I have two cubes here, first cube that have a dimension STORE WHICH HAVE ELEMENTS SUCH AS
-branch(CONSOLIDATED)
-regency(CONSOLIDATED)
-Subdistrict(CONSOLIDATED)
-village(CONSOLIDATED)
-store(LEAF ELEMENT),
also have a measure value ( amount ), period (month and year)
and the second cube have a dimension subdistrict that consist of branch(consolidated) and subdistrict(element)
... and the problem is..
I want to copy consolidated amount of subdistrict from first cube and put it to dimension subdistrict in second cube....
Why not use rule? because STORE elements and SUBDISTRICT element are dynamic dimension so i can't mention all of store data and subdistrict data in rule.
thank you
Without rules, and if you are not using CellGetN in TurboIntegrator to get a cell's numerical value, I think the best tool you can use is CTRL+C.
P.S. You should at least tell which tool you're planning to use (Perspectives on Excel, TI Processes, etc.)
Related
I am trying to generate an Access database with information which is currently in endless sheets and tables in Excel.
I would like to know if there is any way to add a field to one table which is a calculation (average value) based on several other cells.
I need to calculate the running 6 months average value of another field which contains 1 value per month.
Hopefully the previous image shows what I mean.
What is the best approach to import this functionality into access?
You wouldn't normally store a calculated field in Access, you would run a query that provides you the calculation on the fly.
Without seeing your data structure it is impossible to tell you how to calculate the answer you need, but you would need your data correctly normalised in order to make this simple.
I have a little bit of an Excel problem and would be happy about any suggestions.
Long version: I have a dataset with raw data representing journal entries. The structure of this dataset can be seen here:
Now, what I want to achieve is to assign each row/each journal entry to a cost category (marketing, personnel, IT, depreciation, …) based on the values in the account number, type, and cost center rows, and, in a second step, break down the categories once more, eg. for labour costs, distinguish between direct and indirect labour costs.
The way my company does this right now is using an Excel sheet with several macros where the criteria are hardcoded in the VBA code to loop through the whole list, check if a row matches the criteria for a certain cost category, and if it does, copy the row to a new sheet (having one new sheet for each category), then using a second macro to break down the categories, assigning values to the “description”-column which is empty initially based on another set of criteria. Then, pivot tables are used on each of the new sub-datasets to calculate sums for each sub-category. These sums are finally used as input data for a management report (as seen in the image above) which is the ultimate goal of this whole ordeal.
Now, not only does this seem overly complicated to me and running the macros and manually adjusting the input ranges for the pivot tables takes forever, but also the criteria for allocating the costs can change quite often, and opening the VBA editor and changing the code is not really user-friendly.The initial idea was to maybe include some helper columns (one for each cost category) and somehow create an indicator variable being one of the entry falls in the respective category, and zero otherwise, and then use these columns for further calculations (e.g. for Sumifs and such).
The problem is that a) combinations of account number and type are not unique, so that one account number can go along with various types, and one type can go along with various account numbers, so the criteria can be something like C6 = 544300 OR 544700 AND D6<>110246, etc. And b) criteria can change, meaning sometimes a new account number or type is added that also has to be assigned to an already existing category such as labor costs, which would make it necessary to include that criterion in all the formulas for that particular cost category. So, is it possible to somehow create a criteria table for each category that serves as input for some sort of IF/SUMIF or lookup function?
Short version: I have a data set (can range from 5000 to up to 100000 rows, 8 columns) where I want to perform a lookup, but based on various criteria. And, in addition to that, it would be nice if the criteria could somehow be drawn from a separate list so that they can be modified fairly easily without having to change the formula itself. Is there a way to do so? Or do you think using the advanced filter might be the most suitable option?
I have 2 tables.
First table: dimensional table to show available units of cars at start of selling cycle and for how long these units will be available.
Second table: to show how many cars were sold on a given month within their "available cycle".
I'd like to compare the "selling behaviour" within each cycle. Thus, I want to display the total initial units available next to the units sold at each stage within the cycle. The second dimension works fine, but not the first one.
This is what I get:
And this the desired output (note rows 4 and 5 for available_units)
I tried the below DAX code without success:
SumAvailableUnits:=CALCULATE(SUM([available_units]),FILTER(ALL(Table1[month_within_cycle]),[month_within_cycle]>=MAX([months_available])))
First, DAX Formatter is your friend. You may like writing unreadable single line measures, but no one else likes reading them.
I've also taken the liberty of cleaning up your table names and adding fully qualified column references. (Ignoring that your dimension isn't a pure dimension, as it holds numeric values that you aggregate in a measure)
SumAvailableUnits :=
CALCULATE (
SUM ( DimCar[available_units] ),
FILTER (
ALL ( FactSale[month_within_cycle] ),
FactSale[month_within_cycle] >= MAX ( DimCar[months_available] )
)
)
And immediately we see a problem. With the fully qualified column references, it is clear that you're trying to filter the lookup table (the one side) by the base table (the many side). In Power Pivot for Excel, we do not have bi-directional relationships (though they're available in Power BI and coming for Excel 2016). Our relationships' filter context only flows from the lookup table to the base table, typically from the dimension to the fact.
Additionally, your DimCar, by holding [available_units] and [months_available] encodes an implicit assumption that a specific [car_id] can only ever refer to a single, unchanging lot. You will never see another row with [car_id] = 1. This strikes me as highly unlikely. Even if it is the case the better solution is a model change.
In general, anything that goes onto a row or column label should come from a dimension, not a fact. Similarly, anything you're aggregating in a measure should live in a fact table. The usual exception is dimension counts - either bare, or as a denominator in a measure. Following these will get you 80% of the way in terms of dimensional modeling. You can see the tables and model diagram I've ended up with in the image below.
Here are the measure definitions
SumAvailableUnits:=SUM( FactAvailability[available_units] )
SumSold:=SUM( FactSale[cars_sold] )
Here are my source tables, my model diagram with relationships, and a pivot table built from these pieces and the measures above. Note the source of [month_within_cycle] in the pivot.
Finally, you might notice that my grand total behaves in a different way than in your original. Since the values are repeated monthly, we get a much larger grand total. If you need to instead end with the sum from the latest month (which it looks like you have in your sample), you can use an alternate measure, provided below. I don't understand why this would be your desired grand total, but you can achieve it fairly easily. Personally, I'd probably blank the measure at the grand total level.
SumAvailableUnits - GrandTotal:=
SUMX(
TOPN(
1
,FactAvailability
,FactAvailability[month_within_cycle]
,0
)
,FactAvailability[available_units]
)
This uses SUMX() to step through the table provided, defined by TOPN(). TOPN() returns the first row (in this case) in FactAvailability, including ties, after sorting by [month_within_cycle], out of all rows available in the filter context. In the context of a specific month, you get all the rows associated with that month - identical to the simple sum. In the grand total context, you get the rows associated with the last month.
SUMX() iterates over that table and accumulates the values of [available_units] in a sum.
I wonder if it's possible to create a table like this:
I have calculated the equity ratio for two companies shown in the column "Calculated field 1". Now I would like to create the average and the minimum value of this column for each company! as shown in the table (red numbers).
For clarification, the column C, row C5 to C26 show the average for company 1.
The row C28 to C49 show the average for company 2.
Any ideas how to proceed this?
Average:=
AVERAGEX(
ALL(Datasrc[Year])
,[Calculated field 1] // Seriously? Come up with a better name.
)
Minimum:=
MINX(
ALL(Datasrc[Year])
,[Calculated field 1]
)
*X() functions take a table expression as their first argument. They step row by row through this table, evaluating the expression in the second argument for each row, and accumulate it. AVERAGEX() accumulates with an average, MINX() with a minimum.
ALL() returns unique elements of a table, column, or set of columns stripped of context. Since we are only calling ALL() on the [Year] column, that is the only column whose context we remove. The context from [Stock Ticker] remains in place.
These two measures are unreasonable in the grand total level.
Ninja edit: There's likely a better way to write the measures, but with no insight into what [Calculated field 1] is doing, I can't make suggestions toward that. *X() functions force single threaded evaluation in the formula engine. It's not a bad thing, but you take a performance hit over queries which can utilize only the storage engine. If you could rewrite it to a CALCULATE(SUM(),ALL(Datasrc[Year])) / ALL(Datasrc[Year]), you'd get faster evaluation for sure, but I have no clue what your data looks like.
I just started using powerpivot.
Currently I have slicers that filter based on the values of one variable, but I also want to create a pivotchart where the user can select what variable to graph. Is there a way to populate one slicer with variables from a powerpivot table?
I have a handful of variables that a user might want to look at, and I do not want to have one graph for each variable.
I hope this makes sense. Any suggestions? Thanks & Cheers.
There is a solution to this that is pretty straightforward and although it's not perfect as it doesn't work dynamically, I've used it very successfully in this kind of situation where you are talking about selecting between a well defined set of metrics.
The technique makes use of a disconnected table to drive the slicer with the final formula using the selected value to decide which measure to use.
Lets say you have 2 measures [Sales] and [Profit] you want to choose between. You create a table in your PowerPivot model called, say, 'SlicerTbl' with a single column called Measure that has 2 rows 'Sales' and 'Profit'. No relationship is required. (the table can be called whatever you like btw).
You can then write a 3rd measure that enables you to choose which measure to use:
=
IF (
HASONEVALUE ( SlicerTbl[Measure] ),
SWITCH (
VALUES ( SlicerTbl[Measure] ),
"Measure 1", [Measure 1],
"Measure 2", [Measure 2]
),
BLANK ()
)
This basically checks to see if the slicer has a single value and if so uses VALUES() to return the selected value - because of the initial clause this can only ever be a single row. This means it returns blank in the case of multiple selections.
The SWITCH() is basically an elegant IF() that is easy to scale so I don't see why you couldn't do this with 20+ measures very quickly.
Hope this helps, I created a stupidly simple example here: Example Model