Objective:
Get the previous value based on a criteria.
Situation:
I have a table with groups numbered 1,2. I would like to look at the previous value (referring to the previous date) but for each group.
Desired Output:
My output should look like this
+------------+-------+-------+----------------+
| date | group | value | previous value |
+------------+-------+-------+----------------+
| 2019-02-02 | 2 | 50 | 45 |
| 2019-02-02 | 1 | 60 | 80 |
| 2019-01-18 | 2 | 45 | |
| 2019-01-18 | 1 | 80 | |
+------------+-------+-------+----------------+
What I tried:
previous value =
LOOKUPVALUE(
Table[value],
Table[date],
CALCULATE(
MAX(Table[date]),
FILTER(
Table,
Table[group]=EARLIER(Table[group]) && Table[date]<EARLIER(Table[date])
)
)
)
As I understand, you want this as a calculated column, not a measure. Try:
Previous Value =
VAR Current_Date = Table[date]
VAR Previous_Date =
CALCULATE (
MAX ( Table[date] ),
Table[date] < Current_Date,
ALLEXCEPT ( Table, Table[group] )
)
RETURN
CALCULATE (
MAX ( Table[value] ),
Table[Date] = Previous_Date,
ALLEXCEPT ( Table, Table[group] )
)
How it works:
We iterate each record of the Table and store its date in "Current_Date" variable.
For each record, find previous date, which is the max date that is smaller than the date of the record we are iterating. To do that, we need to have access to all dates, not only the date of the current record, so we need to use ALL function. However, since we need to do it by group, we use ALLEXCEPT, which preserves filter for the current group.
Once previous date is found, you can use exactly the same pattern to find previous value - find MAX value where record's date equals previous date, while preserving group filter.
Related
I am looking for a solution to sum previous rows in a calculated column.
Sample data:
product | sales | cumulative
000001 | 2000 | 2000
000001 | 2000 | 4000
000002 | 1500 | 1500
000001 | 2000 | 6000
000002 | 1500 | 3000
Could anyone help me with the DAX please.
This is what I would do
step 1)
I would create a new ordinal column with a sequential number for each product group
index =
RANKX (
FILTER (
Sales,
EARLIER ( Sales[Product] ) = Sales[Product]
),
Sales[Sales],
,
ASC
)
Step 2)
I create the Running Totals column that shows the running totals by group
Running Totals =
CALCULATE (
SUM (Sales[Sales]),
FILTER(
Sales,
Sales[Product] = EARLIER ( Sales[Product])
&& Sales[index] <= EARLIER ( Sales[index])
)
)
this is the result
In Power BI I would like to create a DAX measure that will retrieve the latest string value for specific IDs. Example source table:
Name_ID | Name | DateTime | Value
----------------------------------------------------------
1 | Child_1 | 18.8.2021 12:33:24 | F
32 | Parent_32 | 18.8.2021 11:41:09 | F
13 | Child_1 | 18.8.2021 11:30:58 | E
48 | Parent_48 | 18.8.2021 09:13:11 | F
2 | Child_2 | 17.8.2021 00:09:42 | S
1 | Child_1 | 17.8.2021 23:03:34 | F
48 | Parent_48 | 17.8.2021 21:46:27 | S
6 | Parent_6 | 16.8.2021 17:31:26 | S
.
.
.
specific parents IDs for example here are 6, 32 and 48, so the result should be something like this:
Name_ID | Name | DateTime (of last execution) | Value
------------------------------------------------------------------------------
32 | Parent_32 | 18.8.2021 11:41:09 | F
48 | Parent_48 | 18.8.2021 09:13:11 | F
6 | Parent_6 | 16.8.2021 17:31:26 | S
The result table I'm trying to get is only parents latest appearance and retrieving the whole row or just Value from last column.
This seems so easy in theory and on paper but I just can't seem to get it in DAX I have tried with various calculate formulas but without any result worth mentioning .
I'm beginner in Power Bi and any help would be very appreciated!
You can use a measure like this one, where we check Max Date per Name:
Flag =
var MaxDatePerName = CALCULATE(max(Sheet3[DateTime]), FILTER(ALL(Sheet3), SELECTEDVALUE(Sheet3[Name]) = Sheet3[Name]))
return
if( MaxDatePerName = SELECTEDVALUE(Sheet3[DateTime]) && LEFT(SELECTEDVALUE(Sheet3[Name]),6) = "Parent", 1, BLANK())
With RANKX
Measure2 =
VAR _0 =
MAX ( 'Table 1'[DateTime] )
VAR _00 =
MAX ( 'Table 1'[Name] )
VAR _1 =
CALCULATE (
RANKX (
FILTER ( ALL ( 'Table 1' ), 'Table 1'[Name] = _00 ),
CALCULATE ( MAX ( 'Table 1'[DateTime] ) ),
,
DESC
)
)
VAR _2 =
IF ( _1 = 1 && CONTAINSSTRING ( _00, "Parent" ) = TRUE (), _0, BLANK () )
RETURN
_2
I have this table:
Order | Total | FirstPayment | Months
1 | 1000 | 2021-01-01 | 2
2 | 600 | 2021-02-01 | 3
And I need to create a another table with the installments, like this:
Month | Order | Value
2021-01-01 | 1 | 500
2021-02-01 | 1 | 500
2021-02-01 | 2 | 200
2021-03-01 | 2 | 200
2021-04-01 | 2 | 200
So, I want to create a child table with one row for each month of payment.
Please, can you help?
As per my comment, I would actually do it like this:
Create dates table that spans all dates between two ranges. You could actually filter it to contain only relevant dates for better performance, but I didn't bother (this is a table formula):
Payments = CALENDAR(MIN(Orders[FirstPayment]), MAXX(Orders, EDATE(Orders[FirstPayment], Orders[Months])))
Create a measure that would show appropriate values for relevant dates:
Payment amount =
SUMX (
Payments,
VAR d =
DAY ( Payments[Date] )
RETURN
SUMX (
FILTER (
Orders,
DAY ( Orders[FirstPayment] )
== d
&& Payments[Date] <= EDATE ( Orders[FirstPayment], Orders[Months] -1 )
&& Payments[Date] >= Orders[FirstPayment]
),
[Total] / [Months]
)
)
The result - based on Order from Orders table and Date from Payments table:
EDIT
Of course, it is also possible to do what you asked. You have to combine the two formulas to create a calculated table like this (below is a table formula that you apply when you select New table):
Installments =
SELECTCOLUMNS (
FILTER (
CROSSJOIN (
CALENDAR (
MIN ( Orders[FirstPayment] ),
MAXX ( Orders, EDATE ( Orders[FirstPayment], Orders[Months] ) )
),
Orders
),
[Date] >= [FirstPayment]
&& DAY ( [Date] ) = DAY ( [FirstPayment] )
&& [Date]
<= EDATE ( [FirstPayment], [Months] - 1 )
),
"Date", [Date],
"Order", [Order],
"Value", [Total] / [Months]
)
Can someone help me to convert the sql string to Dax?
row_number() p over (partition by date, customer, type order by day)
The row number is my desired output.
Assuming that your data looks like this table:
Sample
+------------+----------+---------+--------+
| Date | Customer | Product | Gender |
+------------+----------+---------+--------+
| 01/01/2018 | 1234 | P2 | F |
| 01/01/2018 | 1234 | P2 | M |
| 03/01/2018 | 1235 | P1 | F |
| 03/01/2018 | 1235 | P2 | F |
+------------+----------+---------+--------+
I have created a calculated column called Rank, using the RANKX and FILTER function.
The first part of the calculation is to create variables outside the scope of the FILTER function. The second part uses RANKX that takes an expression value - in this case Gender - to order the values.
Rank =
VAR _currentdate = 'Sample'[Date]
VAR _customer = 'Sample'[Customer]
var _product = 'Sample'[Product]
return
RANKX(FILTER('Sample',
[Date]=_currentdate &&
[Customer] = _customer &&
[Product] = _product),[Gender],,ASC)
The output is
I contrasted the output to the SQL equivalent.
select
*,
row_number() over(partition by Date,Customer,Product order by Gender)
from (
select '2018-01-01' as Date,1234 as CUSTOMER,'P2' AS PRODUCT, 'M' Gender union
select '2018-01-01' as Date,1234,'P2','F' UNION
select '2018-01-03' as Date,1235,'P1','F' UNION
select '2018-01-03' as Date,1235,'P2','F'
)t1
I have a table with inventory movements. Each inventory item has a unique ID and they change status overtime (let's say status A, B, C and D, but not always in this order). Each status change of an ID is a new record in the table with the timestamp of the status change. My goal is to calculate with Power BI DAX the number of inventory at a certain day in status 'B'. The logic is to count the number of distinct IDs, which breached status 'B' before the certain day but doesn't have any newer status before that day.
Example of the source table:
ID | TimeStamp | Status
1 | 8/20/2018 | A
1 | 8/21/2018 | B
1 | 8/24/2018 | C
2 | 8/19/2018 | A
2 | 8/20/2018 | B
2 | 8/22/2018 | C
2 | 8/24/2018 | D
3 | 8/18/2018 | A
3 | 8/21/2018 | B
4 | 8/15/2018 | A
4 | 8/17/2018 | B
4 | 8/24/2018 | D
Example of the output table:
Date | Count of Items in Status B on this Day
8/17/2018 | 3
8/18/2018 | 2
8/19/2018 | 0
8/20/2018 | 8
8/21/2018 | 10
8/22/2018 | 5
8/23/2018 | 3
I was thinking of creating a table for the latest timestamp with status 'B' for each ID and then look for the next timestamp, after the timestamp of status 'B', if applicable:
ID (primary key) | TimeStamp of 'B' breached | TimeStamp of next status breach
1 | 8/20/2018 | 8/21/2018
2 | 8/18/2018 | 8/22/2018
3 | 8/21/2018 |
4 | 8/15/2018 | 8/20/2018
Then I would plug the above data into the Date context and count the number of IDs from the above table, where the "TimeStamp of 'B' breached" value is smaller AND the "TimeStamp of next status breach" value is greater than the certain date.
Unfortunately I am not sure how to plug this logic into DAX syntax, hence any recommendations would be appreciated.
Thanks a lot!
Gergő
This is a bit tricky, but we can do it with the use of a temporary calculated summary table within a measure:
CountStatusB =
SUMX(
ADDCOLUMNS(
SUMMARIZE(
FILTER(
ALL(Inventory),
Inventory[TimeStamp] <= MAX(Inventory[TimeStamp])
),
Inventory[ID],
"LastTimeStamp",
MAX(Inventory[TimeStamp])
),
"Status",
LOOKUPVALUE(Inventory[Status],
Inventory[ID], Inventory[ID],
Inventory[TimeStamp], [LastTimeStamp])
),
IF([Status] = "B",
1,
0
)
)
First, we create a summary table which calculates the last TimeStamp for each ID value. To do this, we use the SUMMARIZE function on a filtered table where we only consider dates from the current day or earlier, group by ID, and calculated the max TimeStamp.
Once we have the maximum TimeStamp per ID for the current day, we can look up what the Status is on that day and add that as a column to the summary table.
Once we know the most recent Status for each ID for the current day, we just need to sum up the ones where that Status is "B" and ignore the other ones.
It may be easier to read the measure if we break it up into steps. Here's the same logic as before, but using variables for more clarity.
CountB =
VAR CurrDay = MAX(Inventory[TimeStamp])
VAR Summary = SUMMARIZE(
FILTER(
ALL(Inventory),
Inventory[TimeStamp] <= CurrDay
),
Inventory[ID],
"LastTimeStamp",
MAX(Inventory[TimeStamp])
)
VAR LookupStatus = ADDCOLUMNS(
Summary,
"Status",
LOOKUPVALUE(Inventory[Status],
Inventory[ID], Inventory[ID],
Inventory[TimeStamp], [LastTimeStamp]
)
)
RETURN SUMX(LookupStatus, IF([Status] = "B", 1, 0))