I am brand new to Spotfire (using 7.7) and am hitting a roadblock on a simple task so am hoping for some assistance.
I need a count of customers that are scheduling service dates in consecutive years.
Since users will be using different date filters I created a MaxServiceDate calculated column to give me the max date depending on the filter and from there subtract a year to give me a PreviousYear calculated column.
Then my thought is then do a distinct count of all CustomerIDs which have a ServiceDate that match the MaxServiceDate and PreviousYear. The issue I'm running into is I don't know how to group by or fix the CustomerID in order to loop through the ServiceDates and check if they have dates matching my criteria.
Thanks for any help.
Sample data:
You can create a secondary table with a pivot transformation having the max(ServiceDate) and customer ID. The join that table back with your original so that you have the maxdate for each customer on your main table. Then do a calculated column based on the Max(ServiceDate) and Service date to determine if > 365 days.
Pivot Transformation
I think a calculated column like the following would meet your needs:
If((Max(Year([ServiceDate])) over (Intersect([CustomerID])) - Min(Year([ServiceDate])) over (Intersect([CustomerID])) + 1)=UniqueCount(Year([ServiceDate])) over (Intersect([CustomerID])),"good","bad")
For each customer, take their max service date year and subtract their min service date year and add +1. This number should match the number of distinct years associated to that customer's service dates.
This will tell you if a particular customer (at least while you had that customer) had service done in consecutive years or not.
Related
I have a list of employee names on one tab and another tab with orders shipped by employees and the month they were shipped going back 12 months. I'd like to calculate the average number of products shipped per employee per month, but I need to know how many months they were here to do that. So what I'd like to do is essentially write a formula that says give me the count of the distinct number of months they've been shipping products.
Sample employee data:
And here's the sample data on the individual shipments:
So in short, I need to know that Joe Smith shipped those 250 products across 3 distinct months to see he averages 83.3 shipments per month. Again, because there are many new people who have come onboard in the last 12 months, I can't just divide them all by 12 and need to know how many months they were shipping items in.
FILTER Shipper and Month based on Shipper column with criteria Employee name. Apply UNIQUE on filtered array to get only unique values (name + month number). Use COUNT to get active months. Divide Products Shipped by it.
Result:
Average by Count of Uniques
=LET(Shippers,B2:B11,Months,C2:C11,uShippers,E2:E4,uProducts,F2:F4,
uMonths,BYROW(uShippers,LAMBDA(uShipper,
ROWS(UNIQUE(FILTER(Months,Shippers=uShipper))))),
IFERROR(uProducts/uMonths,""))
You can use this array version, which spills all the results at once:
=LET(empl, A2:A4, prods, B2:B4, shipper, B7:B16, months, C7:C16,
ux, MAP(empl, LAMBDA(e, COUNT(UNIQUE(FILTER(months, shipper=e))))), prods/ux)
Here is the output:
It is also possible not using MAP but it is a verbose solution:
=LET(empl, A2:A4, prods, B2:B4, shipper, B7:B16, months, C7:C16,
left, TRANSPOSE(N(shipper=TOROW(empl))), right, N(months=TOROW(UNIQUE(months))),
cnts, N(MMULT(left, right)>0), ux, MMULT(cnts, SEQUENCE(ROWS(cnts),,1,0)), prods/ux)
Replacing TOROW with TRANSPOSE it should work for older Excel versions.
This is how I would have done it, create a Pivot table(Insert->Pivot Table) with Months as column, Employee as row and the values as count of shipments. Once you have the Pivot table, your life becomes easier. Now you do a count of the employee row (COUNT(COL-1:COL-X)) to count the total months a particular employee showed up.You now have count of shipments and a count of months. You can calculate the average. Not sure I can think of anything else easier.
I have a pivot table that summarized data from multiple lines of hours on different accounts an employee has planned to work.
I want to have a calculated field that shows the difference between the hours the employee has planned to work and the number of hours authorized for an employee to work for the year.
The number of hours authorized is different for certain employees.
I am currently using a helper field in my pivot table and xlookup to another employee authorized hours table to add the authorized hours on each of the lines in the source data.
I can get the table to display the correct authorized hours using the "max of" but I don't know how to calculate the difference by either modifying the table, or using calculated fields.
Any ideas appreciated.
I am trying to solve this question. The question is:
A sport store target is to provide at least one employee per 10 customers and at least one administer officer per 10 customers. The store checks those figures running a report on the first Friday of the Month every 30 days.
I have added an example of the data. I am trying to identify the months during which the store failed to achieve those figures?
I would appreciate if anybody can help. please see the picture bellow.
=LET(data,FILTER(A:B,(A:A<>"")*(ROW(A:A)>1)),a,INDEX(data,,1),b,INDEX(data,,2),u,UNIQUE(a),x,--(TRANSPOSE(a)=u),mmlt1,CEILING(MMULT(x,--(b="customer")),10)/10,mmlt2,MMULT(x,--(b="SECURITY")),mmlt3,MMULT(x,--(b="administer officer")),FILTER(u,(mmlt3<mmlt1)+(mmlt3<mmlt2),"all correct"))
I took column A for WEEK_ENDING and B for ROLE
You can replace FILTER(A:B,(A:A<>"")*(ROW(A:A)>1)) with your actual data (without headers).
I than took the unique dates and calculated
the count of "customer` matching the date (mmlt1)
the count of SECURITY matching the date (mmlt2)
the count of administer officer matching the date (mmlt3)
I rounded up the customer count to the nearest 10 and then divided by 10.
Now either of the mmlt2 or mmlt3 values should be larger than the count of mmltq (round up & divided by 10) to meet the conditions.
Finally I filtered the unique dates to the conditions. The result spills the dates where either too little security was available per 10 customers, or too little administer officers.
Currently i had a project using Microsoft SQL Server Analysis Service. I found a problem regarding filtering data with excel timeline.
Here is my date dimension screenshot:
<img src="https://i.stack.imgur.com/NUr2x.png"/><img src="https://i.stack.imgur.com/5OSgA.png" />
I had a cube with 2 measures, Sales Quantity (measures) and Sales Quantity Last Year (calculation). Here is MDX expression for Sales Quantity Last Year calculation:
( ParallelPeriod([Date].[YM].[Calendar Year],1,[Date].[YM].CurrentMember),[Measures].[Sales Quantity In 1000] )
After deploying the project to my local server, the data can be shown perfectly using excel 2013:
Pic: Data in Excel without filter
The problem start when i want to filter the data using excel timeline. When i filter only '2016', my calculation measure is no longer working. You can see the data in 'Sales Quantity in 1000 LY' column is blank. It looks like that i cant see the data outside current filter (2016). Pic: Filtered using timeline filter
But when i use slicer, the data can be shown normally Pic:Filtered using Slicer
Did i make a mistake in building date dimension? Or i need to fix the MDX calculation query? Because when i test this case in Microsoft AdventureWorksDW2014 with the same date hierarchy and the same calculation, all is going well.
Your parallel period calculation looks correct assuming [Date].[YM] is your date hierarchy. I am guessing that your date dimension is off somehow.
Make sure that:
it has a hierarchy created, and the hierarchy is what you are referencing in the parallel period calculation. Here is an example, you could have more or less attributes in the hierarchy obviously.
Your attribute relationships are defined correctly.
Key columns on the attributes in the hierarchy are correct. In the example above, you would just make year the key for the year column, but then for quarter it would be a collection of the year and quarter column. For period, key columns would be year, quarter, period. For week, key columns would be year, quarter, period, week. Date would just use the date column since date is the key.
4.Make sure that the date key attribute is using a date field for it's value column, as a time slicer needs this.
define time intelligence on your date dimension. Right click on the date dimension on the solution explorer and choose add business intelligence, then on choose enhancement screen pick define dimension intelligence. Then set the attribute type for each dimension attribute. Here is how it would be for our example.
Hopefully one of these does it for you.
I'm looking a way to add an extra column in a pivot table that that averages the sum of the count for the months ("Count of records" column) within a time period that is selected (currently 2016 - one month, 2017 - full year, 2018 - 5 month). Every month would have the same number based on the year average, needs to be dynamically changing when selecting different period: full year or for example 4 months. I need the column within the pivot table, so it could be used for a future pivot chart.
I can't simply use average as all my records appear only once and I use Count to aggregate those numbers ("Count of records" column).
My current data looks like this:
The final result should look like this:
I assume that it somehow can be done with the help of "calculated filed" option but I couldn't make it work now.
Greatly appreciate any help!
Using the DataModel (built in to Excel 2013 and later) you can write really cool formulas inside PivotTables called Measures that can do this kind of thing. Take the example below:
As you can see, the Cust Count & Average field gives a count of transactions by month but also gives the average of those monthly readings for the subtotal lines (i.e. the 2017 Total and 2018 Total lines) using the below DAX formula:
=AVERAGEX(SUMMARIZE(Table1,[Customer (Month)],"x",COUNTA(Table1[Customer])),[x])
That just says "Summarize this table by count of the customer field by month, call the resulting summarization field 'x', and then give me the average of that field x".
Because DAX measures are executed within the context of the PivotTable, you get the count that you want for months, and you get the average that you want for the yearly subtotals.
Hard to explain, but demonstrates that DAX can certainly do this for you.
See my answer at the following link for an example of how to add data to the DataModel and how to subsequently write measures:
Using the Excel SMALL function with filtering criteria AND ignoring zeros
I also recommend grabbing yourself a book called Supercharge Excel when you learn to write DAX by Matt Allington, and perhaps even taking his awesome online course, because it covers this kind of thing very well, and will save you significant head-scratching compared to going it alone.