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Anonymous
Not applicable

Overlapping Time Intervals

‎07-27-2023 11:46 AM

Hello Everyone

 

I have been struggling with a problem involving overlapping times and dates. I have a very large document with a ton of potential time clock overlapps. I have tried the solutions listed in the link below. How to Get Your Question Answered Quickly - Microsoft Fabric Community Sadly, this solution hasn't worked for me and I was looking for a solution hopefully through Power Query. I am trying to find a way to flag when there are overlapping times clocked in.  My data is listed below. This is a simplified document with the 3 columns of dupMeas_Payroll_ID_Date, StartTimeF, and EndTimeF. All 3 are constructed with combining multiple columns such as dates, times, and Payroll_ID. My data is listed below. If someone would prefer I repost the data constructed in a different way, I can do that.

 

 

 

 

 

 

Partition.DupMeas_Payroll_ID_Date	data.Partition.StartTimeF	data.Partition.EndTimeF
103573 , 1/11/2022	1/11/2022 8:00	1/11/2022 9:20
103573 , 1/11/2022	1/11/2022 9:20	1/11/2022 10:00
103573 , 1/11/2022	1/11/2022 10:00	1/11/2022 10:20
103573 , 1/11/2022	1/11/2022 10:20	1/11/2022 10:40
103573 , 1/11/2022	1/11/2022 10:40	1/11/2022 11:00
103573 , 1/11/2022	1/11/2022 11:00	1/11/2022 11:20
103573 , 1/11/2022	1/11/2022 11:20	1/11/2022 12:10
103573 , 1/11/2022	1/11/2022 12:10	1/11/2022 12:25
103573 , 1/11/2022	1/11/2022 12:40	1/11/2022 13:00
103893 , 1/11/2022	1/11/2022 8:25	1/11/2022 9:20
103893 , 1/11/2022	1/11/2022 9:20	1/11/2022 10:00
103893 , 1/11/2022	1/11/2022 10:00	1/11/2022 10:20
103893 , 1/11/2022	1/11/2022 10:20	1/11/2022 10:40
103893 , 1/11/2022	1/11/2022 10:40	1/11/2022 11:00
103893 , 1/11/2022	1/11/2022 11:00	1/11/2022 11:20
103893 , 1/11/2022	1/11/2022 11:20	1/11/2022 12:20
103893 , 1/11/2022	1/11/2022 12:10	1/11/2022 12:25
103893 , 1/11/2022	1/11/2022 12:40	1/11/2022 13:00

 

 

 

 

 

Partition.DupMeas_Payroll_ID_Datedata.Partition.StartTimeFdata.Partition.EndTimeF
103573 , 1/11/20221/11/2022 8:001/11/2022 9:20
103573 , 1/11/20221/11/2022 9:201/11/2022 10:00
103573 , 1/11/20221/11/2022 10:001/11/2022 10:20
103573 , 1/11/20221/11/2022 10:201/11/2022 10:40
103573 , 1/11/20221/11/2022 10:401/11/2022 11:00
103573 , 1/11/20221/11/2022 11:001/11/2022 11:20
103573 , 1/11/20221/11/2022 11:201/11/2022 12:10
103573 , 1/11/20221/11/2022 12:101/11/2022 12:25
103573 , 1/11/20221/11/2022 12:401/11/2022 13:00
103893 , 1/11/20221/11/2022 8:251/11/2022 9:20
103893 , 1/11/20221/11/2022 9:201/11/2022 10:00
103893 , 1/11/20221/11/2022 10:001/11/2022 10:20
103893 , 1/11/20221/11/2022 10:201/11/2022 10:40
103893 , 1/11/20221/11/2022 10:401/11/2022 11:00
103893 , 1/11/20221/11/2022 11:001/11/2022 11:20
103893 , 1/11/20221/11/2022 11:201/11/2022 12:20
103893 , 1/11/20221/11/2022 12:101/11/2022 12:25
103893 , 1/11/20221/11/2022 12:401/11/2022 13:00

My desired result would be a way to flag overllapping times for the specific day and Payroll Id. For example, the 2 lines highlighted in Blue have an overlapping time interval. Flagging these 2 lines so a simple filter could show a table with all overlapping times would be great. The end result for running it on this data would look like.

Partition.DupMeas_Payroll_ID_Datedata.Partition.StartTimeFdata.Partition.EndTimeF
103893 , 1/11/20221/11/2022 11:201/11/2022 12:20
103893 , 1/11/20221/11/2022 12:101/11/2022 12:25

There are a lot of clock in/out times for each payroll_id.

 

Thank you very much in advance

 

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Message 1 of 2
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0
1 ACCEPTED SOLUTION
ThxAlot
Super User
Super User

‎07-27-2023 03:31 PM 
let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("jZJBDoQgDEWvQliT2BaYUa5ivAfHn1EmEyzSdme+75VC/r57hJjf0QWHC+JCQOSD/3+7tQDcgq0Q+COo4sX1AcI5ymA2kCW2QxvIkmRVE1PRujAOC6N1YRwWpoI29QJZQtmo8rvG7q7rJvXhe8SsD5Io90EylT4oqtQHRZX6IKpyHxR17INRlfugqNM+1FrPPw/wjaYZHZ/onCd0+r1cF73ms/M4u9HHBw==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [Partition.DupMeas_Payroll_ID_Date = _t, data.Partition.StartTimeF = _t, data.Partition.EndTimeF = _t]),
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"Partition.DupMeas_Payroll_ID_Date", type text}, {"data.Partition.StartTimeF", type datetime}, {"data.Partition.EndTimeF", type datetime}}),
    #"Sorted Rows" = Table.Sort(#"Changed Type",{{"Partition.DupMeas_Payroll_ID_Date", Order.Ascending}, {"data.Partition.StartTimeF", Order.Ascending}}),
    Grouped = Table.Group(#"Sorted Rows", Table.ColumnNames(Source), {"grp", each _}, 0, (x,y) => Byte.From(x[Partition.DupMeas_Payroll_ID_Date] <> y[Partition.DupMeas_Payroll_ID_Date] or y[data.Partition.StartTimeF] >= x[data.Partition.EndTimeF])),
    Selected = Table.Combine(List.Select(Grouped[grp], each Table.RowCount(_) > 1))
in
    Selected

ThxAlot_0-1690497098920.png



Expertise = List.Accumulate(


        {Days as from Today},


        {Skills and Knowledge},


        (Current, Everyday) => Current & Day.LearnAndPractise(Everyday)


)



View solution in original post

Message 2 of 2
2,366 Views
1 REPLY 1
ThxAlot
Super User
Super User

‎07-27-2023 03:31 PM 
let
    Source = Table.FromRows(Json.Document(Binary.Decompress(Binary.FromText("jZJBDoQgDEWvQliT2BaYUa5ivAfHn1EmEyzSdme+75VC/r57hJjf0QWHC+JCQOSD/3+7tQDcgq0Q+COo4sX1AcI5ymA2kCW2QxvIkmRVE1PRujAOC6N1YRwWpoI29QJZQtmo8rvG7q7rJvXhe8SsD5Io90EylT4oqtQHRZX6IKpyHxR17INRlfugqNM+1FrPPw/wjaYZHZ/onCd0+r1cF73ms/M4u9HHBw==", BinaryEncoding.Base64), Compression.Deflate)), let _t = ((type nullable text) meta [Serialized.Text = true]) in type table [Partition.DupMeas_Payroll_ID_Date = _t, data.Partition.StartTimeF = _t, data.Partition.EndTimeF = _t]),
    #"Changed Type" = Table.TransformColumnTypes(Source,{{"Partition.DupMeas_Payroll_ID_Date", type text}, {"data.Partition.StartTimeF", type datetime}, {"data.Partition.EndTimeF", type datetime}}),
    #"Sorted Rows" = Table.Sort(#"Changed Type",{{"Partition.DupMeas_Payroll_ID_Date", Order.Ascending}, {"data.Partition.StartTimeF", Order.Ascending}}),
    Grouped = Table.Group(#"Sorted Rows", Table.ColumnNames(Source), {"grp", each _}, 0, (x,y) => Byte.From(x[Partition.DupMeas_Payroll_ID_Date] <> y[Partition.DupMeas_Payroll_ID_Date] or y[data.Partition.StartTimeF] >= x[data.Partition.EndTimeF])),
    Selected = Table.Combine(List.Select(Grouped[grp], each Table.RowCount(_) > 1))
in
    Selected

ThxAlot_0-1690497098920.png



Expertise = List.Accumulate(


        {Days as from Today},


        {Skills and Knowledge},


        (Current, Everyday) => Current & Day.LearnAndPractise(Everyday)


)



Message 2 of 2
2,367 Views

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