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Hi ,
I have an excel source with columns like Area , Age Range and Population corresponding to each age and area . Something like below.
The age categories goes up till 100 years and there are multiple areas.
How do I find the average age of each area please?
This is a sample extract from the excel.
Please help.
Thanks
Ankhi
Area | Age (101 categories) | Population |
XyZ | Aged under 1 year | 539 |
XyZ | Aged 1 year | 583 |
XyZ | Aged 2 years | 698 |
XyZ | Aged 3 years | 674 |
XyZ | Aged 4 years | 698 |
XyZ | Aged 5 years | 765 |
XyZ | Aged 6 years | 737 |
XyZ | Aged 7 years | 752 |
XyZ | Aged 8 years | 843 |
XyZ | Aged 9 years | 812 |
XyZ | Aged 10 years | 791 |
XyZ | Aged 11 years | 790 |
XyZ | Aged 12 years | 735 |
XyZ | Aged 13 years | 767 |
XyZ | Aged 14 years | 730 |
XyZ | Aged 15 years | 680 |
XyZ | Aged 16 years | 748 |
XyZ | Aged 17 years | 667 |
XyZ | Aged 18 years | 613 |
XyZ | Aged 19 years | 488 |
XyZ | Aged 20 years | 448 |
XyZ | Aged 21 years | 546 |
XyZ | Aged 22 years | 528 |
XyZ | Aged 23 years | 554 |
XyZ | Aged 24 years | 591 |
XyZ | Aged 25 years | 605 |
XyZ | Aged 26 years | 624 |
XyZ | Aged 27 years | 600 |
XyZ | Aged 28 years | 648 |
XyZ | Aged 29 years | 675 |
XyZ | Aged 30 years | 669 |
XyZ | Aged 31 years | 708 |
XyZ | Aged 32 years | 724 |
XyZ | Aged 33 years | 807 |
XyZ | Aged 34 years | 758 |
XyZ | Aged 35 years | 796 |
XyZ | Aged 36 years | 745 |
XyZ | Aged 37 years | 767 |
XyZ | Aged 38 years | 788 |
XyZ | Aged 39 years | 843 |
XyZ | Aged 40 years | 829 |
XyZ | Aged 41 years | 851 |
XyZ | Aged 42 years | 825 |
XyZ | Aged 43 years | 831 |
XyZ | Aged 44 years | 796 |
XyZ | Aged 45 years | 874 |
XyZ | Aged 46 years | 843 |
XyZ | Aged 47 years | 828 |
XyZ | Aged 48 years | 977 |
XyZ | Aged 49 years | 974 |
XyZ | Aged 50 years | 947 |
XyZ | Aged 51 years | 926 |
XyZ | Aged 52 years | 943 |
XyZ | Aged 53 years | 910 |
XyZ | Aged 54 years | 933 |
XyZ | Aged 55 years | 931 |
XyZ | Aged 56 years | 965 |
XyZ | Aged 57 years | 875 |
XyZ | Aged 58 years | 911 |
XyZ | Aged 59 years | 845 |
XyZ | Aged 60 years | 850 |
XyZ | Aged 61 years | 777 |
XyZ | Aged 62 years | 746 |
XyZ | Aged 63 years | 847 |
XyZ | Aged 64 years | 740 |
XyZ | Aged 65 years | 714 |
XyZ | Aged 66 years | 721 |
XyZ | Aged 67 years | 678 |
XyZ | Aged 68 years | 768 |
XyZ | Aged 69 years | 713 |
XyZ | Aged 70 years | 695 |
XyZ | Aged 71 years | 756 |
XyZ | Aged 72 years | 795 |
XyZ | Aged 73 years | 881 |
XyZ | Aged 74 years | 896 |
XyZ | Aged 75 years | 661 |
XyZ | Aged 76 years | 718 |
XyZ | Aged 77 years | 623 |
XyZ | Aged 78 years | 635 |
XyZ | Aged 79 years | 484 |
XyZ | Aged 80 years | 482 |
XyZ | Aged 81 years | 514 |
XyZ | Aged 82 years | 465 |
XyZ | Aged 83 years | 433 |
XyZ | Aged 84 years | 403 |
XyZ | Aged 85 years | 330 |
XyZ | Aged 86 years | 331 |
XyZ | Aged 87 years | 256 |
XyZ | Aged 88 years | 240 |
XyZ | Aged 89 years | 213 |
XyZ | Aged 90 years | 197 |
XyZ | Aged 91 years | 150 |
XyZ | Aged 92 years | 119 |
XyZ | Aged 93 years | 110 |
XyZ | Aged 94 years | 72 |
XyZ | Aged 95 years | 52 |
XyZ | Aged 96 years | 56 |
XyZ | Aged 97 years | 23 |
XyZ | Aged 98 years | 15 |
XyZ | Aged 99 years | 17 |
XyZ | Aged 100 years and over | 23 |
ABC | Aged under 1 year | 1097 |
ABC | Aged 1 year | 1177 |
ABC | Aged 2 years | 1210 |
ABC | Aged 3 years | 1225 |
ABC | Aged 4 years | 1365 |
ABC | Aged 5 years | 1299 |
ABC | Aged 6 years | 1273 |
ABC | Aged 7 years | 1342 |
ABC | Aged 8 years | 1398 |
ABC | Aged 9 years | 1403 |
ABC | Aged 10 years | 1468 |
ABC | Aged 11 years | 1435 |
ABC | Aged 12 years | 1383 |
ABC | Aged 13 years | 1382 |
ABC | Aged 14 years | 1375 |
ABC | Aged 15 years | 1315 |
ABC | Aged 16 years | 1289 |
ABC | Aged 17 years | 1241 |
ABC | Aged 18 years | 1182 |
ABC | Aged 19 years | 1014 |
ABC | Aged 20 years | 998 |
ABC | Aged 21 years | 1145 |
ABC | Aged 22 years | 1232 |
ABC | Aged 23 years | 1303 |
ABC | Aged 24 years | 1372 |
ABC | Aged 25 years | 1357 |
ABC | Aged 26 years | 1441 |
ABC | Aged 27 years | 1390 |
ABC | Aged 28 years | 1493 |
ABC | Aged 29 years | 1546 |
ABC | Aged 30 years | 1606 |
ABC | Aged 31 years | 1479 |
ABC | Aged 32 years | 1463 |
ABC | Aged 33 years | 1514 |
ABC | Aged 34 years | 1455 |
ABC | Aged 35 years | 1458 |
ABC | Aged 36 years | 1433 |
ABC | Aged 37 years | 1477 |
ABC | Aged 38 years | 1449 |
ABC | Aged 39 years | 1420 |
ABC | Aged 40 years | 1449 |
ABC | Aged 41 years | 1568 |
ABC | Aged 42 years | 1404 |
ABC | Aged 43 years | 1335 |
ABC | Aged 44 years | 1419 |
ABC | Aged 45 years | 1460 |
ABC | Aged 46 years | 1578 |
ABC | Aged 47 years | 1617 |
ABC | Aged 48 years | 1836 |
ABC | Aged 49 years | 1942 |
ABC | Aged 50 years | 1918 |
ABC | Aged 51 years | 1948 |
ABC | Aged 52 years | 2014 |
ABC | Aged 53 years | 2021 |
ABC | Aged 54 years | 2027 |
ABC | Aged 55 years | 1963 |
ABC | Aged 56 years | 2075 |
ABC | Aged 57 years | 1979 |
ABC | Aged 58 years | 2014 |
ABC | Aged 59 years | 1818 |
ABC | Aged 60 years | 1779 |
ABC | Aged 61 years | 1694 |
ABC | Aged 62 years | 1722 |
ABC | Aged 63 years | 1599 |
ABC | Aged 64 years | 1646 |
ABC | Aged 65 years | 1540 |
ABC | Aged 66 years | 1449 |
ABC | Aged 67 years | 1568 |
ABC | Aged 68 years | 1471 |
ABC | Aged 69 years | 1464 |
ABC | Aged 70 years | 1517 |
ABC | Aged 71 years | 1610 |
ABC | Aged 72 years | 1626 |
ABC | Aged 73 years | 1729 |
ABC | Aged 74 years | 1679 |
ABC | Aged 75 years | 1241 |
ABC | Aged 76 years | 1334 |
ABC | Aged 77 years | 1253 |
ABC | Aged 78 years | 1121 |
ABC | Aged 79 years | 931 |
ABC | Aged 80 years | 792 |
ABC | Aged 81 years | 785 |
ABC | Aged 82 years | 808 |
ABC | Aged 83 years | 672 |
ABC | Aged 84 years | 607 |
ABC | Aged 85 years | 535 |
ABC | Aged 86 years | 457 |
ABC | Aged 87 years | 422 |
ABC | Aged 88 years | 396 |
ABC | Aged 89 years | 308 |
ABC | Aged 90 years | 253 |
ABC | Aged 91 years | 222 |
ABC | Aged 92 years | 177 |
ABC | Aged 93 years | 143 |
ABC | Aged 94 years | 113 |
ABC | Aged 95 years | 72 |
ABC | Aged 96 years | 73 |
ABC | Aged 97 years | 39 |
ABC | Aged 98 years | 24 |
ABC | Aged 99 years | 16 |
ABC | Aged 100 years and over | 29 |
Hi @Ankhi ,
Try below steps:
Load your data: Load the data from Excel into Power BI.
Add a calculated column for the Age Midpoint: For the age categories, create a new column for the midpoint of each age category. You can use a SWITCH statement or a custom formula to handle the age ranges. Here's a formula for the midpoint:
AgeMidpoint =
SWITCH(
TRUE(),
'Table'[Age] = "Aged under 1 year", 0.5,
'Table'[Age] = "Aged 1 year", 1,
'Table'[Age] = "Aged 2 years", 2,
'Table'[Age] = "Aged 3 years", 3,
'Table'[Age] = "Aged 4 years", 4,
-- Continue for other age ranges --
'Table'[Age] = "Aged 100 years and over", 100
)
Add a weighted age column: Create a calculated column for the weighted age, which is the AgeMidpoint multiplied by the population.
WeightedAge = 'Table'[AgeMidpoint] * 'Table'[Population]
Create a measure to calculate the average age: You can now create a measure to calculate the average age of each area:
AverageAge =
DIVIDE(
SUM('Table'[WeightedAge]),
SUM('Table'[Population]),
0
)
Visualize the average age: Now, you can use a Matrix or Table visual to show the average age by area. Place Area in the rows and use the AverageAge measure in the values.
Please mark this post as solution if it helps you. Appreciate Kudos.
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