I have a measure that is comprised of other measures. I will post below pictures. I am calculating the forecast for the month per location. I created a measure for that and the total is correct for that. I created another measure that calculates the working days in a month and when I divide by this measure, I get the correct results per the location but the total is wrong.
This measure shows the measure I am using to calculate the total forecast for the month per location.
"MPP = (CALCULATE(SUM('TF'[ Forecast ]), DATESBETWEEN ('TF'[Date],[Month Start-pp], [Month end_pp] )))"
This measure shows the measure I am using to calculate the working days in a month.
"Days per location =
CALCULATE (
COUNTROWS ( 'TF' ),
FILTER (
'TF',
'TF'[Date].[MonthNo] = MONTH ( TODAY () )
)
)"
The pic is showing the totaling. The first column is the measure without the division and is correct totaling. The second column is where the issue is.
Related
I have made an measure for showing total sales by each person. I can't figure how to make an measure for dividing total sales by each person BY total sales. For example, for the person "bmo" I want to calculate 303/3153 and so on.
I have tried using the same measure as I use on "Totalt antall salg" and use the Show value as, but that just shows the correct value, and does not calculate it, and I need to use the number for later.
You can ignore filter context by using ALL to get total sales:
VAR _TotalSales =
CALCULATE (
COUNTROWS ( 'KasseJournal' ),
FILTER ( ALL ( 'KasseJournal' ),
'KasseJournal'[Hendelse] = "Bekreftet kasseekspedisjon"
)
)
RETURN DIVIDE ( [Totalt antall salg], _TotalSales, 0 )
I have a table like in the attached image:
As you can see, I have power consumptions of houses by hour.
What I would like to do is create a measure to calculate the average of power consumption by day and other measure to sum this average of power consumption by days depending on a time interval introduced in a filter.
I know it could be easy by don't know how to start with this.
Thanks for your help!
Regards
Start by adding Calculated columns for Date and Time only:
Date = DATEVALUE(FORMAT('Table'[Datetime], "YYYY-MM-DD"))
Time = TIMEVALUE(FORMAT('Table'[Datetime], "hh:mm"))
With this you create a new Calculated table for consumption per day
Consumption per day =
SUMMARIZE(
'Table',
'Table'[Date],
"Avg. Consumption", AVERAGE('Table'[Consumption])
)
and consumption per day for a given time interval
Consumption per day and time interval =
CALCULATETABLE(
SUMMARIZE(
'Table',
'Table'[Date],
"Avg. Consumption", AVERAGE('Table'[Consumption])
),
'Table'[Time] >= TIMEVALUE("00:00"),
'Table'[Time] <= TIMEVALUE("01:00")
)
i have a problem regarding my calculation. I want to visualize the running total over time (for example 01.07 after hours rounded to the nearest half hour to reduce computing power). I have 90 days of data in my dataset. The Problem i have is that the calculation for the dates long time ago (for example 01.12.2021) are super fast and the calculation for recent dates (05.07.2022) are super slow. I think that the measure calculates the running total for the whole column and then the date filter will be applied, which caused the extreme long calculation time for recent dates. Every day has the same amount of rows. Any idea how i can tell the measure to calculate the running total only for the selected date/date which i picked in my filter?
I used the following measures:
Average Sales =
'Measure Table X'[RT Sales] / 'Measure Table X'[RT Rows]
RT Sales = CALCULATE(
Sum('X'[Sales]),
'X'[Time rounded] <= Max('X'[Time rounded]))
RT Rows = CALCULATE(
COUNTA('X'[Time rounded]),
'X'[Time rounded] <= Max('X'[Time rounded]))```
I'm not sure what you are going to achive, but you can try this, this can work faster.
Average Sales =
CALCULATE(
DIVIDE('Measure Table X'[RT Sales] , 'Measure Table X'[RT Rows])
'X'[Time rounded] <= Max('X'[Time rounded]
)
RT Sales = Sum('X'[Sales])
RT Rows =COUNTA('X'[Time rounded])
I have daily sales data and I would like to create a measure or a column that calculates the monthly sales trend based on the average daily units sold in the month.
For example, as of 08/17/2017, we have had 12 working days this month and 360 units sold. This is an average of 30 units per day. So to calculate the trend manually, I would take 30 units/day and multiply it by the 23 total working days in the month for a trend of 690 units sold.
Here is some sample data.
EDIT: Adjusted the explanation and measure definitions based on the new info that was added to the post
You can create three measures to help you reaching your final result. In this example I'm merely splitting the measure into three parts to increase the readability. You can put it all together in a single measure if you like.
The first measure gives you the number of units sold. This is just a simple sum of units sold, since you have already added a month filter to your measures in your sample:
Sales:=SUM('SalesTable'[Items Sold])
The second measure gives you the number of working days for the filtered month up until the latest entry in your Sales Table:
WorkingDaysToDate:=
CALCULATE(
COUNTROWS('DateTable'),
FILTER(
'DateTable',
'DateTable'[Date] > CALCULATE(MAX('SalesTable'[Date]))
&& [Type] = 'Work Day'
)
)
The third measure gives you the total number of working days for the filtered month:
WorkingDays:=
CALCULATE(
COUNTROWS('DateTable'),
FILTER(
'DateTable',
[Type] = 'Work Day'
)
)
In the end you can combine the three measures to get your month trend:
MonthTrend:=DIVIDE([Sales], [WorkingDaysToDate]) * [WorkingDays]
Total Month To Date Sales
TotalMTDSales = SUM('Sales Table'[Items Sold])
Total Month To Date Work Days
TotalMTDWorkDays = CALCULATE(
COUNT('DateTable'[Date]),
FILTER(ALLSELECTED('DateTable'), 'DateTable'[Date]<=MAX('SalesTable'[Date]) &&
'DateTable'[Type] = "Work Day"))
Total Work Days
TotalWorkDays = CALCULATE(
COUNTROWS('DateTable'),
FILTER(
'DateTable',
'DateTable'[Type]= "Work Day"))
Trend = ([TotalMTDSales] / [TotalMTDWorkDays] ) * [TotalWorkDays]
Filter by Month
I have following scenario which has been simplified a little:
Costs fact table:
date, project_key, costs €
Project dimension:
project_key, name, starting date, ending date
Date dimension:
date, years, months, weeks, etc
I would need to create a measure which would tell project duration of days using starting and ending dates from project dimension. The first challenge is that there isn't transactions for all days in the fact table. Project starting date might be 1st of January but first cost transaction is on fact table like 15th on January. So we still need to calculate the days between starting and ending date if on filter context.
So the second challenge is the filter context. User might want to view only February. So it project starting date is 1.6.2016 and ending date is 1.11.2016 and user wants to view only September it should display only 30 days.
The third challenge is to view days for multiple projects. So if user selects only single day it should view count for all of the projects in progress.
I'm thankful for any help which could lead towards the solution. So don't hesitate to ask more details if needed.
edit: Here is a picture to explain this better:
Update 7.2.2017
Still trying to create a single measure for this solution. Measure which user could use with only dates, projects or as it is. Separate calculated column for ongoing project counts per day would be easy solution but it would only filter by date table.
Update 9.2.2017
Thank you all for your efforts. As an end result I'm confident that calculations not based on fact table are quite tricky. For this specific case I ended up doing new table with CROSS JOIN on dates and project ids to fulfill all requirements. One option also was to add starting and ending dates as own lines to fact table with zero costs. The real solution also have more dimensions we need to take into consideration.
To get the expected result you have to create a calculated column and a measure, the calculated column lets count the number of projects in dates where projects were executed and the measure to count the number of days elapsed from [starting_date] and [ending_date] in each project taking in account filters.
The calculated column have to be created in the dim_date table using this expression:
Count of Projects =
SUMX (
FILTER (
project_dim,
[starting_date] <= EARLIER ( date_dim[date] )
&& [ending_date] >= EARLIER ( date_dim[date] )
),
1
)
The measure should be created in the project_dim table using this expression:
Duration (Days) =
DATEDIFF (
MAX ( MIN ( [starting_date] ), MIN ( date_dim[date] ) ),
MIN ( MAX ( [ending_date] ), MAX ( date_dim[date] ) ),
DAY
)
+ 1
The result you will get is something like this:
And this if you filter the week using an slicer or a filter on dim_date table
Update
Support for SSAS 2014 - DATEDIFF() is available in SSAS 2016.
First of all, it is important you realize you are measuring two different things but you want only one measure visible to your users. In the first Expected result you want to get the number of projects running in each date while in the Expected results 2 and 3 (in the OP) you want the days elapsed in each project taking in account filters on date_dim.
You can create a measure to wrap both measures in one and use HASONEFILTER to determine the context where each measure should run. Before continue with the wrapping measure check the below measure that replaces the measure posted above using DATEDIFF function which doesn't work in your environment.
After creating the previous calculated column that is required to determine the number of projects in each date, create a measure called Duration Measure, this measure won't be used by your users but lets us calculate the final measure.
Duration Measure = SUMX(FILTER (
date_dim,
date_dim[date] >= MIN ( project_dim[starting_date] )
&& date_dim[date] <= MAX ( project_dim[ending_date] )
),1
)
Now the final measure which your users should interact can be written like this:
Duration (Days) =
IF (
HASONEFILTER ( date_dim[date] ),
SUM ( date_dim[Count of Projects] ),
[Duration Measure]
)
This measure will determine the context and will return the right measure for the given context. So you can add the same measure for both tables and it will return the desired result.
Despite this solution is demonstrated in Power BI it works in Power Pivot too.
First I would create 2 relationships:
project_dim[project_key] => costs_fact[project_key]
date_dim[date] => costs_fact[date]
The Costs measure would be just: SUM ( costs_fact[costs] )
The Duration (days) measure needs a CALCULATE to change the filter context on the Date dimension. This is effectively calculating a relationship between project_dim and date_dim on the fly, based on the selected rows from both tables.
Duration (days) =
CALCULATE (
COUNTROWS ( date_dim ),
FILTER (
date_dim,
date_dim[date] >= MIN ( project_dim[starting_date] )
&& date_dim[date] <= MAX ( project_dim[ending_date] )
)
)
I suggest you to separate the measure Duration (days) into different calculated column/measure as they don't actually have the same meaning under different contexts.
First of all, create a one-to-many relationship between dates/costs and projects/costs. (Note the single cross filter direction or the filter context will be wrongly applied during calculation)
For the Expected result 1, I've created a calculated column in the date dimension called Project (days). It counts how many projects are in progress for a given day.
Project (days) =
COUNTROWS(
FILTER(
projects,
dates[date] >= projects[starting_date] &&
dates[date] <= projects[ending_date]
)
)
P.S. If you want to have aggregated results on weekly/monthly basis, you can further create a measure and aggregate Project (days).
For Expected result 2 and 3, the measure Duration (days) is as follows:
Duration (days) =
COUNTROWS(
FILTER(
dates,
dates[date] >= FIRSTDATE(projects[starting_date]) &&
dates[date] <= FIRSTDATE(projects[ending_date])
)
)
The result will be as expected: