Forum Discussion
Conditional formatting in Matrix Table
I have a matrix visual showing data of 4 months. Computers name in row field, dates of 4 months in Column field and CPU average of every computer in Value field. I want to change background colour of highest value for every month and for every comupter name i.e; each row should have 4 values should get highligted and same could be applicable for all rows in the matrix. Kindly Help me with the resolution
Here i'm attaching the whole data set for better understanding
| Row Labels | 01-Feb | 02-Feb | 03-Feb | 04-Feb | 05-Feb | 06-Feb | 07-Feb | 08-Feb | 09-Feb | 10-Feb | 11-Feb | 12-Feb | 13-Feb | 14-Feb | 15-Feb | 16-Feb | 17-Feb | 18-Feb | 19-Feb | 20-Feb | 21-Feb | 22-Feb | 23-Feb | 24-Feb | 25-Feb | 26-Feb | 27-Feb | 28-Feb | 29-Feb | 01-Mar | 02-Mar | 03-Mar | 04-Mar | 05-Mar | 06-Mar | 07-Mar | 08-Mar | 09-Mar | 10-Mar | 11-Mar | 12-Mar | 13-Mar | 14-Mar | 15-Mar | 16-Mar | 17-Mar | 18-Mar | 19-Mar | 20-Mar | 21-Mar | 22-Mar | 23-Mar | 24-Mar | 25-Mar | 26-Mar | 27-Mar | 28-Mar | 29-Mar | 30-Mar | 31-Mar | 01-Apr | 02-Apr | 03-Apr | 04-Apr | 05-Apr | 06-Apr | 07-Apr | 08-Apr | 09-Apr | 10-Apr | 11-Apr | 12-Apr | 13-Apr | 14-Apr | 15-Apr | 16-Apr | 17-Apr | 18-Apr | 19-Apr | 20-Apr | 21-Apr | 22-Apr | 23-Apr | 24-Apr | 25-Apr | 26-Apr | 27-Apr | 28-Apr | 29-Apr | 30-Apr | 01-May | 02-May | 03-May | 04-May | 05-May | 06-May | 07-May | 08-May | 09-May | 10-May | 11-May | 12-May | 13-May | 14-May | 15-May | 16-May | 17-May | 18-May | 19-May | 20-May | 21-May | 22-May | 23-May | 24-May | 25-May | 26-May | 27-May | 28-May | 29-May | 30-May | 31-May |
| ABC | 3.245776 | 2.93106 | 2.948109 | 2.906601 | 2.994802 | 3.08809 | 3.052478 | 3.033193 | 3.020442 | 2.960306 | 3.022792 | 2.995876 | 3.046906 | 3.120445 | 3.057552 | 2.995227 | 3.022975 | 2.985178 | 3.041812 | 3.100819 | 3.2454 | 3.22864 | 3.453313 | 3.395911 | 3.414993 | 3.233346 | 3.326281 | 3.230541 | 3.209912 | 3.06939 | 3.086276 | 3.214992 | 3.332429 | 3.531987 | 4.38017 | 4.454582 | 4.203712 | 4.227627 | 4.171633 | 4.164295 | 4.33894 | 4.191572 | 4.200447 | 4.127775 | 4.178199 | 4.222138 | 4.33298 | 4.512986 | 4.283242 | 3.061361 | 3.113764 | 3.065813 | 3.06866 | 3.736458 | 3.01604 | 2.819998 | 2.629012 | 2.573457 | 2.61235 | 2.746112 | 2.847828 | 2.949404 | 2.991171 | 3.043321 | 3.02626 | 3.153112 | 3.122201 | 3.259399 | 3.160183 | 3.187581 | 3.598452 | 3.246137 | 3.266416 | 3.336474 | 3.304811 | 3.230829 | 4.04198 | 3.824973 | 3.190845 | 3.050627 | 2.926364 | 2.977929 | 3.007047 | 3.018291 | 2.956035 | 2.876966 | 2.907845 | 2.850654 | 2.936649 | 2.931553 | 2.91897 | 2.936427 | 3.021741 | 3.010562 | 2.849408 | 2.820951 | 2.740197 | 2.801853 | 2.870882 | 2.904315 | 2.923612 | 2.856269 | 2.829312 | 2.733721 | 2.736908 | 2.949609 | 3.251045 | 2.902697 | 3.03528 | 2.981083 | 2.942972 | 2.888551 | 2.850221 | 2.984866 | 3.004355 | 2.881654 | 2.926332 | 2.984451 | 3.002878 | 4.019996 | 4.093973 |
| DEF | 7.019671 | 6.954742 | 6.874146 | 6.944771 | 6.945359 | 6.986665 | 6.964015 | 6.83828 | 6.852804 | 6.97712 | 6.955708 | 6.92042 | 6.981649 | 7.10423 | 7.092111 | 7.090595 | 7.747339 | 7.391184 | 6.655784 | 6.641957 | 6.793372 | 6.606365 | 6.744089 | 6.83737 | 6.859716 | 6.842978 | 6.787913 | 6.782014 | 6.930302 | 6.641757 | 6.718469 | 6.947947 | 6.846355 | 6.972591 | 6.909307 | 6.964281 | 6.910415 | 7.003529 | 6.983161 | 6.974363 | 7.043452 | 6.939253 | 6.98831 | 6.91065 | 8.350034 | 7.382284 | 7.300735 | 7.30439 | 7.785113 | 7.321004 | 7.325001 | 9.403349 | 7.298425 | 7.495832 | 7.86525 | 7.487628 | 7.528857 | 7.657393 | 7.510195 | 9.286267 | 7.225247 | 7.89622 | 7.882157 | 7.895736 | 7.764631 | 7.886143 | 7.981145 | 7.848593 | 7.866019 | 7.821803 | 7.899974 | 7.85886 | 7.910327 | 8.132237 | 8.07512 | 8.013101 | 8.090156 | 7.929945 | 7.853242 | 8.932072 | 9.25461 | 8.081871 | 8.033737 | 7.823791 | 7.726944 | 7.878582 | 7.830606 | 7.878461 | 7.880838 | 8.412342 | 7.443304 | 8.00961 | 7.974067 | 8.248611 | 8.043625 | 8.094024 | 8.097409 | 8.01078 | 8.228728 | 9.3273 | 9.00056 | 9.036677 | 9.154088 | 9.45481 | 8.758509 | 8.763528 | 8.656596 | 8.38026 | 8.437169 | 8.389222 | 8.398948 | 8.445884 | 8.854428 | 8.72204 | 8.508143 | 8.637486 | 8.707839 | 8.502716 | 8.497472 | 8.690714 | 9.198497 |
| GHI | 4.215081 | 4.205673 | 4.352842 | 4.303439 | 4.338703 | 4.369363 | 4.13554 | 4.115388 | 4.088186 | 7.487878 | 7.1475 | 6.916914 | 7.08097 | 7.150508 | 7.052956 | 7.061769 | 7.971821 | 8.391133 | 6.889737 | 7.088427 | 7.335072 | 7.401137 | 7.536386 | 7.75915 | 7.696268 | 8.233905 | 7.953249 | 7.666672 | 7.713544 | 7.460795 | 7.519606 | 7.683715 | 7.594292 | 7.910269 | 7.961773 | 7.999784 | 7.830665 | 7.983932 | 8.170864 | 8.439053 | 8.309834 | 8.276048 | 8.415485 | 8.459459 | 8.565166 | 7.001095 | 6.891004 | 7.291339 | 7.817413 | 7.107717 | 7.121167 | 7.118648 | 7.291354 | 7.451692 | 7.523231 | 7.593439 | 7.461174 | 7.557248 | 7.521386 | 8.83043 | 7.309711 | 7.998509 | 8.077515 | 8.349378 | 8.373889 | 8.337172 | 8.612138 | 8.461116 | 8.590288 | 8.335542 | 8.460844 | 8.399891 | 8.346152 | 8.502693 | 8.569057 | 8.609424 | 8.52866 | 8.456807 | 8.53165 | 9.239362 | 7.587701 | 9.041251 | 7.679485 | 7.829651 | 7.60095 | 7.734413 | 7.697742 | 7.590675 | 7.672729 | 8.188152 | 7.984101 | 8.077937 | 8.156973 | 8.125922 | 7.893885 | 8.029476 | 8.149572 | 7.947239 | 8.130045 | 9.132759 | 9.030305 | 9.085874 | 8.939794 | 9.294367 | 8.435759 | 8.714246 | 8.820995 | 8.90232 | 9.13037 | 9.21427 | 9.35914 | 9.45922 | 9.801244 | 9.956006 | 9.939951 | 9.855377 | 9.961893 | 10.81999 | 9.625897 | 10.23691 | 10.11735 |
| JKL | 6.985512 | 6.771179 | 6.420013 | 6.599174 | 6.921434 | 6.692304 | 6.648354 | 6.672106 | 6.83702 | 6.375073 | 6.451194 | 6.697211 | 6.911699 | 6.940217 | 6.850909 | 6.932908 | 6.869187 | 6.865522 | 6.926739 | 6.955289 | 6.992228 | 7.020785 | 7.100226 | 7.138665 | 7.185774 | 7.116879 | 7.15579 | 7.075233 | 7.081223 | 6.539243 | 6.303909 | 6.655766 | 6.858507 | 6.843406 | 6.76681 | 6.882103 | 7.503482 | 7.090046 | 7.153272 | 6.705873 | 6.986562 | 6.932012 | 7.243966 | 7.130281 | 7.210777 | 7.278994 | 7.388262 | 7.418765 | 7.342044 | 7.379343 | 7.393087 | 7.333223 | 7.440471 | 7.448555 | 7.523805 | 7.37266 | 7.388847 | 7.516759 | 7.34294 | 7.392543 | 7.370179 | 7.826197 | 7.801821 | 7.898813 | 7.86241 | 8.172781 | 7.747136 | 7.957314 | 7.853387 | 7.759688 | 7.855832 | 7.866072 | 9.086322 | 7.604269 | 7.484297 | 7.619321 | 7.682413 | 9.237487 | 7.626125 | 7.639379 | 7.61254 | 7.551986 | 7.735046 | 7.649145 | 7.605315 | 7.689857 | 7.645417 | 7.776051 | 7.76002 | 7.76519 | 7.563851 | 7.671823 | 7.831358 | 7.850886 | 7.546145 | 7.68857 | 7.790866 | 7.689947 | 7.923901 | 8.932606 | 8.746556 | 8.858991 | 8.784737 | 8.662896 | 8.571929 | 8.811523 | 8.724541 | 8.834073 | 8.790575 | 8.897497 | 8.74917 | 8.999323 | 8.906557 | 8.989048 | 8.846009 | 9.052123 | 9.145074 | 8.756013 | 8.850691 | 8.801747 | 8.743147 |
Hi Anonymous
Based on your needs, I have created the following table.
First, you should create a measure like this:
Measure = VAR select_company = SELECTEDVALUE('Table'[Company]) VAR max_value = MAXX(FILTER(ALL('Table'),'Table'[Company]=select_company),'Table'[CPU]) RETURN SWITCH(TRUE(),SELECTEDVALUE('Table'[CPU])=max_value,"blue")Then click the background color conditional formating:
Result:
Best Regards,
Jayleny
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
2 Replies
Try this measure
HighlightColor = VAR CurrentValue = [AvgCPU] VAR MaxValue = CALCULATE(MAX([AvgCPU]), ALLEXCEPT('YourTable', 'YourTable'[Row Labels])) RETURN IF(CurrentValue = MaxValue, 1, 0)Hi Anonymous
Based on your needs, I have created the following table.
First, you should create a measure like this:
Measure = VAR select_company = SELECTEDVALUE('Table'[Company]) VAR max_value = MAXX(FILTER(ALL('Table'),'Table'[Company]=select_company),'Table'[CPU]) RETURN SWITCH(TRUE(),SELECTEDVALUE('Table'[CPU])=max_value,"blue")Then click the background color conditional formating:
Result:
Best Regards,
Jayleny
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
