While on a recent project I needed to build a variation of the DAX date table. In my previous post, found here Creating DAX Date Tables I was built a date table for every day of the month for multiple years. I’ve only ever needed to consume a fully populated date calendar, but in this instance because the data I was collecting was already aggregated to the first of the month I only needed a date calendar with each month’s start date. After some playing around with my previous DAX functions I think I was able to come up with an elegant solution.
Let’s get into it.
Let’s begin by making a basic table. Open Power BI Desktop, on the Modeling ribbon click New Table.
Enter the following code:
Dates =
GENERATE (
CALENDAR ( DATE ( 2017, 1, 1 ), DATE ( 2017, 12, 31 ) ),
VAR currentDay = [Date]
VAR day = DAY( currentDay )
VAR month = MONTH ( currentDay )
VAR year = YEAR ( currentDay )
RETURN ROW (
"day", day,
"month", month,
"year", year )
)
This will produce our standard table from January 1st to December 31st, 2017 with every day populated in between. In, addition we get numerical values for Day, Month and Year.
Note: This table is producing a list of dates using the Calendar DAX function then iterating row by row to calculate each numerical value for the Day, Month and Year.
Add the Table visual with the following Values from our created DAX table.
Dates Table
Note: When you drag over the field labeled Date the field will be naturally added as a Date Hierarchy. To remove the Date Hierarchy, you have to click the little drop down arrow next to the word DATE and select the word Date in the drop down menu. This will remove the Year, Quarter, Month and Day date context from the visual.
The date calendar we made has every date, but we want only the first of each month.
Lets build a new table by following the previous steps and adding the following:
Start of Month Dates =
GENERATE (
GENERATESERIES(1,12),
VAR inc = [Value]
RETURN ROW (
"date", DATE(2017,inc,1)
)
)
Add the Table visual to the report page and add the following fields:
Start of Month Date Table
Note: I already removed the Date Hierarchy using the instructions listed above in the previous note.
This new DAX Date table is first generating a list of numbers 1 to 12 one for each month. Then it iterates through the list and produces a date using the Date function were we manually provide the Year, and the day. You can see the Generate function produces a column of data called [Value]. The variable denoted by VAR inc is capturing the number for each month. So, now what if we want to produce more than one year of dates? Super simple, just change the generate series from 1 to 12 to 1 to 48. This will produce three years of dates.
Change your Start of Months Dates to the following:
Start of Month Dates =
GENERATE (
GENERATESERIES(1,48),
VAR inc = [Value]
RETURN ROW (
"date", DATE(2017,inc,1)
)
)
With one number change we can produce 4 years of dates.
Four Years of Dates
Cool, let’s go a little further. Just in case we need it we can also produce a list of dates that contain the end of the month. Add the following your Start of Month Dates with the following DAX table (don’t forget the comma on line 1 in the ROW function):
We have added a new column to note the end of each month.
Date Table with Start and End Columns
Well, thanks for following along. In my use case this start of month date table was exactly what I needed. I thought this was a handy little DAX table, and I hope you have found this helpful as well. Be sure to share this post if you found this helpful.
For those of you who work in supply chain management this tutorial will be right up your alley. In my previous job position I had a lot of interaction with our shipping department. We would look at when orders were placed from the customer, and conduct a comparison to what orders were actually shipped or cancelled prior to shipment. Our analytics team would produce reports and metrics to our customers about orders and shipment information.
In an ideal world, every product ordered on the purchase order would be shipped and some point in the future. But, as we know, in the real world this isn’t always the case. Orders get cancelled, products get re-ordered, challenges happen, and therefore we would need to track all these changes. In our shipping analytics group, the team would pull data from our shipping system with columns similar to the following:
Order Date, Ship date, Product type, and Shipped QTY
Sometimes you want to sum the data by the order date, and in other cases you want a total by the shipped date.
In this example, we will walk through making a measure that uses the DAX formula USERELATIONSHIP. To learn more about this function from the Microsoft documentation follow this link.
Open PowerBI Desktop, click the Get Data button on the Home ribbon and select Blank Query. Click Connect to open the Query Editor. On the View ribbon click the Advanced Editor button. While in the Advanced Editor paste the following code into the editor window, click Done to complete the data load.
Note: If you need some more help loading the data follow this tutorial about loading data using the Advanced Query Editor. This tutorial teaches you how to copy and paste M code into the Advanced Editor.
let
Source = Excel.Workbook(Web.Contents("https://powerbitips03.blob.core.windows.net/blobpowerbitips03/wp-content/uploads/2017/06/Clothing-Sales-Ship-Order-Dates.xlsx"), null, true),
ClothingSales_Table = Source{[Item="ClothingSales",Kind="Table"]}[Data],
#"Changed Type" = Table.TransformColumnTypes(ClothingSales_Table,{{"Order Date", type date}, {"Ship Date", type date}, {"Category", type text}, {"Sales", Int64.Type}})
in
#"Changed Type"
Your loaded data should look like the following:
Sales Data Load
Click Close & Apply on the Home ribbon to load the data into the data model.
We will want to create two measures, one that performs a calculation on the Order Date column, and one on the Ship Date. To do this we need a date table to populate all the dates needed for this data set.
We can do this by creating a DAX date table.
On the Modeling ribbon click New Table.
New DAX Table
In the formula bar enter the following.
DateList =
GENERATE (
CALENDAR ( DATE ( 2012, 1, 1 ), DATE ( 2017, 12, 31 ) ),
VAR currentDay = [Date]
VAR startYear = 2012 // we know this by looking at our data
VAR month = MONTH ( currentDay )
VAR year = YEAR ( currentDay )
RETURN ROW (
“month”, month,
“year”, year,
“month index”, INT ( ( year – startYear ) * 12 + month ),
“YearMonth”, year * 100 + month )
)
Note: This DAX formula is building a date table, for each row we are building the columns, Month, Year, Month Index, and an integer for YearMonth index. This is a simple way to repeatedly create a date calendar based on your data.
Great, we have completed the data loading. Now, we need to link the date table to the Clothing Sales data. To do this click on the Relationships button on the black navigation bar located on the left side of the screen. Then Click & Drag the Date column from the DateList table to the Order Date column of the ClothingSales table. This will create a one to one relationship link between the two tables. Note that the relationship is illustrated in a solid white line. This means it is an active relationship.
Next, drag the Date column from the DateList table to the Ship Date column of the ClothingSales table. We have made our second connection. Note that this connection has dotted white line. This means this connection is not active. Also, we can observe that the relationship between the two tables, DateList and ClothingSales is a one to many relationship. This is denoted by the * on the ClothingSales table, and the (1) one on the DateList table. The * means there are duplicate values found in the ClothingSales table. The (1) on the DateList table means in the Date column we only have unique values, no duplicates.
Note: You can edit the connections between tables by double clicking the connecting wires. This brings up the Edit Relationship dialog box which allows you to edit things like, the Cardinality, Cross Filter Direction and activating / deactivating the connection.
Once you’re done your relationships should look like the following:
Linked Tables
By default, Power BI will only allow one active connection between tables. Therefore, we have one connection active and the other has been inactivated by default. Return to the report view by clicking the Report icon on the left black navigation bar.
Report View
Now that we have completed the data modeling let’s make some visuals. We will start by making a simple table to see what the data is doing. Add the columns from the two tables, ClothingSales and DateList to a Table Visual.
Create Table Visualization
Great! Now we have the total number of sales based on the order date. We know this because it is the primary connection that we established earlier when we linked our two tables together. But, what if I wanted to know the sales that were shipped based on the Ship Date. Earlier we made this connection but it is inactive.
Here is the awesomeness! We can create a measure that calculates different results between a user specified relationship.
First, we will re-calculate the sales number that we already have in our table. On the Home ribbon click the New Measure and enter the following in the DAX formula bar:
Order Date Sales =
CALCULATE (
SUM ( ClothingSales[Sales] ),
USERELATIONSHIP ( ‘DateList ‘[Date], ClothingSales[Order Date] )
)
Note: In this DAX formula we are creating a explicit measure, meaning we are specifically telling Power BI to sum a column. An implicit calculation is what we did earlier when we added the sales column to the table.
The USERELATIONSHIP filter within the calculation forces Power BI to calculate the sum based on the dates listed in the Order Date column. To see another demo on UseRelationship you can watch this video from Curbal.
Create another measure with the following DAX formula:
Ship Date Sales =
CALCULATE (
SUM ( ClothingSales[Sales] ),
USERELATIONSHIP ( ‘DateList ‘[Date], ClothingSales[Ship Date] )
)
This time we are forcing Power BI to use the inactive relationship to calculate the sum of the sales by shipped date. Add the two new measures to our table and we now can see how the calculations differ.
Sales By Order & Ship Date
The calculated sales for the order dates match our earlier column. This is expected, and we can confirm that this calculation is working properly. The shipped date sales are now calculating a different number. In some cases, the Shipped Date Sales is lower than the orders, because in that month you took in more orders than you shipped. In other months, the Shipped Date Sales is higher than the Order Date Sales, because there were likely large shipments ordered in the prior month and shipped in a different month.
By adding a Bar Chart from the Visualizations pane, we can now see sales by order date and ship date.
Bar Chart by Year
We can even dig deeper into the data. Click the Expand button to see the data by Year and Month.
Expand Button
Well that is about it. I hope you enjoyed this tutorial about using two relationships between data tables. If you want more information about DAX check out these books that I have found extremely helpful.
