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Hi,
I have the data in the table below which holds property rent charged data every month. I'm trying to compare the change in data between each month to find out:
1) No of new properties in the month
2) No of properties suspended in the month
3) No of properties rent increased
4) No of properties rent decreased
5) No of properties rent was same
Once the data is prepared I like to create a visual
Prop Code | Amount | Adjustments | Payment Due | Month | Year | Payment Date |
1024 | 1408.33 | 1408.33 | July | 2016 | 31/07/2016 | |
1025 | 1408.33 | 1408.33 | July | 2016 | 31/07/2016 | |
1043 | 780 | 780 | July | 2016 | 31/07/2016 | |
1044 | 866.67 | 866.67 | July | 2016 | 31/07/2016 | |
1045 | 823.34 | 823.34 | July | 2016 | 31/07/2016 | |
1047 | 1238.4 | 1238.4 | July | 2016 | 31/07/2016 | |
1054 | 1430 | 1430 | July | 2016 | 31/07/2016 | |
1090 | 1607.74 | 1607.74 | July | 2016 | 31/07/2016 | |
1136 | 1820 | 1820 | July | 2016 | 31/07/2016 | |
1153 | 1603.33 | 1603.33 | July | 2016 | 31/07/2016 | |
1154 | 1911.9 | 1911.9 | July | 2016 | 31/07/2016 | |
1833 | 1825 | -1397 | 428 | July | 2016 | 31/07/2016 |
2058 | 2312 | -57.67 | 2254.33 | July | 2016 | 31/07/2016 |
2415 | 2259.52 | 2259.52 | July | 2016 | 31/07/2016 | |
3149 | 1781.55 | -121 | 1660.55 | July | 2016 | 31/07/2016 |
3400 | 2389.88 | -1320 | 1069.88 | July | 2016 | 31/07/2016 |
3474 | 217.26 | 217.26 | July | 2016 | 31/07/2016 | |
3489 | 2253.33 | 35.36 | 2288.69 | July | 2016 | 31/07/2016 |
3701 | 2368.15 | 2368.15 | July | 2016 | 31/07/2016 | |
3899 | 217.26 | 217.26 | July | 2016 | 31/07/2016 | |
4072 | 3407.56 | -1993.34 | 1414.22 | July | 2016 | 31/07/2016 |
4073 | 3186.23 | -2340 | 846.23 | July | 2016 | 31/07/2016 |
4074 | 2404.63 | -1646.67 | 757.96 | July | 2016 | 31/07/2016 |
1024 | 1408.33 | 1408.33 | August | 2016 | 31/08/2016 | |
1025 | 1408.33 | 1408.33 | August | 2016 | 31/08/2016 | |
1043 | 780 | 780 | August | 2016 | 31/08/2016 | |
1044 | 866.67 | 866.67 | August | 2016 | 31/08/2016 | |
1045 | 823.34 | 823.34 | August | 2016 | 31/08/2016 | |
1047 | 1238.4 | 1238.4 | August | 2016 | 31/08/2016 | |
1054 | 1430 | 1430 | August | 2016 | 31/08/2016 | |
1090 | 1607.74 | 1607.74 | August | 2016 | 31/08/2016 | |
1136 | 1820 | 1820 | August | 2016 | 31/08/2016 | |
1153 | 1603.33 | 1603.33 | August | 2016 | 31/08/2016 | |
1154 | 1911.9 | 1911.9 | August | 2016 | 31/08/2016 | |
1458 | 1968.61 | 43.45 | 2012.06 | August | 2016 | 31/08/2016 |
1488 | 2123.33 | 86.66 | 2209.99 | August | 2016 | 31/08/2016 |
2093 | 2042.26 | 43.46 | 2085.72 | August | 2016 | 31/08/2016 |
2335 | 1300 | 86.67 | 1386.67 | August | 2016 | 31/08/2016 |
3964 | 2036.66 | 173.32 | 2209.98 | August | 2016 | 31/08/2016 |
3966 | 11916.75 | -7150.05 | 4766.7 | August | 2016 | 31/08/2016 |
4058 | 4345.24 | 4345.24 | August | 2016 | 31/08/2016 | |
4059 | 6500 | -3250 | 3250 | August | 2016 | 31/08/2016 |
4060 | 6500 | -3250 | 3250 | August | 2016 | 31/08/2016 |
4061 | 4506.68 | -2253.34 | 2253.34 | August | 2016 | 31/08/2016 |
4075 | 2513.34 | 2513.34 | August | 2016 | 31/08/2016 | |
4076 | 1473.34 | 1473.34 | August | 2016 | 31/08/2016 | |
4077 | 2166.67 | 2166.67 | August | 2016 | 31/08/2016 | |
4078 | 1516.67 | 1516.67 | August | 2016 | 31/08/2016 | |
4079 | 417.18 | 417.18 | August | 2016 | 31/08/2016 | |
1024 | 1408.33 | 1408.33 | Sep | 2016 | 30/09/2016 | |
1025 | 1408.33 | 1408.33 | Sep | 2016 | 30/09/2016 | |
1043 | 780 | 780 | Sep | 2016 | 30/09/2016 | |
1044 | 866.67 | 866.67 | Sep | 2016 | 30/09/2016 | |
1045 | 823.34 | 823.34 | Sep | 2016 | 30/09/2016 | |
1047 | 1238.4 | 1238.4 | Sep | 2016 | 30/09/2016 | |
1054 | 1430 | 1430 | Sep | 2016 | 30/09/2016 | |
1090 | 1607.74 | 1607.74 | Sep | 2016 | 30/09/2016 | |
1136 | 1820 | 1820 | Sep | 2016 | 30/09/2016 | |
1153 | 1603.33 | 1603.33 | Sep | 2016 | 30/09/2016 | |
1154 | 1911.9 | 1911.9 | Sep | 2016 | 30/09/2016 | |
1921 | 2253.33 | 693.36 | 2946.69 | Sep | 2016 | 30/09/2016 |
2386 | 2094.3 | 237.58 | 2331.88 | Sep | 2016 | 30/09/2016 |
2987 | 1820 | 71.3 | 1891.3 | Sep | 2016 | 30/09/2016 |
3482 | 2231.66 | 260 | 2491.66 | Sep | 2016 | 30/09/2016 |
3609 | 2307.9 | -1724.56 | 583.34 | Sep | 2016 | 30/09/2016 |
4018 | 2036.66 | -2015.53 | 21.13 | Sep | 2016 | 30/09/2016 |
4026 | 1868.45 | -1825 | 43.45 | Sep | 2016 | 30/09/2016 |
4096 | 2968.94 | 2968.94 | Sep | 2016 | 30/09/2016 |
Thanks
Jag
I assume you analyze those data on month level. I suggest you add a Month Number column in your Table. Then you can create measures like below:
1) No of new properties in the month
No of new properties in the month = SUMX ( VALUES ( Table[Prop Code] ), IF ( CALCULATE ( COUNTROWS ( Table ), FILTER ( ALL ( Table ), Table[PaymentDate] < MAX ( Table[PaymentDate] ) ) ) = 0 && CALCULATE ( COUNTROWS ( Table ) ) = 1, 1, 0 ) )
2) No of properties suspended in the month
No of properties suspended in the month = SUMX ( VALUES ( Table[Prop Code] ), IF ( CALCULATE ( COUNTROWS ( Table ), FILTER ( ALL ( Table ), Table[Year] = MAX ( Table[Year] ) && Table[MonthNumber] = MAX ( Table[MonthNumber] ) - 1 ) ) > 0 && CALCULATE ( COUNTROWS ( Table ) ) = 0, 1, 0 ) )
3) No of properties rent increased
No of properties rent increased = SUMX ( VALUES ( Table[Prop Code] ), IF ( CALCULATE ( SUM ( Table[Adjustment] ) ) > 0, 1, 0 ) )
4) No of properties rent decreased
No of properties rent decreased = SUMX ( VALUES ( Table[Prop Code] ), IF ( CALCULATE ( SUM ( Table[Adjustment] ) ) < 0, 1, 0 ) )
5) No of properties rent was same
No of properties rent was same = SUMX ( VALUES ( Table[Prop Code] ), IF ( CALCULATE ( SUM ( Table[Adjustment] ) ) = BLANK (), 1, 0 ) )
Regards,
Hi Shiou,
I'm pretty new to PowerBI and havent worked on complex measures. Could you please upload the powerbi file so i can get an idea how the measures have been applied.
Thanks
Jag
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