First, as I am a French guy, I want to apologise in advance for my poor English!
Despite my searches since few days, I cannot find the correct measure to solve my problem.
I think I am close to the solution, but I really need help to achieve this job!
Here is my need:
I have a dataset with a date table and a "Position" (i.e. "stock") table, which is my fact table, with date column.
Classic relationship between these 2 tables. Many Dates in "Position" table / 1 date un "Dates" table.
My "Dates" table has a one date per day (Column "AsOf")
My "Deals" table looks like this:
Id
DealId
AsOfDate
Notional
10000
1
9/1/2022
2000000
10001
1
9/1/2022
3000000
10002
1
9/1/2022
1818147
10010
4
5/31/2022
2000000
10011
4
5/31/2022
997500
10012
4
5/31/2022
1500000
10013
4
5/31/2022
1127820
10014
5
7/27/2022
140000
10015
5
7/27/2022
210000
10016
5
7/27/2022
500000
10017
5
7/27/2022
750000
10018
5
7/27/2022
625000
10019
1
8/31/2022
2000000
10020
1
8/31/2022
3000000
10021
1
8/31/2022
1801257
10022
1
8/31/2022
96976
10023
1
8/31/2022
1193365
10024
1
8/31/2022
67883
Based on a selected date (slicer with all dates from "Dates" table), I would like to calculate the sum of Last Notional for each "Deal" (column "DealId").
So, I must identify, for each Deal, the last "Asof Date" before or equal to the selected date and sum all matching rows.
Examples:
If selected date is 9/1/2022, I will see all rows, except rows asof date = 8/31/2022 for deal 1 (as the last date for this deal is 9/1/2022).
So, I expect to see:
DealId Sum of Notional
1 6 818 147
4 5 625 320
5 2 225 000
Grand Total 14 668 467
If I select 8/31/2022, total for Deal 1 changes (as we now take rows of 8/31 instead of 1/9):
DealId Sum of Notional
1 8 159 481
4 5 625 320
5 2 225 000
Grand Total 16 009 800
If I select 7/29, only deals 4 and 5 are active on this date, so the results should be:
DealId Sum of Notional
4 5 625 320
5 2 225 000
Grand Total 7 850 320
I think I found a solution for the rows, but my total is wrong (only notionals of the selected date are totalized).
I also think my measure is incorrect if I try to display the notional amounts aggregated by Rating (other column in my table) instead of deal.
Here is my measure:
Last Notional =
VAR SelectedAsOf =
SELECTEDVALUE ( Dates[AsOf] )
VAR LastAsofPerDeal =
CALCULATE (
MAX ( Deals[AsOf Date] ),
FILTER ( ALLEXCEPT ( Deals, Deals[DealId] ), Deals[AsOf Date] <= SelectedAsOf )
)
RETURN
CALCULATE (
SUM ( Deals[Notional] ),
FILTER (
ALLEXCEPT ( Deals, Deals[DealId]),
LastAsofPerDeal = Deals[AsOf Date]
)
)
I hope it is clear for you, and you will be able to find a solution for this.
Thanks in advance.
Antoine
Make sure you have no relationship between your calendar table and deals table like so.
Create a slicer with your dates table and create a table visual with deal id. Then add a measure to the table as follows:
Sum of Notional =
VAR slicer = SELECTEDVALUE(Dates[Date])
VAR tbl = FILTER(Deals,Deals[AsOfDate] <= slicer)
VAR maxBalanceDate = CALCULATE(MAX(Deals[AsOfDate]),tbl)
RETURN
CALCULATE(
SUM(Deals[Notional]),
Deals[AsOfDate] = maxBalanceDate
)
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?
I have a table with the Date, Analyst Name, and Quantity of tickets resolved. What I want to create is a visual with a stacked bar chart for every date showing the top 3 analysts with the highest quantities of tickets resolved for each day.
I have tried using the TOP N filter but it filters every analyst except the 3 with the highest number of tickets resolved overall. But I want for each date.
You could use a rank measure.
Considering the following table with sample data:
Date
Analyst Name
Quantity
2021-01-01
A
5
2021-01-01
B
10
2021-01-01
C
15
2021-01-01
D
20
2021-01-02
A
42
2021-01-02
B
32
2021-01-02
C
22
2021-01-02
D
12
2021-01-03
A
15
2021-01-03
B
5
2021-01-03
C
21
2021-01-03
D
30
You can write a measure like this:
Rank =
RANKX (
CALCULATETABLE (
'Table',
-- remove the filter an Analyst Name to only keep grouping by Date
REMOVEFILTERS ( 'Table'[Analyst Name] )
),
-- CALCULATE needed here for context transition
CALCULATE (
SUM ( 'Table'[Quantity] )
)
)
In your report just pull this measure into the filter panel and configure it like this:
The result will then look like this:
My raw data stops at sales - looking for some DAX help adding the last two as calculated columns.
customer_id order_id order_date sales total_sales_by_customer total_sales_customer_rank
------------- ---------- ------------ ------- ------------------------- ---------------------------
BM 1 9/2/2014 476 550 1
BM 2 10/27/2016 25 550 1
BM 3 9/30/2014 49 550 1
RA 4 12/18/2017 47 525 3
RA 5 9/7/2017 478 525 3
RS 6 7/5/2015 5 5 other
JH 7 5/12/2017 6 6 other
AG 8 9/7/2015 7 7 other
SP 9 5/19/2017 26 546 2
SP 10 8/16/2015 520 546 2
Lets start with total sales by customer:
total_sales_by_customer =
var custID = orders[customer_id]
return CALCULATE(SUM(orders[sales], FILTER(orders, custID = orders[customer_id]))
first we get the custID, filter the orders table on this ID and sum it together per customer.
Next the ranking:
total_sales_customer_rank =
var rankMe = RANKX(orders, orders[total_sales_by_customer],,,Dense)
return if (rankMe > 3, "other", CONVERT(rankMe, STRING))
We get the rank per cust sales (gotten from first column), if it is bigger than 3, replace by "other"
On your first question: DAX is not like a programming language. Each row is assessed individual. Lets go with your first row: your custID will be "BM".
Next we calculate the sum of all the sales. We filter the whole table on the custID and sum this together. So in the filter we have actualty only 3 rows!
This is repeated for each row, seems slow but I only told this so you can understand the result you are getting back. In reality there is clever logic to return data fast.
What you want to do "Orders[Customer ID]=Orders[Customer ID]" is not possible because your Orders[Customer ID] is within the filter and will run with the rows..
var custid = VALUES(Orders[Customer ID]) Values is returning a single column table, you can not use this in a filter because you are then comparing a cell value with a table.
I have a fact table named meetings containing the following:
- staff
- minutes
- type
I then created a summarized table with the following:
TableA =
SUMMARIZECOLUMNS (
'meetings'[staff]
, 'meetings'[type]
, "SumMinutesByStaffAndType", SUM( 'meetings'[minutes] )
)
This makes a pivot table with staff as rows and columns as types.
For this pivottable I need to calculate each cell as a percent of the column total. For each staff I need the average of their percents. There are only 5 meeting types so I need the sum of these percents divided by 5.
I don't know how to divide one number grouped by two columns by another number grouped by one column. I'm coming from the SQL world so my DAX is terrible and I'm desperate for advice.
I tried creating another summarized table to get the sum of minutes for each type.
TableB =
SUMMARIZECOLUMNS (
'meetings'[type]
, "SumMinutesByType", SUM( 'meetings'[minutes] )
)
From there I want 'TableA'[SumMinutesByStaffAndType] / 'TableB'[SumMinutesByType].
TableC =
SUMMARIZECOLUMNS (
'TableA'[staff],
'TableB'[type],
DIVIDE ( 'TableA'[SumMinutesByType], 'TableB'[SumMinutesByType]
)
"A single value for column 'Minutes' in table 'Min by Staff-Contact' cannot be determined. This can happen when a measure formula refers to a column that contains many values without specifying an aggregation such as min, max, count, or sum to get a single result."
I keep arriving at this error which leads me to believe I'm not going about this the "Power BI way".
I have tried making measures and creating matrices on the reports view. I've tried using the group by feature in the Query Editor. I even tried both measures and aggregate tables. I'm likely overcomplicating it and way off the mark so any help is greatly appreciated.
Here's an example of what I'm trying to do.
## Input/First table
staff minutes type
--------- --------- -----------
Bill 5 TELEPHONE
Bill 10 FACE2FACE
Bill 5 INDIRECT
Bill 5 EMAIL
Bill 10 OTHER
Gary 10 TELEPHONE
Gary 5 EMAIL
Gary 5 OTHER
Madison 20 FACE2FACE
Madison 5 INDIRECT
Madison 15 EMAIL
Rob 5 FACE2FACE
Rob 5 INDIRECT
Rob 20 TELEPHONE
Rob 45 FACE2FACE
## Second table with SUM of minutes, Grand Total is column total.
Row Labels EMAIL FACE2FACE INDIRECT OTHER TELEPHONE
------------- ------- ----------- ---------- ------- -----------
Bill 5 10 5 10 5
Gary 5 5 10
Madison 15 20 5
Rob 50 5 20
Grand Total 25 80 15 15 35
## Third table where each of the above cells is divided by its column total.
Row Labels EMAIL FACE2FACE INDIRECT OTHER TELEPHONE
------------- ------- ----------- ------------- ------------- -------------
Bill 0.2 0.125 0.333333333 0.666666667 0.142857143
Gary 0.2 0 0 0.333333333 0.285714286
Madison 0.6 0.25 0.333333333 0 0
Rob 0 0.625 0.333333333 0 0.571428571
Grand Total 25 80 15 15 35
## Final table with the sum of the rows in the third table divided by 5.
staff AVERAGE
--------- -------------
Bill 29.35714286
Gary 16.38095238
Madison 23.66666667
Rob 30.5952381
Please let me know if I can clarify an aspect.
You can make use of the built in functions like %Row total in Power BI, Please find the snapshot below
If this is not what you are looking for, kindly let me know (I have used your Input table)
I am trying to create a "Percent Retention" for policies during a given time period ( By month, YTD and year over year) . So all of the policies at a given time period compared to those active at the end of the period.
Policies can be:
N=New
RN=ReNew
C=Cancel
RI=ReInstate
NR=NonRenew
Transaction data kinda looks like this, the StatusNum is something I can derive to show inforce status.
PolicyID PolicyNum StatusDate Status StatusNum Net
1 123 1/1/2018 N 1 1
2 123 3/31/2018 C 0 -1
3 123 4/1/2018 RI 1 +1
4 123 6/1/2018 RN 1 0
5 222 2/1/2018 N 1 1
6 222 7/1/2018 RN 1 0
7 333 1/1/2018 N 1 1
8 333 6/1/2018 NR 0 -1
9 444 1/1/2018 N 1 1
10 444 5/30/2018 C 0 -1
My best guess on how to do this is to take the sum of the last StatusNum values at a PIT (partitioned by Policy Number) divided by the first StatusNum value at the beginning PIT. So if I filter by dates 1/1/2018 to 8/1/2018
123 will be in force (+1,+1)
222 will not be in force yet(so not counted for anything) (+0,+0)
333 was in force at the beginning, but it non renewed (+1,-1)
444 was in force at the beginning, but it cancelled (+1,-1)
So 3 of the policies were active at 1/1/2018 and 2 cancelled, 1 doesn't matter so the retention would be 33.3%
Can anyone offer feedback if this is the best way to do this and how to accomplish this?
Thank you in advance for your assistance.
Update
This is kinda what I am looking for, but it is too slow:
'AsOfPolicies =
var A= SELECTCOLUMNS(SUMMARIZECOLUMNS(Transactions[PolicyNumber], filter( Transactions,Transactions[DateKey]=min(Transactions[DateKey])&&Transactions[IsInForce]=-1) ),"aPolicyNumber", [PolicyNumber])
var B=SELECTCOLUMNS(SUMMARIZECOLUMNS(Transactions[PolicyNumber], filter( Transactions,Transactions[DateKey]<=MAX(Transactions[DateKey]) ),"MaxDate",MAX(Transactions[DateKey]) ),"bPolicyNumber",[PolicyNumber],"MaxDate",[MaxDate]) var C = SELECTCOLUMNS(filter(CROSSJOIN(A,B),[aPolicyNumber]=[bPolicyNumber]),"cPolicyNumber",[aPolicyNumber],"MaxDateKey",[MaxDate])
Var D = SELECTCOLUMNS(filter(CROSSJOIN(C,Transactions),[cPolicyNumber]=[PolicyNumber] && [MaxDateKey]=[DateKey]),"PolicyNumber",[PolicyNumber],"PD_ID",[PD_ID],"IsInForce",[IsInForce])
Return D'
Update
Also the filter does not appear to be working
I think you can do something like this:
Retention =
VAR StartDates =
SUMMARIZE (
ALLSELECTED ( PolicyLog ),
PolicyLog[PolicyNum],
"Start", MIN ( PolicyLog[StatusDate] )
)
VAR Included =
SELECTCOLUMNS (
FILTER ( StartDates, [Start] <= MIN ( Dates[Date] ) ),
"Policies", PolicyLog[PolicyNum]
)
VAR Filtered = FILTER ( PolicyLog, PolicyLog[PolicyNum] IN Included )
RETURN
DIVIDE (
SUMX ( Filtered, PolicyLog[Net] ),
COUNTROWS ( SUMMARIZE ( Filtered, PolicyLog[PolicyNum] ) )
)
First, you create a table, StartDates, that gives the earliest dates for each policy limited to the time frame you have selected. It would look something like this:
StartData =
PolicyNum Start
123 1/1/2018
222 2/1/2018
333 1/1/2018
444 1/1/2018
From there, we just want a list of which policies we want to include in the calculation. So we pick the ones that have a Start on the minimum selected date in the date slicer. We just want a list of the resulting policy numbers, so we just select that column.
Included =
Policies
123
333
444
From there we filter the whole PolicyLog table to just include these ones (Filtered).
Finally, we can add up the Net column for each of these selected policies and divide by the distinct count of them to get our retention percentage.
Edit: In response to your comment, I think you want to be a bit more selective with the StartDate variable. Instead of MIN( PolicyLog[StatusDate] ), try something more like this:
CALCULATE( MIN(PolicyLog[StatusDate]), PolicyLog[Status] IN {"N", "RN", "RI"} )