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Hi All,
I am trying to create monthly/Yearly PowerBI attrition. Following is data.
Attrition = (Total No of employee left for month/No of average employee for month)*100
Number of average employee = (no of employee at the start of the month + no of employee at the end of the month)/2
I want the output in the desired format
I saw three date columns in the data set: start date, end date, and date. Can you explain why the date column was used and where was the date column derived from?
Ashish_Mathur
Hi,
Step into the Query Editor to see the transformation steps which i ran on the dataset.
Hi,
When I put the month-end employees query, it is calculating I one extra employee under each country, not sure why.
Month end employees = CALCULATE([Employee count],DATESBETWEEN('Calendar'[Date],[Date since when data is available],[Date since when data is available]),USERELATIONSHIP('Employee Records'[Hire Date],'Calendar'[Date]))+[Hired till date]-[Left till date]
Hi,
Study my previous file carefully. If the answers on that file are correct, then the answers on your live file should also be so. I d not know how else to help you.
I did, I have put the calculations same as yours. In the month-end employees, it is giving me one extra count. I'm sure in some way you can help me, if you would like to 🙂
Associate ID | Full Name | Hire Date/Rehire Date | Termination Date |
1 | Sample, 1 | 07/18/2011 | 02/01/2022 |
2 | Sample, 2 | 09/15/2011 | |
3 | Sample, 3 | 01/01/2008 | |
4 | Sample, 4 | 01/07/2008 | |
5 | Sample, 5 | 01/21/2008 | |
6 | Sample, 6 | 02/20/2008 | |
7 | Sample, 7 | 03/24/2008 | |
8 | Sample, 8 | 03/17/2008 | |
9 | Sample, 9 | 04/09/2008 | |
10 | Sample, 10 | 08/25/2008 | |
11 | Sample, 11 | 04/03/2009 | |
12 | Sample, 12 | 04/20/2009 | 03/04/2022 |
13 | Sample, 13 | 04/20/2009 | |
14 | Sample, 14 | 05/11/2009 | |
15 | Sample, 15 | 05/11/2009 | |
16 | Sample, 16 | 08/17/2009 | |
17 | Sample, 17 | 11/09/2009 | |
18 | Sample, 18 | 11/02/2009 | |
19 | Sample, 19 | 01/04/2010 | |
20 | Sample, 20 | 01/04/2010 | |
21 | Sample, 21 | 02/01/2010 | |
22 | Sample, 22 | 05/26/2010 | |
23 | Sample, 23 | 07/06/2010 | |
24 | Sample, 24 | 08/23/2010 | |
25 | Sample, 25 | 10/11/2010 | |
26 | Sample, 26 | 02/21/2011 | 02/01/2022 |
27 | Sample, 27 | 03/07/2011 | |
28 | Sample, 28 | 08/29/2011 | |
29 | Sample, 29 | 08/29/2011 | |
30 | Sample, 30 | 09/15/2011 | |
31 | Sample, 31 | 09/15/2011 | |
32 | Sample, 32 | 10/10/2011 | |
33 | Sample, 33 | 11/14/2011 | 05/13/2022 |
34 | Sample, 34 | 11/07/2011 | |
35 | Sample, 35 | 06/01/2014 | |
36 | Sample, 36 | 06/01/2014 | |
37 | Sample, 37 | 06/15/2009 | |
38 | Sample, 38 | 10/14/2013 | |
39 | Sample, 39 | 02/09/2009 | |
40 | Sample, 40 | 11/16/2009 | |
41 | Sample, 41 | 10/17/2008 | |
42 | Sample, 42 | 04/12/2010 | |
43 | Sample, 43 | 06/01/2010 | |
44 | Sample, 44 | 07/17/2006 | |
45 | Sample, 45 | 02/21/2011 | |
46 | Sample, 46 | 03/07/2011 | |
47 | Sample, 47 | 04/11/2011 | |
48 | Sample, 48 | 02/14/2007 | |
49 | Sample, 49 | 02/09/2007 | |
50 | Sample, 50 | 11/19/2016 | |
51 | Sample, 51 | 07/11/2022 | |
52 | Sample, 52 | 07/05/2011 | |
53 | Sample, 53 | 01/16/2014 | |
54 | Sample, 54 | 04/26/2019 | 04/25/2022 |
55 | Sample, 55 | 03/04/2019 | |
56 | Sample, 56 | 12/15/2016 | |
57 | Sample, 57 | 10/16/2006 | |
58 | Sample, 58 | 04/16/2018 | |
59 | Sample, 59 | 08/11/2022 | 03/08/2023 |
60 | Sample, 60 | 02/24/2017 | |
61 | Sample, 61 | 05/26/2015 | |
62 | Sample, 62 | 03/09/2016 | |
63 | Sample, 63 | 07/11/2022 | |
64 | Sample, 64 | 04/13/2016 | |
65 | Sample, 65 | 03/04/2022 | |
66 | Sample, 66 | 11/14/2016 | |
67 | Sample, 67 | 11/14/2018 | |
68 | Sample, 68 | 04/05/2022 | |
69 | Sample, 69 | 08/01/2014 | 06/01/2022 |
70 | Sample, 70 | 01/03/2017 | |
71 | Sample, 71 | 09/15/2008 | |
72 | Sample, 72 | 09/21/2016 | |
73 | Sample, 73 | 07/09/2012 | |
74 | Sample, 74 | 04/02/2012 | |
75 | Sample, 75 | 11/30/2022 | |
76 | Sample, 76 | 11/30/2004 | 11/01/2022 |
77 | Sample, 77 | 05/01/2020 | |
78 | Sample, 78 | 05/15/2017 | |
79 | Sample, 79 | 10/11/2021 | 05/10/2022 |
80 | Sample, 80 | 09/02/2014 | |
81 | Sample, 81 | 12/03/2012 | |
82 | Sample, 82 | 08/16/2010 | |
83 | Sample, 83 | 08/29/2016 | |
84 | Sample, 84 | 10/07/2019 | |
85 | Sample, 85 | 12/21/2018 | |
86 | Sample, 86 | 06/05/2013 | |
87 | Sample, 87 | 08/03/2009 | |
88 | Sample, 88 | 01/18/2021 | |
89 | Sample, 89 | 01/01/2019 | |
90 | Sample, 90 | 11/16/2020 | 02/09/2022 |
91 | Sample, 91 | 09/05/2019 | |
92 | Sample, 92 | 02/18/2019 | |
93 | Sample, 93 | 08/07/2000 | |
94 | Sample, 94 | 11/23/2020 | |
95 | Sample, 95 | 08/21/2017 | |
96 | Sample, 96 | 08/06/2018 | |
97 | Sample, 97 | 12/17/2018 | |
98 | Sample, 98 | 01/04/2016 | |
99 | Sample, 99 | 08/01/2002 | |
100 | Sample, 100 | 05/07/2018 | |
101 | Sample, 101 | 06/10/2019 | |
102 | Sample, 102 | 04/07/2019 | |
103 | Sample, 103 | 06/20/2017 | |
104 | Sample, 104 | 05/15/2017 | |
105 | Sample, 105 | 03/01/2017 | |
106 | Sample, 106 | 04/15/2019 | |
107 | Sample, 107 | 10/01/2018 | |
108 | Sample, 108 | 05/07/2018 | |
109 | Sample, 109 | 09/30/2019 | |
110 | Sample, 110 | 05/14/2021 | |
111 | Sample, 111 | 04/18/2019 |
Hi @Nabarun1992,
Can you please share a pbix or some dummy data that keep the raw data structure with expected results? It should help us clarify your scenario and test to coding formula.
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Xiaoxin Sheng
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