Here's the edited version:
I have this sample table using powerbi where %Currnt is a measure and the rest is column. I also have filters where i just selected area=asia and salesoffice=australia and fyear =2018. I have to sum up %currnt column(measure) and place it under ExpectedResult column.
But as you can see in my current resultset, when i sum the %Current seems like its it returns its average (0.485) rather than the sum (0.97). I need to get the sum of the %currnt first. My expected result is 0.97 per row
Does anyone of you encounter the same problem? Let me know if you find any solution. Appreciate your help. Thanks
FYear FMonth CurrentAR TotalAR CountFY %Currnt ExpectedResult**
2018 August, 2018 18,000.00 25,000.00 2 0.72 0.97
2018 July, 2018 33,000.00 130,000.00 2 0.25 0.97
**0.485**
not sure I have understand your problem, but you have to consider that, in DAX, when creating a measure, the context of the query is everything.
I mean that if you create a measure to count or calculate something, DAX apply it the current context of the data, ie if you apply filters, these filter is applied to the context. So maybe, you should consider create measure that is context free for the calculation of the MAX CountFY column using CALCULATION DAX formula.
Let me know if this helps. If you want to go further can you post and sample data to investigate deeper on your problem?
Related
I'm trying to figure out a solution to my problem. Basically we get a monthly report with about 3000 records and there's a bunch of reporting that is done on that, and there are calculations based on various columns. e.g.
Date
Total usage
Recommended reduction
Product
01.01.2022
1000
500
A
01.01.2022
1300
70
B
01.01.2022
2000
900
C
...
...
...
At the end of it Power BI kindly sums up the columns which is great, but now what I am trying to do is take the sum of these columns and store them in a summary table so that it would be something like this so that I could use it for a time series visual
Month
Sum Total Usage
Sum Recommended Reduction
January
59720
12040
February
81020
20580
...
...
...
I have no idea how to go about doing this. Is this the right way to go ? Or is there a way to create a visual without having to create a summary table ? I'm at a bit of a loss, so any suggestions would be really appreciated.
You don't need any DAX calculations for that. Simply pull your data onto the fields of a line chart visual like shown below. Note that you have to drill-down from Year to Month to actually see the lines.
I'm new to tableau. my question is, is DAX from Power BI same as LOD in Tableau?
It'd be great if someone could help me with an explanation for this.
thanks in advance!
There are similarities for sure. In Excel it can be a way to display a value that is unaffected by filtering.
LODs are similar in that you can fix your value.
For example, if I want to know the total sales in a workbook regardless of what row/column the value is on, I could use a FIXED LOD expression:
{FIXED : SUM([Sales]}
If you put the above on Label and a Month dimension on rows, it will ignore the segmentation of months and display the total sum of sales for the entire workbook.
You can choose to fix on a different level of detail to achieve a different result:
{FIXED [Month] : SUM([Sales]}
If you put the above on Label and a month and date dimension on rows it will display the total sum of sales for each month, and ignore the date. And so on...
They're somewhat tricky to get used to, but they come up a lot in more complex workbooks.
I would put it this way, the DAX language is at least as powerful as Tableau expressions (involving LOD or otherwise). That is, if you can do it with Tableau LOD, then there's also a way to write something equivalent with DAX.
I'm using Power Bi desktop to create a sales dashboard, and ran into a small code issue.
I need to calculate average sales in a period of working days, and tried:
TOTALYTD([Sales];
dimCalendar[Date];
dimCalendar[WorkingDays]<=16)
16 is the related working day for 22APR2019 in Brazil.
After that I plan to divide it by a month count or another similar method.
The hard coded 16 works very well, however when I try to use another formula instead:
TOTALYTD([Sales];
dimCalendar[Date];
dimCalendar[WorkingDays]=CALCULATE(MAX(dimCalendar[WorkingDays]);
LASTDATE(dimCalendar[Date])
))
It gives an error that I can't use calculate in a true/false expression.
I tried to use calculated column, but doesn't work as well.
Do you have any idea of how I can create this measure?
I'm a bit new to the DAX/Power BI, so i dont have broad understanding of it.
I have table 'Sales' as follows:
ItemID SalesValue Amount actionSales Date
1 0.5 2 0 2018-02-18
1 0.3 5 1 2018-02-18
1 0.5 6 0 2018-02-19
-I left out some fileds as description, brand, etc...
-actionSales field is a bit, that represent if sold item was on a discount.
I have made few measures that have total salesValue, total salesValueOnActionSales, percentOfActionSales, etc...
Within visuals in Power BI I can filter values if they are greater or lower then some value.
Then i figured that it would be nice to show only items that have share of action sales greater then 'parameter' (Parametr is a whole number, and it is in visual with a slider) that I made on a report.
After some time, I'm no closer to a solution. Can this be made? if it can, can someone please point me to a direction on how to do this or share some insights on the matter.
Cheers
This can be done with the 'What If' parameter in PowerBI. In short: you define a parameter, connect it to a slicer and use the parameter in a measure.
Link to MSDN Docs
Can you not add the actionSales column to a slicer and select Greater than or equal to from the options?
If this doesn't work let me know and I have another solution that might do it.
I am attempting to calculate the most recent 6-Month STDEVX.P (not including the current month; so in May 2017, I'd like to the STDEVX.P for periods Nov 2016 - Apr 2017) for sales by product in order to further calculate variation in sales orders.
The Sales Data is made up of daily transactions so it contains transaction date: iContractsChargebacks[TransactionDate] and units sold: iContractsChargebacks[ChargebackUnits], but if there are no sales in a given period, then there will be no data for that month.
So, for example, on July 1st, sales for the past 6 months were the following:
Jan 100
Feb 125
Apr 140
May 125
Jun 130
March is missing because there were no sales. So, when I calculate STDEVX.P on the data set, it is calculating it over 5 periods, when in fact there were 6, just one happens to be zero.
At the end of the day, I need to calculate STDEVX.P for the current six month period. If when pulling the monthly sales numbers, it only comes back with 3 periods(months), then it needs to assume the other 3 periods with a zero value.
I thought about manually calculating standard deviation instead of using the DAX STDEVX.P formula and found these 2 links as a reference on how to do so, the first being closest to my need:
https://community.powerbi.com/t5/Desktop/Problem-with-STDEV/td-p/19731
Calculating the standard deviation from columns of values and frequencies in Power BI...
I attempted to make a go of it, but still am not getting the correct calculation. My code is:
STDEVX2 =
var Averageprice=[6M Sales]
var months=6
return
SQRT(
DIVIDE(SUMX(
FILTER(ALL(DimDate),
DimDate[Month ID]<=(MAX(DimDate[Month ID])-1) &&
DimDate[Month ID]>=(MAX(DimDate[Month ID])-6)
),
(iContractsChargebacks[SumOfOrderQuantity]-Averageprice)^2),
months
)
)
*note: Instead of using date parameters in the code, I created a calculated column in the date table that gives each Month a unique ID, makes it easier for me.
Your question would definitely be easier to answer with more explanation regarding your model. E.g. how you defined [SumOfOrderQuantity] and [6M Sales], since a mistake there could definitely impact the final result. Also, knowing what the result you're seeing is vs. the result you expect would be helpful (using sample data).
My guess, however, is that your DimDate table is a standard date table (with one row per date), but you want standard deviation by month.
The FILTER statement in your formula limits the date range to the prior 6 full months correctly, but it will still have one row per date. You can confirm this in Power BI by going into the Data View, selecting 'New Table' under Modeling on the ribbon, and putting your FILTER statement in:
Table = FILTER(ALL(DimDate),
DimDate[MonthID]<=(MAX(DimDate[MonthID])-1) &&
DimDate[MonthID]>=(MAX(DimDate[MonthID])-6))
Assuming you have more than one day of sales for a given month, calculating the variance by day rather than by month is going to mess things up.
What I'd suggest trying:
Table = FILTER(SUMMARIZE(ALL(DimDate),[MonthID]),
DimDate[MonthID]<=(MAX(DimDate[MonthID])-1) &&
DimDate[MonthID]>=(MAX(DimDate[MonthID])-6))
The additional SUMMARIZE statement means that you only get one row for each MonthID, rather than 1 row for each date. If your [6M Sales] is the monthly average across all 6 months, and [SumOfOrderQuantity] is the monthly sum for each month, then you should be set to go calculating the variance, squaring, dividing by 6, and square rooting.
If you need to do further troubleshooting, remember you can put a table on your canvas with MonthID, SumOfOrderQuantity and [6M Sales] and compare the numbers you expect at each stage of the calculation with the numbers you're seeing.
Hope this helps.
I was facing a similar problem while trying to calculate the coefficient of variation (Std. /Mean) by SKUS from sales data. I could use the Pivot-Unpivot function in Power Query editor to to do away with the problem of months with missing sales:
1) Export the data with any calculated columns
2) Reimport the data so that the calculated columns are also available in the power query editor
3) Pivoted the data by months
4) Replaced null values with 0s
5) Unpivoted the data
6) Close and apply the query
7) Add a calculated column for the coefficient of variation using the formula
CV = CALCULATE(STDEV.P(Table1[Value]),ALLEXCEPT(Table1,Table1[Product]))/CALCULATE(AVERAGE(Table1[Value]),ALLEXCEPT(Table1,Table1[Product]))
Thus zero sales for the missing months will also be considered both for Standard Deviation and Mean.