Skip to main content
cancel
Showing results for 
Search instead for 
Did you mean: 

Find everything you need to get certified on Fabric—skills challenges, live sessions, exam prep, role guidance, and more. Get started

Reply
Gvandijk
New Member

Occupancy rate of the building

I am mapping out how the different spaces in the building are used. for this I have an export with all planned appointments per time slot. For each appointment I have a line with the room number and the start and end time of the appointment. These reservations vary from a duration of 15 minutes to 10 hours. I want to provide insight into the occupancy per room per quarter of an hour and present this in a visual. I cannot yet find an option to convert this into time slots of 15 minutes.

1 ACCEPTED SOLUTION

Hi @Gvandijk ,

In response to the data you provided further, here are my answers.

Create a table of what you are showing.

vyilongmsft_0-1708335203900.png

Create a new calculated column to calculate elapsed time.

Column = HOUR('Table'[Duration]) * 60 + MINUTE('Table'[Duration])

vyilongmsft_1-1708335475156.png

Then calculate the number of experiences every 15 minutes.

Column 2 = 'Table'[Column] / 15

vyilongmsft_2-1708335631654.png

Finally turn it into a visualization to get the results you need.

vyilongmsft_3-1708335665463.png

 

 

Best Regards

Yilong Zhou

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

 

View solution in original post

3 REPLIES 3
Gvandijk
New Member

@v-yilong-msft , thank you for your reply. I read the topic about the 15 minutes interval but the problem is that i have two colums with time (start / end) with a different duration. I woud like to visualise this data in the graph you suggested. Here is an small example of the dataset:

Gvandijk_0-1707898305137.png

 

 

Hi @Gvandijk ,

In response to the data you provided further, here are my answers.

Create a table of what you are showing.

vyilongmsft_0-1708335203900.png

Create a new calculated column to calculate elapsed time.

Column = HOUR('Table'[Duration]) * 60 + MINUTE('Table'[Duration])

vyilongmsft_1-1708335475156.png

Then calculate the number of experiences every 15 minutes.

Column 2 = 'Table'[Column] / 15

vyilongmsft_2-1708335631654.png

Finally turn it into a visualization to get the results you need.

vyilongmsft_3-1708335665463.png

 

 

Best Regards

Yilong Zhou

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

 

v-yilong-msft
Community Support
Community Support

Hi @Gvandijk ,

Based on your questions, here are my answers.

I started by creating a table based on your description.

vyilongmsft_0-1707874151774.png

Then create a new column named 15Min_Data. Here are the DAX codes:

15Min_Data = ROUNDDOWN('Table'[Time] * 24 * 60 / 15, 0) / (24 * 60 / 15)

vyilongmsft_1-1707874342383.png

Now you can create visuals based on this new column.

vyilongmsft_2-1707874429861.png

It is possible to determine the time interval based on this image, and you can also read this topic for more information: Solved: Creating 15 Minute intervals from Date and Time Co... - Microsoft Fabric Community

 

If it does not help, please provide more details with your desired output and pbix file without privacy information (or some sample data) .

How to Get Your Question Answered Quickly 

 

 

Best Regards

Yilong Zhou

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

Helpful resources

Announcements
Europe Fabric Conference

Europe’s largest Microsoft Fabric Community Conference

Join the community in Stockholm for expert Microsoft Fabric learning including a very exciting keynote from Arun Ulag, Corporate Vice President, Azure Data.

Power BI Carousel June 2024

Power BI Monthly Update - June 2024

Check out the June 2024 Power BI update to learn about new features.

RTI Forums Carousel3

New forum boards available in Real-Time Intelligence.

Ask questions in Eventhouse and KQL, Eventstream, and Reflex.