkey1],[otherkey2],[otherkey3],
AVG([value1] * [weight]) / AVG([weight]) AS [wavg_value1],
AVG([value2] * [weight]) / AVG([weight]) AS [wavg_value2]
FROM [data_table]
CROSS JOIN weighted_avg
GROUP BY [otherkey1],[otherkey2],[otherkey3],DATEADD(day,[lag],[date])
ORDER BY [otherkey1],[otherkey2],[otherkey3],[reference_date];
select *
, (select avg(c2.clicks) from #clicks_table c2
where c2.date between dateadd(dd, -2, c1.date) and c1.date) mov_avg
from #clicks_table c1
Use a different join predicate:
SELECT current.date
,avg(periods.clicks)
FROM current left outer join current as periods
ON current.date BETWEEN dateadd(d,-2, periods.date) AND periods.date
GROUP BY current.date HAVING COUNT(*) >= 3
The having statement will prevent any dates without at least N values from being returned.
assume x is the value to be averaged and xDate is the date value:
SELECT avg(x) from myTable WHERE xDate BETWEEN dateadd(d, -2, xDate) and xDate
In hive, maybe you could try
select date, clicks, avg(clicks) over (order by date rows between 2 preceding and current row) as moving_avg from clicktable;
For the purpose, I'd like to create an auxiliary/dimensional date table like
create table date_dim(date date, date_1 date, dates_2 date, dates_3 dates ...)
while date is the key, date_1 for this day, date_2 contains this day and the day before; date_3...
Then you can do the equal join in hive.
Using a view like:
select date, date from date_dim
union all
select date, date_add(date, -1) from date_dim
union all
select date, date_add(date, -2) from date_dim
union all
select date, date_add(date, -3) from date_dim
NOTE: THIS IS NOT AN ANSWER but an enhanced code sample of Diego Scaravaggi's answer. I am posting it as answer as the comment section is insufficient. Note that I have parameter-ized the period for Moving aveage.
declare #p int = 3
declare #t table(d int, bal float)
insert into #t values
(1,94),
(2,99),
(3,76),
(4,74),
(5,48),
(6,55),
(7,90),
(8,77),
(9,16),
(10,19),
(11,66),
(12,47)
select a.d, avg(b.bal)
from
#t a
left join #t b on b.d between a.d-(#p-1) and a.d
group by a.d
--#p1 is period of moving average, #01 is offset
declare #p1 as int
declare #o1 as int
set #p1 = 5;
set #o1 = 3;
with np as(
select *, rank() over(partition by cmdty, tenor order by markdt) as r
from p_prices p1
where
1=1
)
, x1 as (
select s1.*, avg(s2.val) as avgval from np s1
inner join np s2
on s1.cmdty = s2.cmdty and s1.tenor = s2.tenor
and s2.r between s1.r - (#p1 - 1) - (#o1) and s1.r - (#o1)
group by s1.cmdty, s1.tenor, s1.markdt, s1.val, s1.r
)
I'm not sure that your expected result (output) shows classic "simple moving (rolling) average" for 3 days. Because, for example, the first triple of numbers by definition gives:
ThreeDaysMovingAverage = (2.230 + 3.150 + 5.520) / 3 = 3.6333333
but you expect 4.360 and it's confusing.
Nevertheless, I suggest the following solution, which uses window-function AVG. This approach is much more efficient (clear and less resource-intensive) than SELF-JOIN introduced in other answers (and I'm surprised that no one has given a better solution).
-- Oracle-SQL dialect
with
data_table as (
select date '2012-05-01' AS dt, 2.230 AS clicks from dual union all
select date '2012-05-02' AS dt, 3.150 AS clicks from dual union all
select date '2012-05-03' AS dt, 5.520 AS clicks from dual union all
select date '2012-05-04' AS dt, 1.330 AS clicks from dual union all
select date '2012-05-05' AS dt, 2.260 AS clicks from dual union all
select date '2012-05-06' AS dt, 3.540 AS clicks from dual union all
select date '2012-05-07' AS dt, 2.330 AS clicks from dual
),
param as (select 3 days from dual)
select
dt AS "Date",
clicks AS "Clicks",
case when rownum >= p.days then
avg(clicks) over (order by dt
rows between p.days - 1 preceding and current row)
end
AS "3 day Moving Average"
from data_table t, param p;
You see that AVG is wrapped with case when rownum >= p.days then to force NULLs in first rows, where "3 day Moving Average" is meaningless.
We can apply Joe Celko's "dirty" left outer join method (as cited above by Diego Scaravaggi) to answer the question as it was asked.
declare #ClicksTable table ([Date] date, Clicks int)
insert into #ClicksTable
select '2012-05-01', 2230 union all
select '2012-05-02', 3150 union all
select '2012-05-03', 5520 union all
select '2012-05-04', 1330 union all
select '2012-05-05', 2260 union all
select '2012-05-06', 3540 union all
select '2012-05-07', 2330
This query:
SELECT
T1.[Date],
T1.Clicks,
-- AVG ignores NULL values so we have to explicitly NULLify
-- the days when we don't have a full 3-day sample
CASE WHEN count(T2.[Date]) < 3 THEN NULL
ELSE AVG(T2.Clicks)
END AS [3-Day Moving Average]
FROM #ClicksTable T1
LEFT OUTER JOIN #ClicksTable T2
ON T2.[Date] BETWEEN DATEADD(d, -2, T1.[Date]) AND T1.[Date]
GROUP BY T1.[Date]
Generates the requested output:
Date Clicks 3-Day Moving Average
2012-05-01 2,230
2012-05-02 3,150
2012-05-03 5,520 4,360
2012-05-04 1,330 3,330
2012-05-05 2,260 3,120
2012-05-06 3,540 3,320
2012-05-07 2,330 3,010