I'm trying to create a line chart visualization that shows a constantly increasing sum over time. I think this would be called a cumulative total or a running total, but most of the questions I've seen on here have been about showing a total at the bottom of a matrix, subtotals, etc. Instead, I just want to visualize the units from the previous date being added to the current date, and so on. Not sure if this would be a custom column in the dataset or if there is an existing function for this.
Any help is appreciated. Thanks!
Goto Quick Measures, in the "Calculation" dropdown, select "Totals - Running Total", pull in Base Value (what you want to aggregate) and Field (probably your calender) and here you are!
Related
I'm pretty new to Power BI. I'm unsure how to approach this.
I have one visualization that displays the ten most frequently bought products in a time frame that is set by a slicer. In another visualization, I display how those products have been selling over the past few years (this time frame is not determined by the slicer). I want to display only the ten products that come from the first visualization, not the ten most common over the time frame in the second visualization.
How can I accomplish this? The approach I have in mind (and I'm open to others) is to create a true/false column that changes with the first visualization. "True" would be for products that are frequently bought as determined by the first visualization in the slicer-determined time range, and the second visualization would only look at values with a "true" in that column. How can I create a column (or table, maybe?) that changes depending on a visualization?
Clarification: most of the pages will say Top10 ... Actually, the measure used was a simple Top5 that includes products with the same number of orders than the 5th product. Therefore, to avoid dealing with larger images, 7 products will be seen but it is a Top5 ranking. The idea is you can replace it with your custom TopN measure.
What I understood:
The simplification of your model plus the disconnected help table would be:
I have one visualization that displays the ten most frequently bought
products in a time frame that is set by a slicer.
The Date slicer belongs to the Dates table in the Data model.
The table viz represents the number of rows in the sales table in the
current context (for each product within the Date range).
The table viz is sorted according to the [#Rows] measure in descending
order.
The table viz only presents the TopN products even without the presence
of the [#Rows] measure due to the presence of the [TopOrders]
measure within Filters on this visual. [TopOrders] is 1.
On the second page you create:
A slicer with the Dates[Date] column (the same one used on the
previous page).
A matrix with Products[ProductName] on the rows, HDates[Year] on
the columns, and a measure on values.
From the View tab, you select the Sync Slicers option.
Inside the Sync Slicers pane:
In the Sync column, check the boxes related to the necessary pages.
In the Display column uncheck the box that contains the over
years report.
So far all we have done is pass the time frame context from page 1 to page 2.
Since the TopN context depends on the time frame context, we can now use the [TopOrders] measure as a Filters on this visual in the matrix. Again, [TopOrders] is 1.
Why do the numbers differ between rows and not between columns?
Also, in this example, the Sales table only has information up to 12/31/2020 but the visualization shows an additional year and the Sales[Amount] values for each order is $1 so that [#Orders] and [SalesAmount] are the same for easy comparison.
HDates is not related to the model and for each combination of HDates[Year]-Products[ProductName], the [SalesAmount] measure is using the information coming from the previously hidden slicer and the respective Products[ProductName] because the information coming from HDates[Year] has no effect yet.
In order to complete this exercise, it only remains to modify the [SalesAmount] measure in such a way that it removes the filter on the time frame (Dates[Date]) and it recognizes HDates[Year] as Dates[Year].
SalesAmount :=
CALCULATE(
SUM(Sales[Amount]),
ALL(Dates),
TREATAS(VALUES(HDates[Year]),Dates[Year])
)
And this is the final result.
I hope it works for someone or the idea can be improved.
To preface this, I'm fairly experienced in Excel and VBA but new to PowerBI and more than a bit confused.
I have a flat table with a [creationdate]-, [Prio] (Priority (1,2,3)) and a calculated [Days Open] column, among many irrelevant others. I need to create a chart that displays the average days a case was open by priority of the case.
To display the average "days required" per (opening-) month for the past 18 months, I created the following measure:
Prio 1 = CALCULATE(AVERAGE('SourceName'[Days Open]),'SourceName'[Prio]=1)
Then I used that as a value, and used the [creationdate] as the x-axis. (Later I changed the x-axis to a new date table linked to [creationdate] without it making a difference.) To display this as monthly averages, I used the hierarchy limited to years and months, and went down one level in the chart.
Something seemed off so I checked first in Excel, then in the data source in PowerBI and yep: The averages in the PowerBI chart are complete bullshit.
Where did I go wrong? I assume it has something to do with the date hierarchy... So I created a date table as recommended (which....why?!) and linked it. That didn't make a difference.
Meanwhile in the data panel if I filter by the date column and calculate the average with the filtered selection of numbers externally, everything works as expected, so its not like there's a date formatting issue.
Do I have to create a calculated column with something akin to
DATE(YEAR([DateColumn]),MONTH([DateColumn]),1)
, then use that as the x-axis without the hierarchy, and hope nobody cares about the day in the label? Or is there something wrong with the measure used? I'm completely lost.
I have the following Power BI table example for an operating expense report that uses a slicer to filter the first column named "Actual". This is to see the operating expenses for one month compared to the budget figures for the year. It also compares the year-to-date and annual figures. How can I create dynamic columns that change based on the slicer selection? These additional columns are not shown in the pic below but included in the last pic. The Budget column below was just created as an example to show what it should look like.
I set up a star schema with several tables shown below. There's only one expense fact table used and the slicer only works for the first column as previously stated but I need all the other columns to use different parameters and adjust based off what's selected in the slicer. The last image is an overview of the info and the parameters for each column. I tried creating new columns with measures for the budget to see if I can get that going but can't figure out how to make it adjust with the slicer selection.
I'm not sure if I should be using separate queries for each column or can this be done using the one expense table. Hope this isn't too confusing. Please let me know if more info is needed.
If I understood what you wanted correctly I think I solved your problem.
I was able to create the following:
I did not use all values since I did not want to type everything, if you provide some test data it is easier to replicate you dashboard.
This matrix (so not table) allows you to filter for Date (if you so desire, you can always show all date's in the matrix) Book and AccountTree.
The way this is done is by putting the address column in the ROWS for the matrix, Putting the Date column in the COLUMNS of the matrix and putting your values (actual, budget, variance) in the values of the matrix.
For the date is used days, since it was easier to type. You can always use weeks, months, quarters or years.
For this to work you have to create the following relationships:
Hope this helps.
If not, please provide test data so it is easier to try and solve your problem.
I am working on a report that has data by month. I have created a measure that will calculate a cost per unit which divides the sum of dollars by the sum of production volume for the selected month(s):
Wtd Avg = SUM('GLData - Excel'[Amount])/SUM('GLData - Excel'[Production])
This works well and gives me the weighted average that I need per report category regardless of if I have one or multiple months selected. This actual and budget data is displayed below:
If you take time to total the actual costs you get $3.180. Where I am running into trouble is a measure to sum up to that total for a visual (This visual does not total sadly). Basically I need to sum the aggregated values that we see above. If I use the Wtd Avg measure I get the average for the total data set, or .53. I have attempted another measure, but am not coming up with the correct answer:
Total Per Unit Cost = sumX('GLData - Excel','GLData - Excel'[Wtd Avg])/DISTINCTCOUNT('GLData - Excel'[Date])
We see here I return $3.186. It is close, but it is not aggregating the right way to get exactly the $3.180:
My Total Per Unit Cost formula is off. Really I am simply interested in a measure to sum the post aggregated Wtd Avg measure we see in the first graph and total to $3.180 in this example.
Here is my data table:
As you probably know already, this is happening because measures are dynamic - if you are not grouping by a dimension, they will compute based on the overall table. What you want to do is to force a grouping on your categories, and then compute the sum of the measure for each category.
There are 2 ways to do this. One way is to create a new table in Power BI (Modeling tab -> New Table), and then use a SUMMARIZE() calculation similar to this one to define that table:
SUMMARIZE('GLData - Excel',[Category],[Month],[Actual/Budget],"Wtd Avg",[Wtd Avg])
Unfortunately I do not know your exact column names, so you will need to adjust this calculation to your context. Once your new table is created, you can use the values from that table to create your aggregate visual - in order to get the slicers to work, you may need to join this new table to your original table through the "Manage Relationships" option.
The second way to do this is via the same calculation, but without having to create a new table. This may be less of a hassle. Create a measure like this:
SUMX(SUMMARIZE('GLData - Excel',[Category],[Month],[Actual/Budget],"Wtd Avg",[Wtd Avg]),[Wtd Avg])
If this does not solve your issue, go ahead and show me a screenshot of your table and I may be able to help further.
MWE set up:
1) From the Power Bi visual website: https://app.powerbi.com/visuals/ there is a custom download "Box and Whisker (Jan Pieter)"
2) Download sample.
3) create new measure with dax formula:
Distinct count score = if(distinctCOUNT(Courses[Score]) > 4, average(Courses[Score]), Blank())
4) Add a Stacked column chart with Course as the axis and the newly created 'Distinct count score' as the Value and get the following:
5) compare this graph to the Box and Whisker provided by Power BI:
Here is my problem. I only want to show values in the Box and Whisker where the Distinct Count of Scores is greater than 4 -- So I only want Physics to show up (like the stacked column chart above).
So if I try the solution working with the stacked bar chart using the Dax formula. I get the following--nothing shows up:
And this is what I want to happen:
Question:
Is there a way in Power BI run and distinct count statement within a Box and Whisker chart to only show data with > 4 distinct values (or any if statement)?
I want it to be formula based, I cannot just 'visual filter' items I do not want.
Possible answer:
I thought about going to the source code to try and 'throw in' a if statement. But I went to the developers git hub: https://github.com/liprec -- I couldn't find the repo for this visual.
Basically this is due to the way the box and whisker chart is working. The visuals needs a dataset to calculate the values (mean, median, etc.) and use those values to show the box and whisker.
So in your case you need to create a measure that is on the same level as the scores (because those values are needed) and is only available. See the screenshot for a visual explanation of the needed measure.
I created the measure with the follow DAX measure:
Filter Score = IF(CALCULATE(DISTINCTCOUNT(Courses[Score]), ALLEXCEPT(Courses, Courses[Course]))>4, MIN(Courses[Score]), BLANK())
The Boolean expression of the IF statement calculates the distinct scores per course via a CALCULATE expression and the ALLEXCEPT filter option to ignore everything but the course value. And the TRUE part returns the score which needs to be aggregated, so the MIN and the FALSE part return a BLANK() value so is can be ignored.
When you add the new measure and create a BW chart it will only show 'Physics' course results.
If you need more help, please let me know here or via email.
-JP
BTW: I just updated my PowerBI visual GitHub repository (https://github.com/liprec/PowerBI-custom-visuals) and added my box and whisker chart and my hierarchy slicer to it in the folder oldAPI.
The crux of your problem, as far as I can tell, is that you want to filter visuals to courses that have a particular number of distinct values. Which visual you want to use is almost irrelevant (though it was helpful to have a sample Power BI workbook to follow along with).
The way I'd approach this (and not saying this is the best or only way)
Step 1
Create a new Course dimension table, with one row for each unique course. In the sample workbook, you can click 'Enter Data' and manually type in the data.
Course
------
English
Math
Physics
Step 2
Next, create a calculated column in the new table and calculate the distinct count for each course. This isn't a measure - it's a column in your table, that uses the Distinct Count calculation from your question.
Distinct Count = CALCULATE(DISTINCTCOUNT(Courses[Score]), SUMMARIZE('Courses','Courses'[Course]))
The SUMMARIZE works like a GROUP BY. In essence, creating one row per course with a distinct count of scores.
Step 3
Use this new attribute as a filter on your visual. You can then dynamically alter the number of distinct values as you feel like (4, 3, 2).
I know this isn't quite as good as typing a formula into the visual filter field, though in practice it's still formula driven. The formula is just on an underlying table.
Why so complex?
The reason you have to do this for the Box & Whiskers visual, whereas your 'Distinct Count score' measure works so well, is that on the column chart, you are displaying a single value (the average score). The Box & Whiskers chart, by contrast, is plotting every individual score.
In fact, if you removed the 'Course' from the axis of your column chart, the value changes as it adds back in the courses you filtered out. (The reason for this is that, if no course is on your axis, your formula calculates the distinct count of all the courses, which is 7). Likewise, if you were to filter your column chart to a particular session, your column chart would go blank (since in any given session, no course has more than 4 distinct values).
The technique I've described above fixes those problems, because it filters out the courses Math & English from the get-go. It doesn't matter if you've filtered to a single session, or not filtered by course at all. English & Math will always be excluded as long as their distinct count is below the value you specify.
Hope this helps.