I have two tables. One table contains two columns RepresentativeID and SalesAmount.
The second Table contains three columns RepresentativeID, BaseAmount and RateCommision.
A representative gets a commission of let say 1% all sales over 1000.
I need to compute total commission for each representative.
How can I aggregate one table first and then connect it with another?
Welcome to Stack Overflow, Iryna!
First, you need to connect your tables on RepresentativeID.
For simplicty, I will call your first table "Sales", and the second table "Rep".
Your data model then should look something like that:
Note that Rep table should be on 1 side, and Sales table on many side (1:*)
Then, in Sales table add measure:
Rep Sale = SUM(Sales[Amount])
and another measure:
Rep Commission = SUMX( Rep, ( [Rep Sale] - Rep[Base]) * Rep[Rate])
How it works:
to calculate commissions, you need to iterate by each representative. SUMX function iterates over table Rep, calculates sales for each of them, then calculates their commissions, and then sums them up.
Related
I have a table that shows the recurrence of purchasing a product, with the columns: product_id, report_date, quantity.
I need to list in a table the products that are more than 50 days unsold. The opposite I managed to do (list those that were sold in the last 50 days) but the opposite logic has not yet been able to implement.
Does anyone have any tips?
An example of the table:
product_id,date,report_date,quantity
329,2019-01-02 08:19:17,2019-01-02 14:34:12,6
243,2019-01-03 09:19:17,2019-01-03 15:34:12,6
238,2019-02-02 08:19:17,2019-03-02 14:34:12,84
170,2019-04-02 08:19:17,2019-04-02 14:34:12,84
238,2019-04-02 08:19:17,2019-04-02 14:34:12,8
238,2019-04-02 08:19:17,2019-04-02 14:34:12,100
238,2019-08-02 08:19:17,2019-08-02 14:34:12,100
238,2019-10-02 08:19:17,2019-10-02 14:34:12,100
170,2020-01-02 08:19:17,2020-01-02 14:34:12,84
170,2020-01-02 08:19:17,2020-01-02 14:34:12,84
There are many steps to do this task. I assume the date column is the one to work with. Your example from table includes duplicated entries. Is it right that at the same time the order is there twice?
So here are the steps:
At first add an calculated field date_past to your dataset:
DATE_DIFF(CURRENT_DATE(),date)
To the dataset add a filter SO_demo with:
include date_past<30
Then blend the data with it self. Use product_id as Join key. Only the 2nd dataset has the SO_demo filter. Add to the dimension of this dataset the calculated field sold_last_30_days with the formula "yes".
In the table/chart to display add a filter on the field include sold_last_30_days is Null.
I have blended 2 datasets, joining them by a couple of keys. The left dataset contains data for most of the dates, while the second one has monthly sales goals for each salesman.
So, I'll have the daily sales, which when summed up, they give me the total, but when I sum the sales goals from the right dataset, it gets repeated for each sales person occurrence in the left one, giving me the wrong result.
If I put it on a table visual and set its calculation to Average, it gives me the correct sales goal for each person, but the total is wrong and if I put it on a KPI visual, the total is also wrong.
Any help is appreciated.
Thank you!
Sorry, it is not possible to have the same field aggregated as average first and in the next step sum over these average values.
For the total of the "sales goal" can be extracted from right dataset.
If your data comes from Big Query, you could do following steps:
add an "empty" record for each month and sales person to the left dataset
join the two datasets in BigQuery
add a calculated column "goal per order", which is the "monthly sales goals" divided by the number of orders this salesmen had this month. This count number is count(orders) over (partition by salesmen, month_column)
In Data Studio the aggegated sum of the "goal per order" is the value for "goal per salesman"
I have a pivot table in Excel.
My dataset includes workforce numbers for the 32 states in the country, which are grouped in six different regions. This data set is divided into quarters since 2015 to date.
I need this table to show both the % of total workforce at a national level, and at a regional level.
I have tried using calculate, sum, allselected, but then if I use an slicer and show just one region, I end up with the % of workforce within that region and not at a national level.
I am a beginner in DAX and using Power Pivot, how can I calculate the total workforce at a national level for a given period so that it doesn't change if I change the region?
Please help!
Let's say you have a measure called "Total Workforce" (whatever it is - you haven't provided any information about its meaning).
To calculate total workforce at a national level, you need to create another measure:
National Workforce = CALCULATE( [Total Workforce], ALL(Table[Region]))
where Table is your table name, and Region is the name of the field containing regions. Function ALL in DAX removes all filters from a column, a list of columns, or an entire table.
Then, to calculate the ratio:
Workforce % of National = DIVIDE( [Total Workforce], [National Workforce])
Please note: it's very hard to answer questions like that without seeing your data, data model, formulas and the description of the desired outcome. It'd be great if you could provide this information.
I am looking to calculate in the calc script something, so I can allocate a row from a fact table to a dimension member.
The business scenario is the following. I have a fact table that record customer credit and debit ( customer can do a lot of little loan) and a dimension Customer.I want to classify my customer base on his history of credit and debit on a given period.Classification of customer change over time.
Example
The rule is, if a customer balance (for a given period ) is over - 50 000, the classification is "large", if he have more than a record and have done a payement in the last 3 month he is a "P&P.If he doesn't own any money and have done a payement in the last 3 month its "regular".
My question is more about direction than a specific code,which way is the best to implement this kind of rule ?
Best Regards
Vincent Diallo-Nort
I'd create a fact table with a balance auto-updated status every day:
check the rolling balance yesterday plus today's records.
when the balance = 0, then remove a record.
Plus add a flow fact table with payments only.
Add measures:
LastChild aggregation for the first fact table.
Sum aggregation for the second fact table.
When it's done, you may apply a MDX calculation:
case
when [Measure].[Balance] > 50000
then "Large"
when [Measure].[Payments] + ([Date].[Calendar].CurrentMember.Lag(1),[Measure].[Payments]) + ([Date].[Calendar].CurrentMember.Lag(2),[Measure].[Payments]) > 0
then "P&P"
else "Regular"
end
In order to give you answer in detail you have to provide more information about your data structure.
I'm using ssas tabular (powerpivot) and need to design a data-model and write some DAX.
I have 4 tables in my relational database-model:
Orders(order_id, order_name, order_type)
Spots (spot_id,order_id, spot_name, spot_time, spot_price)
SpotDiscount (spot_id, discount_id, discount_value)
Discounts (discount_id, discount_name)
One order can include multiple spots but one spot (spot_id 1) can only belong to one order.
One spot can include different discounts and every discount have one discount_value.
Ex:
Order_1 has spot_1 (spot_price 10), spot_2 (spot_price 20)
Spot_1 has discount_name_1(discount_value 10) and discount_name_2 (discount_value 20)
Spot_2 has discount_name_1(discount_value 15) and discount_name_3 (discount_value 30)
I need to write two measures: price(sum) and discount_value(average)
How do I correctly design a star schema with fact table (or maybe two fact tables) so that I in my powerpivot cube can get:
If i choose discount_name_1 I should get
order_1 with spot_1 and spot_2 and price on order_1 level will have value 50 and discount_value = 12,5
If I choose discount_name_3 I should get
order_1 with only spot_2 and price on order level = 20 and discount_value = 30
Fact(OrderKey, SpotKey, DiscountKey, DateKey, TimeKey Spot_Price, Discount_Value,...)
DimOrder, DimSpot, DimDiscount, etc....
TotalPrice:=
SUMX(
SUMMARIZE(
Fact
,Fact[OrderKey]
,Fact[SpotKey]
,Fact[Spot_Price]
)
,Fact[Spot_Price]
)
AverageDiscount:=
AVERAGE(Fact[Discount_Value])
Fact table is denormalized and you end up with the simplest star schema you can have.
First measure deserves some explanation. [Spot_Price] is duplicated for any spot with multiple discounts, and we would get wrong results with a simple SUM(). SUMMARIZE() does a group by on all the columns passed to it, following relationships (if necessary, we're looking at a single table here so nothing to follow).
SUMX() iterates over this table and accumulates the value of the expression in its second argument. The SUMMARIZE() has removed our duplicate [Spot_Price]s so we accumulate the unique ones (per unique combination of [OrderKey] and [SpotKey]) in a sum.
You say
One order can include multiple spots but one spot (spot_id 1) can only
belong to one order.
That's is not supported in the table definitions you give just above that statement. In the table definitions, one order has only one spot but (unless you've added a unique index to Orders on spot_id) each Spot can have multiple orders. Each Spot can also have multiple discounts.
If you want to have the relationship described in your words, the table definitions should be:
Orders(order_id, order_name, order_type)
OrderSpot(order_id, spot_id) -- with a Unique index on spot_id)
Spots (spot_id, spot_name, spot_time, price)
or:
Orders(order_id, order_name, order_type)
Spots (spot_id, spot_name, spot_time, order_id, price)
You can create the ssas cube with Order as the fact table, with one dimention in the Spot Table. If you then add the SpotDiscount and Discount tables with their relations (SpotDiscount to Spot, Discount to SpotDiscount) you have a 1 dimentional.
EDIT as per comments
Well, the Fact table would have order_id, order_name, order_type
The Dimension would be made up of the other 3 tables and have the columns you're interested in: probably spot_name, spot_time, spot_price, discount_name, discount_value.