Forum Discussion
Count and Conditional formatting by cell values
Anonymous
Hi again,
I apologise upfront since I hadn't considered the impact of 0 values, except for the visual you requested to show the count of names with 0 in any week. And of course, it also has an impact (potentially) in the monthly calculations. A big oversight of mine.
I have therefore re-worked through most of the measures to include names with 0 bookings in the overall calculations, including the count of those below the expected target (I have highlighted them in the tables). I'm afraid you will have to labour through the changes to adapt them to your exact model. Also please check out the filters applied to each visual in the filter pane.
There is one imortant point about the calculations. The model presently only considers 0 values if the value in the table is an actual 0. If there is no data present for a particular week, it does not compute that particluar User ID.
So based on the sample data there are 3 Users with 0 values included.
On the other hand, Richard ID 10 only appears in week 1 2018 (there are no values in 2017. Therefore there no records or listings for this user prior to 2018 Week 1).
In effect, the interpretation is that if there is no value, that user wasn't operating. IF that user was operating, we would expect at least a 0 as a value. Does that make sense?
I do have another version which will compute a 0 value for all IDs when there are no records, but this would deliver data potentially for IDs who have not even started operating. The ideal situation would of course be to have an operating start/end week/year for each ID (HR table) which we can then use to filter appropriately. For the time being, I've assumed that if there is no data for a given User ID, that user was not operating neither bookings nor target).
If you would like the version which computes all User IDs in all periods, do let me know and i'll post it for you.
The easiest way to control this phenomenom is if there are fields in the HR Table which establish an activity start or end year/week. These time frames can then be used to filter the data accordingly. Just bear this in mind.
Anyway, here are some resulting screenshots:
And here is the link to the file:
Many Thanks Paul, really appreciate your reply, exactly this is the view I'm after. The solution you provide is working perfectly fine on small datasets, but when I start applying to large datasets and connecting with HR datasets, some of the views are throwing error. Maybe I assume the in my earlier dataset I combine the table for convenience purposes, which might be the case and so when i flatten the table, relationship not working. Again this is my fault for not normalising the file.
For e.g. following error is visible like:-
- The sum monthly target hours coming the same for all the countries.
- The other issue we have with the same name but these different people with a unique ID (for e.g. Richard with ID 10 - newly added), due to which it is showing Many to Many relationships
- In future the HR data is coming from HR Software, my worry is if we flatten the table and create individual tables for Name, Product, Country, what if we have to add new rows how that new data sync with these individual table
I've recreated the datasets again. Any help will be grateful. I've attached the new datasets with 3 tables
- Main Table (User_ID replaced the Name) - Coming from Time tracking software
| USER_ID | ProjectCode | BookingYear | BookingMonth | BookingWeek | Booked_Hours | Customer | |
| 1 | C | 2017 | 12 | 52 | 240 | GEO | |
| 2 | C | 2017 | 12 | 52 | 120 | GEO | |
| 3 | M | 2017 | 12 | 52 | 510 | KLL | |
| 4 | M | 2017 | 12 | 52 | 15 | KLL | |
| 5 | K | 2017 | 12 | 52 | 60 | FOX | |
| 6 | K | 2017 | 12 | 52 | 30 | FOX | |
| 7 | K | 2017 | 12 | 52 | 72 | HTT | |
| 8 | U | 2017 | 12 | 52 | 6 | KBA | |
| 9 | U | 2017 | 12 | 52 | 510 | KBC | |
| 2 | C | 2018 | 1 | 1 | 0 | GEO | |
| 1 | C | 2018 | 1 | 1 | 24 | GEO | |
| 3 | M | 2018 | 1 | 1 | 24 | KLL | |
| 4 | M | 2018 | 1 | 1 | 0 | KLL | |
| 5 | K | 2018 | 1 | 1 | 24 | FOX | |
| 6 | K | 2018 | 1 | 1 | 18 | FOX | |
| 7 | K | 2018 | 1 | 1 | 43 | HTT | |
| 8 | U | 2018 | 1 | 1 | 111 | KBA | |
| 9 | U | 2018 | 1 | 1 | 0 | KBC | |
| 10 | C | 2018 | 1 | 1 | 100 | GEO |
2. CountryHour (No changes) Standalone
| COUNTRY | Base Hours | Expected Weekly Hours | Working Days | Target Hours |
| US | 8 | 40 | 22 | 176 |
| UK | 8 | 40 | 22 | 176 |
| CHINA | 8 | 40 | 23 | 184 |
| MEXICO | 7.5 | 37.5 | 21 | 157.5 |
3. HR Data (Added a new name with Unique ID) Coming from HR software
| Name | USER_ID | Manager | Country |
| Peter | 1 | Jerry | CHINA |
| Sodhi | 2 | Jerry | CHINA |
| Sodhi | 2 | Jerry | CHINA |
| Peter | 1 | Jerry | CHINA |
| Richard | 10 | Jerry | CHINA |
| Emmanuel | 3 | Naple | MEXICO |
| Philip | 4 | Naple | MEXICO |
| Emmanuel | 3 | Naple | MEXICO |
| Philip | 4 | Naple | MEXICO |
| Henry | 5 | Simon | UK |
| Mary | 6 | Simon | UK |
| Richard | 7 | Simon | UK |
| Henry | 5 | Simon | UK |
| Mary | 6 | Simon | UK |
| Richard | 7 | Simon | UK |
| Patel | 8 | Martin | US |
| John | 9 | Martin | US |
| Patel | 8 | Martin | US |
| John | 9 | Martin | US |
Hi,
What exact result are you expecting?
Thanks Ashish
What I am trying to achieve based on these three datasets is how many employees each week and each month cover the set listed below requirements.
1. The number of people booking time (Full) - Need to know the list of names [in a table] and total count where their working hours were above the target hours by country and product code. For ex. The US the target hours was 176 mentioned in the reference table
2. Who has not booked any time for a week - Need to know the list of names [in a table] and count where their working hours was "0" by country and product code3. Who has not booked full time for a month - Need to know the list of names [in a table] and count where their working hours was less than target monthly hours by country and product code. For ex. The US the target hour was 176
4. Booking status (traffic light) for each individual (fully/partially/not) - Set conditional formatting by highlighting [GREEN] who has achieved the target and above 80%, partial [YELLOW] between [79% to 30%] and lastly [RED] not meeting the target [between 29% to 0%]Appreciate your help.Many ThanksAnonymous
I see, yes, if the structure is different, we need to tweak the model and a couple of measures. See if this works for you:
First the structure of the model with the new HR Table.
Please note that I have converted the HR Table into a lookup table (no duplicate values) on import.
And with a couple of tweaks to the measures to adjust for the new table, data structure etc, we get the following.
Answer question 1: People above the target
Answer question 2: people booking 0 hours (please note that newcomer Richard has no data for 2017 (ie no value at all, either 0 or otherwise - it looks like he joined in 2018?)
Answer Question 3: not booked full time in a month
Answer question 4: Traffic Light Table
And here is the PBIX file:
Thanks Paul once again for your swift reply. Seems all working, except my Sum Monthly Target hours column which is giving wrong results (attached), all the values are coming same. I checked the relationship, they seem okay (attached below). Don't know what went wrong.
What I'm doing is replicating your model with my Org data. Since the time tracking data is connected with Power BI real-time, so can't tweak, Two other files imported into PBI - HRData and Country Hour. I'm currently focusing on metrics by Name and Country, not by ProjectCode and Customer(which in my CDM is BookingKey & Summary). DateTable is there, not currently using, instead, I'm using the Booking hours and Booking month, Week from my HoursPax table.
Don't know where I'm going wrong?