How to via count of orders


please could you help me with the following task: I need to display count of orders in a chart. Imported CSV looks like:

“2022-05-14 07:17:25”,643
“2022-05-14 07:18:04”,643
“2022-05-14 17:43:35”,643
“2022-05-16 06:37:43”,645
“2022-05-16 06:38:52”,645
“2022-05-16 20:17:20”,652
“2022-05-17 06:18:35”,653

The value is the actual summary of the count. Not orders created from previous time. Also there are more values per day and report displays SUM of those values. One solution would be to filter imported CSV, and take the last value per day. But I believe there have to be possibility to handle it in EazyBI too.
Also if I would pre-process the CSV, I would get the same situation when I switch time interval to month.

I have tried to create calculated member like Sum([Measures].[Orders Count]) , but it seems to be wrong.
The example for use of Max na Min functions in documentation is very complex and I don’t understand it at all.

Thank you,
— Jaroslav

Hi @pavrda
Welcome to the eazyBI community!

This is how eazyBI works in general: it aggregates values from granular initial data to totals, not vice versa. The total values on the day level are automatically summed up for months/quarters/years. Unfortunately, I can not imagine an effective way how to import data with the current data structure (cumulative values) and dice (not sum) them in eazyBI.

Our suggestion would be to change the data structure of the source to contain only 1 row per day with a total value of this day, or, if you have more rows per day, each would contain the change since the last timestamp (so all the rows of this day would give a total sum for the day level).

For instance, the same data file could look the following:

“2022-05-14 07:17:25”,0
“2022-05-14 07:18:04”,0
“2022-05-14 17:43:35”,0
“2022-05-16 06:37:43”,2
“2022-05-16 06:38:52”,0
“2022-05-16 20:17:20”,7
“2022-05-17 06:18:35”,1

Then you would automatically get 0 for May 14, 2 for May 16 and 7 for May 17 or 10 for May 2022, cumulative values would be easily calculable. If all previous time periods are also imported, you would get total value 653.

Ilze /

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Hi Ilze,

Thank you for the explanation. I will change the feed.

— Jaroslav

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