i would like to display the comparison of these two cumulative values in a graph in power bi, however the one line always breaks when the value of the table is at max. Meaning if the value of table 1 is at 9:30 and the value of the right table is at 12 o'clock, the table should take the time value from table right but keep the date. The date on the left is one week ago today, on the right is from today
Related
I just want to select a range of days in a slicer and show in a table the number of days for each month/period (month-year).
I used DAX to create a table with the information I need and I don't have problems with the periods (first column), it changes dinamically, the problem is the column "Days" (second column) because it's always showing the total number of days for each month.
Here my DAX code
SelectedPeriods = GROUPBY(DimDate;DimDate[Period];"Days";COUNTX(CURRENTGROUP();DimDate[DateKey]))
Here the result
What I expect is:
2 for april, 31 for may, 1 for june
This is an issue with execution order.
SelectedPeriods = GROUPBY(DimDate;DimDate[Period];"Days";COUNTX(CURRENTGROUP();DimDate[DateKey]))
Generates a calculated table. These are calculated when the data model is refreshed and stored in it. They are not refreshed each time a connected dimension is changed within a dashboard.
In your case, while changing date filters may hide rows from this table the number of days remains fixed at the number calculated initially when there was no filter context on the data i.e. counting all days in the month.
If you want the result to change then you need to use a measure instead of a calculated table. Measures react to the current filter context within the report and so will adjust their output each time a slicer is changed.
The needed measure will depend on your model but might be something as simple as:
CountOfDays := CountRows(DimDate)
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.
I have a table in Power BI, where I have two columns like Date and Daily Targets. Daily Targets are always same on the same date so I need a measure to only SUM 1 value for 1 date instead of calculating every row because these two columns contains duplicate values. Please see at attached screenshot for the data table.
As you look into my data, there are two distinct dates and all I need is when I add this Daily Target Column in any visualization, instead of adding 11653+11653+11653 for 3rd Jan, it should only Sum 11653 for 3rd Jan. Please help me with it, I will be very grateful to you.
To get a measure that takes the maximum value of the Daily Target by date, you can do something like this:
Daily Target = SUMX(GROUPBY(Table1, Table1[Date], "Max Daily Target", MAXX(CURRENTGROUP(), [DailyTarget])), [Max Daily Target])
Assuming your table is called Table1
The inner GROUP BY says to identify the highest daily target for each date. This assumes any given date will only have a single daily target (you could equally pick the MIN or AVG as they should all result in the same number). Note, if you have a single date with 2 different daily targets, this formula will fall down because it will only pick the biggest.
The outer SUMX sums each day's biggest daily target. This is important if you are aggregating by month or year. At the end of January, you want to have up to 31 daily targets added together.
Note: In general, I would roll up the daily target by day before loading the data into Power BI. It's not fully clear from your screenshot why you have records at a lower granularity, so I can't explain how I'd do it in your particular case. However, this post by DAXPatterns.com does go into how to handle "sales vs. budget", which may be relevant to you: http://www.daxpatterns.com/handling-different-granularities/
Need help in Data Analytics Calculations.
Currently, I am getting historical data for consumption as follows:
on above data, I am adding custom columns for calculating exact consumption(gallons) in no. of days. like:
Now, I have to plot month wise bar chart for consumption of respective Meter ID in 2016 year. But problem here is, I will have to calculate Every months consumption by dividing it in days in each respective month of 2016, and then only I will able to plot them monthly like:
y axis = consumption in every month
x axis = Jan Feb March Apr May Jun Jul Aug Sep Oct Nov Dec
so, in jan month, consumption should be = 10 + 100 + ((115/38) * 7) gallons
Notes: here, in ((115/38) * 7) : we are calculating avg consumption of single day 7 days in Jan and whole march and then getting last 7 day consumption of Jan so that we can add it in calculation of total consumption of Jan month
but how to add measure/custom column/new table for these calcualtions?
Thanks
What you need to do is relatively complicated, but the summary of my solution is:
Calculate the per-day consumption
Calculate the start and end date of each reading (e.g. the previous reading date plus one day, and the reading date)
Expand your data to have 1 row per day rather than 1-row per reading
You want to do these steps before you load the data into your data model (i.e. in your source system, or as the data is loaded using the Query Editor/Power Query).
Below, I assume you're using the Query Editor/Power Query. However, if you can use your source system, it's often the better choice (since the source system may be a database that is vastly faster than your desktop).
Note that your No. of Days calculation doesn't make sense to me. There are more than 38 days between 24 Jan 2016 and 31 Mar 2016. There are also more than 13 days between 10 Jan and 24 Jan. For this reason, it was difficult to tell whether you wanted a new reading to count on the day the previous reading was taken, or on the next full day. I assume the former. Also note, I've proceeded on the basis that your No. of Days calculation is correct
Calculate the Per Day Consumption
This is the easiest step, given that you have already calculated the Consumption and the No. of Days. Just divide one by the other. In the Query Editor, you can click in the Consumption (gallons) column and select Add Column > Standard > Divide. Under Value, choose Use values in a column and then select the No. of Days column.
Calculate the Start & End Date of Each Reading
The date of the reading is the end date, so you can rename Date to be End Date (since a reading is applied retroactively).
For the start date, in the Query Editor, you will need to add an index column (Add Column > Index Column). You will want to make sure your data is sorted by Meter ID and Date Ascending before doing this. Call the column Index.
Next, Add Column > Custom Column and pull the reading date from the prior row. Call the new column Previous End Date for now.
// A try is necessary because we can't get the previous row if there is no previous row (we'll get an error, which we can handle in the 'otherwise' block)
try
if
// See if the previous row is for the same Meter ID
[Meter ID] = #"Reordered Columns"{[Index] - 1}[Meter ID]
then
// If it is, grab the Reading Date from the previous row
#"Reordered Columns"{[Index]-1}[End Date]
else
// If this is the first reading for a meter, calculate the Start Date by subtracting the No. of Days from the End Date
Date.AddDays([End Date], -[No. of Days])
otherwise
// If this is the first row in the table, also calculate the Start Date by subtracting the No. of Days from the End Date
Date.AddDays([End Date], -[No. of Days])
Next, you'll want to add 1 to the Start Date, as we want the reading to apply to the day after the previous reading, not on the day of the previous reading.
Note, if you want the reading date to count in the prior period, subtract 1 from the End Date rather than add 1 to the start date (previous end date).
Expand your data to have 1 row per day
At this point, you should have a Meter ID, Start Date, End Date, and per day consumption column that reflects what you expect (i.e. the per day consumption is correct for the date range).
The final step is to duplicate each row for each date in the date range. There are several solutions to this outlined in this thread (https://community.powerbi.com/t5/Desktop/Convert-date-ranges-into-list-of-dates/td-p/129418), but personally, I recommend the technique (and video) posted by MarcelBeug (https://youtu.be/QSXzhb-EwHM).
You should end up with something more like this (after some removing & renaming of columns):
Finally
Now that you have one row per meter & date, with a per day consumption already calculated, you can build a visual. For example, you could do a column chart with Date on the Axis, and Consumption per Day as the value. By default, Power BI will recognize that Date is a date, and will roll it up by Year-Quarter-Month-Day. Press the little 'x' by Year and Quarter, and you'll have a chart that sums up the per day consumption by month. You can also drill down to individual date.
Further Reading
Reading a value from a previous row in Power Query
If Statements in Power Query
The AddDays function in Power Query
Adding Comments in Power Query
Catching Errors in Power Query
Converting a date range into a list of dates (Marcel Beug's solution)
A similar problem I previously answered
I have a line graph in PowerBI and in my date dimension I have the Week Number for every date (note that this is a custom week number with the week starting on Friday).
Whenever I put it on a the x-axis, PowerBI groups all the weeks together, regarless of year... so Week 1 of year 2015 will be grouped together with Week 1 of 2016...
I think to myself: "Ok, no problem, I'll just add the Year after every week number so I'll have 1-2016, 2-2016, and so on."
Well PowerBI sees this concatenation as a string value so when I put that on the graph, it goes
1-2016, 1-2017, 2-2016, 2-2017, 3-2016, 4-2016, and so on....
I've tried sorting the new column by the old week number column, but it does the same thing. Any suggestions on how to accomplish this?
You're on the right track. I recommend a separate (hidden) sort column that sorts alphabetically (i.e. year first, then 2 digit week). In other words, 1-2016 = 201601.
This way, all the weeks for 2016 sort before the weeks for 2017, and the weeks sort in the right order too. (A 1 digit week would mean 20161 will be followed by 201610, which you don't want either.)