Forum Discussion
Dynamic compare two scenarios based off selection
- Anonymous4 years ago
entity YYYY Region Market Client_Billings Client_Net_Revenue Client_Total_Gross_Revenue 5frFRANCcon 2019 EMEA France 7215.001 7215.001 7215.001 5grGRBGMcon 2019 EMEA Greece 1139903.943 169980.721 169980.721 5totANNALECT 2019 Global Global -0.33 0 0 5totPUERT 2019 LATAM Puerto Rico 0 0 0 5xtotBELGM 2019 EMEA Belgium 686262.48 29564.944 29564.944 2clCHILE 2019 LATAM Chile 0 -83248.259 -83248.259 5frFRANCcon 2019 EMEA France 48257.709 48257.709 48257.709 5totNZLND 2019 APAC New Zealand 0 0 0 5xtotBELGM 2019 EMEA Belgium 490480.479 35611.351 35611.351 5totOUTDR 2019 North America USA 7617 2151 2151 5totPHDUS 2019 North America USA 1881256 131983 131983 5totRESOL 2019 North America USA 344556.09 95790.55 333039 5totICON 2019 North America USA 4185 -7830 4185 5frFRANCcon 2019 EMEA France 0 0 0 5totASTRL 2019 APAC Australia 127774.281 127774.138 127774.138 5vnVIETNcon 2019 APAC Vietnam 631129.281 22485.766 22485.766 5xtotBELGM 2019 EMEA Belgium 26706.15 3997.819 3997.819 5totOUTDR 2019 North America USA 13907408 304864 304864 5dnDNMRKcon 2019 EMEA Denmark 0 0 0 5grGREECcon 2019 EMEA Greece 438234.409 316254.77 316254.77 5totOUTDR 2019 North America USA 422292 20041 20041 5totMOBIL 2019 EMEA United Kingdom 0 0 0 5totUKGRP 2019 EMEA United Kingdom 0 63424.817 63424.817 - 4 years ago
Ok, here are two possible ways of going about this.
The first is using the default matrix capabilities; this method will have a better performance but the metrics' headers are less clear.
The second involves creating a specific table layout which recreates the measures displayed to be used as the matrix header. It provides more clarity at the expense of performance possibly (my sample dataset is minute so I can't really compare the performances of both solutions).So...
Option 1: Default matrix capabilities.
We need two tables for "Year" to be able to compare the values. One of these tables has a regular One-to-many active relationship with the fact table; the second Year table's relationship is inactive. The model looks like this:
The measures for each metric follow these patterns:
For the first Year selection.Client Billings = SUM(FTable[Client_Billings])For the comparison Year selection:
Comp Billings = CALCULATE ( [Client Billings], REMOVEFILTERS ( DYear[Year] ), USERELATIONSHIP ( VYear[Year], FTable[Year] ) )and the % var between both years:
Billings % var = SWITCH ( TRUE (), AND ( ISBLANK ( [Client Billings] ), ISBLANK ( [Comp Billings] ) ), BLANK (), AND ( [Client Billings] = 0, [Comp Billings] = 0 ), 0, DIVIDE ( [Client Billings], [Comp Billings] ) - 1 )The matrix is set up as follows, with each individual measure set in order in the values bucket:
To get:
Option 2: Custom Header to show the columns by years
The setup requires creating a custom table to use as the matrix header. Firstly we need a list of the measures and their respective order. (copy and paste from Excel or use the "Enter Data" option to create it):
We then need to add the Years from the model (Crossjoin) to create both the actual header needed in the matrix and a sorting column. This is all done in Power Query:
Leave this table unrelated in the model. The model looks like this:
We only need to create one measure, referencing the measures we have already created, which is the one we will use for the matrix:
For Matrix = SWITCH ( TRUE (), ISBLANK ( [Client Billings] ) && ISBLANK ( [Comp Billings] ) && ISBLANK ( [Net Revenue] ), BLANK (), AND ( MAX ( Header[HYear] ) = SELECTEDVALUE ( DYear[Year] ), MAX ( Header[Order] ) = 1 ), FORMAT ( [Client Billings], "Currency" ), AND ( MAX ( Header[HYear] ) = SELECTEDVALUE ( VYear[Year] ), MAX ( Header[Order] ) = 2 ), FORMAT ( [Comp Billings], "Currency" ), AND ( MAX ( Header[HYear] ) = SELECTEDVALUE ( DYear[Year] ), MAX ( Header[Order] ) = 3 ), FORMAT ( [Billings % var], "Percent" ), AND ( MAX ( Header[HYear] ) = SELECTEDVALUE ( DYear[Year] ), MAX ( Header[Order] ) = 4 ), FORMAT ( [Gross Revenue], "Currency" ), AND ( MAX ( Header[HYear] ) = SELECTEDVALUE ( VYear[Year] ), MAX ( Header[Order] ) = 5 ), FORMAT ( [Comp Gross Revenue], "Currency" ), AND ( MAX ( Header[HYear] ) = SELECTEDVALUE ( DYear[Year] ), MAX ( Header[Order] ) = 6 ), FORMAT ( [Gross Rev % Var], "Percent" ), AND ( MAX ( Header[HYear] ) = SELECTEDVALUE ( DYear[Year] ), MAX ( Header[Order] ) = 7 ), FORMAT ( [Net Revenue], "Currency" ), AND ( MAX ( Header[HYear] ) = SELECTEDVALUE ( VYear[Year] ), MAX ( Header[Order] ) = 8 ), FORMAT ( [Comp Net Revenue], "Currency" ), AND ( MAX ( Header[HYear] ) = SELECTEDVALUE ( DYear[Year] ), MAX ( Header[Order] ) = 9 ), FORMAT ( [Net Rev % Var], "Percent" ) )We then set up the Matrix with the field fromt the Header Table as the columns, whatever dimension for the rows and the [For Matrix] measure as the values:
To get:So take your pick!
I've attached the sample PBIX file
Yes I get that. What I mean is that the sample data you posted is 2019 data, so there is no way of setting up a working example which will showcase year comparisons, which is what you originally requested
Like if they chose 2022 and 2021
| Billings 2022 | Billings 2021 | Var $ | Var % | Revenue 2022 | Revenue 2021 | Var$ | Var % |
| $2,214,728 | $2,428,931 | ($214,203) | -8.8% | $161,881 | $169,822 | ($7,940) | -4.7% |
| $2,166,979 | $2,670,534 | ($503,555) | -18.9% | $158,611 | $162,008 | ($3,397) | -2.1% |
| $2,118,540 | $1,909,052 | $209,488 | 11.0% | $128,230 | $128,157 | $73 | 0.1% |
| $1,199,792 | $1,491,702 | ($291,910) | -19.6% | $116,792 | $150,954 | ($34,161) | -22.6% |
| $1,464,133 | $1,097,197 | $366,936 | 33.4% | $104,524 | $78,612 | $25,911 | 33.0% |
| $688,568 | $693,241 | ($4,674) | -0.7% | $100,258 | $69,041 | $31,216 | 45.2% |
| $2,571,724 | $2,518,014 | $53,710 | 2.1% | $95,071 | $91,866 | $3,205 | 3.5% |
| $1,386,870 | $1,229,554 | $157,316 | 12.8% | $92,826 | $96,726 | ($3,900) | -4.0% |
| $310,772 | $939,260 | ($628,488) | -66.9% | $67,270 | $227,506 | ($160,236) | -70.4% |
| $563,914 | $681,810 | ($117,896) | -17.3% | $47,662 | $52,473 | ($4,811) | -9.2% |
| $110,789 | $87,714 | $23,075 | 26.3% | $45,665 | $39,549 | $6,116 | 15.5% |
| $244,178 | $228,595 | $15,583 | 6.8% | $44,483 | $44,968 | ($484) | -1.1% |
| $127,202 | $120,970 | $6,232 | 5.2% | $41,585 | $37,202 | $4,383 | 11.8% |
| $294,939 | $343,728 | ($48,789) | -14.2% | $36,720 | $38,968 | ($2,248) | -5.8% |