In Tableau I have a db containing records with a create date [create date]
I need to compare the number of records that came in last 7 days to the number of records that came in the last 7 days -1 .
And show the change as a percentage.
Thanks
Assuming today is August 31th, last 7 days should be Aug 24-30, excluding today.
sum(if DATEDIFF('day',[Order Date],DATE(TODAY()-1)) <= 6 then 1 end )
If "7 days -1" means last 7 days starting from one day prior to yesterday, this second range is Aug 23-29.
sum(if DATEDIFF('day',[Order Date],DATE(TODAY()-2)) <= 6 then 1 end )
According to your different needs, you can just use this formula [(A/B)-1] in order to get the change in %:
(
sum(if DATEDIFF('day',[Order Date],DATE(TODAY()-1)) <= 6 then 1 end )
/
sum(if DATEDIFF('day',[Order Date],DATE(TODAY()-2)) <= 6 then 1 end )
)
-1
Related
I have a table 'fact WorkItems', where changes made to items are saved on the day of the change ([ChangedDate]).
WorkItemId
AreaPath
ChangedDate
State
StoryPoints
IterationPath
1day
14day
Commitment
Delivered
1
A
04/01/2022
New
4
24.1
4
1
A
06/01/2022
Ready
6
24.1
6
1
A
10/01/2022
Active
6
24.1
6
1
A
12/01/2022
Testing
8
24.1
8
8
1
A
18/01/2022
Testing
2
24.1
1
A
19/01/2022
Testing
2
24.1
1
A
28/01/2022
Closed
2
24.1
2
1
A
01/02/2022
Closed
2
24.1
2
2
1
A
03/02/2022
Closed
2
24.1
I also have a table called 'dim Itrations', where cycles' start and end dates are kept.
IterationPath
StartDate
EndDate
24.1
13/01/2022
02/02/2022
24.2
03/02/2022
17/02/2022
24.3
18/02/2022
04/03/2022
I have created a measure [1day] which tells me how many [StoryPoints] had an Item on or before a cycle started
1day =
CALCULATE (
SUM( 'fact WorkItems'[StoryPoints] ),
FILTER (
'dim Iterations',
MAX('fact Workitems'[ChangedDate]) <= 'dim Iterations'[StartDate]
)
)
I have also created a measure [14day] which tells me how many [StoryPoints] had an Item by the end of the cycle once it's in "Closed" [State].
14day =
CALCULATE (
SUM( 'fact WorkItems'[StoryPoints] ),
FILTER ('fact WorkItems',
AND(
'fact Workitems'[State]="Closed",
'fact Workitems'[ChangedDate] <= MIN('dim Iterations'[EndDate])
)
))
My objective is to create two measures: [Commitment] and [Delivered] which will sum all the StoryPoints each team ([AreaPath]) had for each of the cycles exactly on the the day the cycle started and ended (the latter with status closed). I know how to "tell" Power BI to only look at the rows, where Change date = [StartDate] or [ClosedDate].
The problem is when changes to items are made before the start/end date. I'm trying to "fill in those gaps" in the table by telling Power BI to look at the last record which was changed before or on the Start/End Date and to use those values. What I'm trying to achieve is presented in the table. Any ideas?
Would you, please, help me to apply different calculations for 2 rows in power BI:
that is, to transform this table:
client_ids products purchased month
1 0 0 jan
2 1A 1 jan
2 1B 1 jan
3 0 0 jan
4 0 0 jan
5 0 0 feb
into this:
purchased jan feb
1 1
0 3 1
That is, to perform calculations:
-on purchased = 0 - count over month, client
-on purchased = 1 - count distinct over month, client
Thank you.
I used the method:
-create the reference to the main query in the query editor
-drop the column with products
-drop duplicates
But this makes downloading the report slower.
To return the expected output, you can use two steps to obtain the result from the data:
Assuming this is your table with date:
First, calculate the month different compared with today to find recently month (you can try other method depend on your data nature):
Mon Diff = (YEAR(NOW()) - YEAR(Sheet1[date])) + (MONTH(NOW()) - MONTH(Sheet1[date]))
Second, rank the recent month as current:
rank =
var ranking = RANKX(Sheet1,Sheet1[Mon Diff],,,Dense)
return
SWITCH(ranking,1,"prior",2,"current")
Third, generate distinct values from purchase column
Table = DISTINCT(Sheet1[purchased])
Fourth, calculate the frequencies of 0 & 1 in Prior Month, the same for Feb
Jan = CALCULATE(COUNT(Sheet1[rank]),Sheet1[rank]="prior",
Sheet1[purchased]=EARLIER('Table'[purchased]))
Feb = CALCULATE(COUNT(Sheet1[rank]),Sheet1[rank]="current",
Sheet1[purchased]=EARLIER('Table'[purchased]))
The New table for the infor (In Jan, purchase 2 has 2 occurrence instead of 1):
I have a dataset OvertimeHours with EMPLID, checkdate and NumberOfHours (and other fields). I need a running total NumberOfHours for each employee by checkdate. I tried using the Quick Measure option but that only allows for a single column and I have two. I do not want the measure to recalculate when filters are applied. Ultimately what I am trying to do is identify the records for the first 6 hours of overtime worked on each check so that they can get a category of OCB and all overtime over the first 6 hours is OTP and it does not have to be exact (as demonstrated in the output below). I have only been working with Power BI for about a month and this is a pretty complex (for me) formula to figure out...
EMPLID CheckDate WkDate NumberOfHours RunningTotal Category
124 1/1/19 12/20/18 5 5 OCB
124 1/1/19 12/21/18 9 14 OTP
125 1/1/19 12/20/18 3 3 OCB
125 1/1/19 12/20/18 2 5 OCB
125 1/1/19 12/22/18 2 7 OTP
124 1/15/19 1/8/19 3 3 OCB
*Edited to add the WkDate.
Edit:
I have tweaked my query so that I have the running total and a sequential counter now:
Using the first 12 records, I am looking to get the following results:
I can either do it in a query if that is the easiest way or if there is a way to use DAX in PowerBI with this dataset now that I have the sequential piece, I can do that too.
I got it in the query:
select r.CheckDate,
r.EMPLID,
case
when PayrollRunningOTHours <= 6
then PayrollRunningOTHours
else 6
end as OCBHours,
case
when PayRollRunningOTHours > 6
then PayRollRunningOTHours - 6
end as OTPHours
from #rollingtotal r
inner
join lastone l
on r.CheckDate = l.CheckDate
and r.EMPLID = l.EMPLID
and r.OTCounter = l.lastRec
order by r.emplid,
r.CheckDate,
r.OTCounter
I have a question on SAS Programming. It is about conditional sum. But it is very specific for me. Therefore, I want to ask as an example. I have the following dataset:
Group A Quantity
1 10 7
1 8 4
1 7 3
1 10 5
2 11 6
2 13 8
2 9 7
2 13 9
I want to add two more columns to this dataset. The new dataset should be:
Group A Quantity B NewColumn
1 10 7 10 12 (7+5)
1 8 4 10 12
1 7 3 10 12
1 10 5 10 12
2 13 6 13 15 (6+9)
2 10 8 13 15
2 9 7 13 15
2 13 9 13 15
So, the column B should be equal tha maximum value of each group and it is the same for all observations of each group. In this example, Group number 1 has 4 values. They are 10, 8, 7, 10. The maximum among these values is 10. Therefore, the values of the observations of the B column for the first group are all equal to 10. Maximum number for group number 2 is 13. Therefore, the values of the observations of the B column for the second group are all equal to 13.
The column C is more complicated. Its value depends on the all columns. Similiar to B column, it will be the same within group. More detailed, it is the sum of the specific observations of QUANTITIES column. These specific observations should belong to the observations that have the maximum value in each group. In our example, it is 12 for the first group. The reason is, the maximum number of first group is 10. and the quantities belong to 10 are 7 and 5. So, the sum of these is 12. For the second group it is 15. because the maximum value of the second group is 13 and the quantities belong to 13 are 6 and 9. So the sum is 15.
I hope. I can explain it. Many thanks in advance.
You can do this with proc sql:
proc sql;
select t.*, max_a as b,
(select sum(t2.quantity)
from t t2
where t2.group = t.group and t.a = max_a
) as c
from t join
(select group, max(a) as max_a
from t
group by group
) g
on t.group = g.group;
run;
If the data is coming from an underlying database, most databases support window functions which make this easier.
This is untested (I'm away from sas) and will probably have mistakes, but a triple DoW loop should work. One pass to get the max per group, second pass to get the sum, third pass to output the records. Something like:
data want ;
do until(last.group) ;
by group ;
set have ;
B=max(A,B) ;
end ;
do until(last.group) ;
set have ;
by group ;
if A = B then NewColumn = sum(NewColumn, Quantity) ;
end;
do until(last.group);
set have ;
by group;
output ;
end ;
run;
I'm looking to build flags for students who have repeated a grade, skipped a grade, or who have an unusual grade progression (e.g. 4th grade in 2008 and 7th grade in 2009). My data is unique at the student id-year-subject level and structured like this (albeit with more variables):
id year subject tested_grade
1 2011 m 10
1 2012 m 11
1 2013 m 12
2 2011 r 4
2 2012 r 7
2 2013 r 8
3 2011 m 6
3 2013 m 8
This is the code that I've used:
sort id year grade
gen repeat_flag = .
replace repeat_flag = 1 if year!=year[_n+1] & grade==grade[_n+1] ///
& subject!=subject[_n+1] & id==id[_n+1]
replace repeat_flag = 0 if repeat_flag==.
One problem is that there are a lot of students who took a test in say 6 grade, didn't take one in 7th and then took one in 8th grade. This varies across years and school districts, as certain school districts adopted tests in different years for different grade levels. My code doesn't account this.
Regardless though I think there must be more elegant ways to do this and as a side note I wanted to know if the use of indexes is appropriate for a problem like this. Thanks!
Edit
Included a sample of what my data looks like above in response to one of the comments below. If still not clear any feedback is welcomed.
What may seem anomalous are students progressing faster or more slowly in tested grade than the passage of time would imply. That's possibly just one line for the grunt work:
clear
input id year str1 subject tested_grade
1 2011 m 10
1 2012 m 11
1 2013 m 12
2 2011 r 4
2 2012 r 7
2 2013 r 8
3 2011 m 6
3 2013 m 8
end
bysort id (year) : gen flag = (tested - tested[_n-1]) - (year - year[_n-1])
list if flag != 0 & flag < . , sepby(id)
+---------------------------------------+
| id year subject tested~e flag |
|---------------------------------------|
5. | 2 2012 r 7 2 |
+---------------------------------------+