I have a table with the customer name and its product code. I want to count the number of products having its name as "NA" on a customer level using only measures as I don't have access to create new columns.
Customer Name Product Code
---------------------------
Customer 1 NA
Customer 1 NA
Customer 1 999
Customer 2 888
Customer 2 777
Customer 3 NA
Customer 3 666
Customer 3 NA
Customer 4 5
Output should be something like this:
Customer Name Product Code
---------------------------
Customer 1 2
Customer 2 0
Customer 3 2
Customer 4 0
In this case, it sounds like you want to leverage the calculate function which is used modify calculation context.
The example below, the count of your Product Code, is being modified so that it only is done on rows in which the product code is NA.
NA count:= CALCULATE ( COUNT ( table[ Product Code ] ) , table[ Product Code ] = "NA" )
Calculate is a powerful function, since you can use it to both restrict or expand how the calculation works based on what filter conditions you provide.
You can do it in various ways, here are a couple:
The first like Marcus suggested:
NA Count :=
CALCULATE (
COUNT ( 'Table'[Product Code] ),
KEEPFILTERS ( FILTER ( 'Table', 'Table'[Product Code] = "NA" ) )
)
Or, Using SUMMARIZECOLUMNS, which will create a table:
NA COUNT :=
SUMMARIZECOLUMNS (
'Table'[Customer Name],
"NA Count", CALCULATE (
COUNT ( 'Table'[Product Code] ),
KEEPFILTERS ( FILTER ( 'Table', 'Table'[Product Code] = "NA" ) )
)
)
But since you cant create calculated columns, I am assuming you wont be able to create a new table either :) Good luck and have fun!
Related
Hi I have a data table in powerbi structured
id date data
1 2022-10-30 123
1 2022-11-01 130
1 2022-11-30 456
the data spans multiple user ids and multiple years and it the values are cumulative (like minutes on a phone plan for instance). This is not the real data
I want to add up the end of month data. In the ideal case, my table would be complete and 2022-10-31 would exist for instance, then I could do
Measure =
CALCULATE(
SUM( 'Table'[data] ),
'Table'[dates] = EOMONTH( 'Table'[dates],0 )
)
This returns 456 but I want 579 (123+456). So i cannot use EOMONTH
I think the answer is some combination of the dax above and
FILTER( Table, Table[date] = MAX( Table[date] ) )
Though if I paste that in solo, it grabs the actual latest date only, not all monthly latest dates
Also I will use slicers on user ID's in case that changes the DAX
Please use this measure to get what you need:
Measure_ =
VAR TblSummary =
ADDCOLUMNS ( YoursTable, "EOM", CALCULATE ( ENDOFMONTH ( YoursTable[date] ) ) )
RETURN
SUMX ( TblSummary, IF ( [EOM] = [date], [data] ) )
If we test our above measure on a table visual:
I have two tables:
user table (contains: user registration data. columns: user_id, create_date)
customer order table (contains: history of orders. columns: user_id, order_date, order_id)
*user and customer aren't the same. when a user registers his first order, he becomes a customer.
For each month of each year, I want the accumulative count of distinct users and the accumulative count of the distinct customers because at last, I want to calculate the ratio of the accumulative count of the distinct customers to the accumulative count of the distinct users for each month.
I don't know how can I calculate the accumulative values and the Ratio that I said, using DAX.
Note that if a customer registers more than one order in a month, I want to count him just once for that month and if he registers a new order in the next months, also I count him in each new month.
Maybe these pictures help you to understand my question better.
-I don't count_of_users and count_of_customers columns in my tables. I should calculate them.
the user table:
user_id
create_date
1
2017-12-03
2
2018-01-01
3
2018-01-01
4
2018-02-04
5
2018-03-10
6
2018-04-07
7
2018-04-08
8
2018-09-12
9
2018-10-02
10
2018-10-02
11
2018-10-09
12
2018-10-11
13
2018-10-12
14
2018-10-12
15
2018-10-20
the customer order table:
user_id
order_date
order_id
1
2018-03-28
120
1
2018-03-28
514
1
2018-03-30
426
2
2018-02-11
125
2
2018-03-01
547
3
2018-02-10
588
3
2018-04-03
111
4
2018-02-10
697
5
2018-04-02
403
5
2018-04-05
321
6
2018-04-09
909
11
2018-10-25
8401
You need a few building blocks for this. Here is the data model I used:
<edit>
I see user_id in the different tables are not the same, in that case you can omit the relationship between the tables and the two relationships from the Calendar table will both be active - with no need to change the relationship semantics in the count_of_customer measure. </edit>
The calendar table is important because we can't rely on one single date column to aggregate data from different tables, so we create a common calendar table with this sample DAX code:
Calendar =
ADDCOLUMNS (
CALENDARAUTO () ,
"Year" , YEAR ( [Date] ) ,
"Month" , FORMAT ( [Date] , "MMM" ) ,
"Month-Year" , FORMAT ( [Date] , "MMM")&"-"&YEAR ( [Date] ) ,
"YearMonthNo" , YEAR ( [Date] ) * 12 + MONTH ( [Date] ) - 1
)
Make sure to sort the Month-Year column by the YearMonthNo column so your tables look nice:
Set your relationships as shown with the active relationship from Calendar to user - if not the measures will not work unless you alter the relationships accordingly in the code! In my data model the inactive relationship is between Calendar and customer order.
Next up are the measures we will use for this. First off we count the users, a simple row count:
count_of_users = COUNTROWS ( user )
Then we count distinct user ids in the order table to count customers, here we need to use the inactive relationship between Calendar and customer order and to do this we have to invoke CALCULATE:
count_of_customers =
CALCULATE (
DISTINCTCOUNT ( 'customer order'[user_id] ) ,
USERELATIONSHIP (
'Calendar'[Date] ,
'customer order'[order_date]
)
)
We can use this measure to count users cumulatively:
cumulative_users =
VAR _maxVisibleDate = MAX ( 'Calendar'[Date] )
RETURN
CALCULATE (
[count_of_users] ,
ALL ( 'Calendar' ) ,
'Calendar'[Date] <= _maxVisibleDate
)
And this measure to count cumulative customers per month:
cumulative_customers =
VAR _maxVisibleDate = MAX ( 'Calendar'[Date] )
RETURN
CALCULATE (
SUMX (
VALUES ( 'Calendar'[YearMonthNo] ) ,
[count_of_customers]
),
ALL ( 'Calendar' ) ,
'Calendar'[Date] <= _maxVisibleDate
)
Lastly we want the ratio of these last cumulative measures:
cumulative_customers/users =
DIVIDE (
[cumulative_customers] ,
[cumulative_users]
)
And here is your result:
I know that only CALCULATE can modify the filter context. However following are 2 example using VALUES and ALL.
Example 1:
Revenue =
SUMX(
Sales,
Sales[Order Quantity] * Sales[Unit Price]
)
Revenue Avg Order =
AVERAGEX(
VALUES('Sales Order'[Sales Order]),
[Revenue]
)
What is the purpose of VALUES in AVERAGEX function? Is this to add an additional filter context?
Example 2:
Product Quantity Rank =
RANKX(
ALL('Product'[Product]),
[Quantity]
)
What is the purpose of using ALL in an iterator function?
Suppose we have a table like this:
ID
Sales Order
Order Quantity
UnitID
Unit Price
1
101
10
4
39.99
2
101
15
3
24.99
3
102
5
2
15.99
4
103
5
1
14.99
5
103
10
3
24.99
Since the Sales Order column has duplicates,
Revenue Avg Order = AVERAGEX ( VALUES ( Sales[Sales Order] ), [Revenue] )
gives a different result than
Revenue Avg ID = AVERAGEX ( Sales, [Revenue] )
since the first averages over the three Sales Order values whereas the second averages over the five ID rows.
Using DISTINCT instead of VALUES would work too.
Using ALL is instead of VALUES gives the same total but ignores the local filter context from the table visual:
Revenue Avg All = AVERAGEX ( ALL ( Sales[Sales Order] ), [Revenue] )
In this context, ALL is acting as a table function that returns all of the distinct values of the column specified ignoring filter context.
I am having trouble working out a measure (Revenue) in power bi.
I have a measure which is basically counting distinct values in a table (table 1). From this column I want to multiply the distinct count to get the total price (prices are in another table).
See below for an example
Table 1
Product DistinctCount Revenue (Measure I am trying to Calculate)
A 15 45.00
B 30 60.00
Prices Table
Product Price
A 3.00
B 2.00
At the moment the Revenue is calculating based on COUNT and not DISTINCTCOUNT.
Any help would be much appreciated.
thanks!
Measures, Calculated Columns, Google
I am assuming you have a relationship set up between these two tables on [Product]. If this is the case you can do something like this to create a calculated column:
Revenue =
CALCULATE (
SUMX ( 'Table 1', 'Table 1'[DistinctCount] * RELATED ( 'Prices Table'[Price] ) )
)
If you are trying to create a table visual try the DAX below, where ID is just a transaction ID for each product in your 'Table 1':
Revenue =
VAR DistinctCountOfProductTransactions =
CALCULATE ( DISTINCTCOUNT ( 'Table'[Id] ) )
VAR Result =
CALCULATE (
DistinctCountOfProductTransactions * SUM ( Prices[Price] ),
TREATAS ( VALUES ( 'Table'[Product] ), Prices[Product] )
)
RETURN
Result
New to PowerBI, so forgive me for the description here. I'm working with a dataset of retail headcount sensors, which gives me a table of locations, timestamps, and a count of shoppers:
Room TimeStamp Count_In
123 3/13/2019 8
456 4/4/2019 9
123 3/28/2019 11
123 3/18/2019 11
456 3/22/2019 3
etc...
I'm trying to calculate a running total for each "room" over time. The overall running total column is easy:
C_In =
CALCULATE (
SUM ( Sheet1[In] ),
ALL ( Sheet1 ),
Sheet1[Time] <= EARLIER ( Sheet1[Time] )
)
But I'm unable to figure out how to add that second filter, making sure that I'm only summing for each distinct location. Help is appreciated!
Your ALL function removes all context on Sheet1, try using ALLEXCEPT to keep the row context of the Room.
C_In =
CALCULATE (
SUM ( Sheet1[In] ),
ALLEXCEPT ( Sheet1, Sheet1[Room] ),
Sheet1[Time] <= EARLIER ( Sheet1[Time] )
)