⚡️Power BI: Working with Visual Calculations (Part III: Matrix Tables with Square Numbers as Example)

Introduction

In the previous post I exemplified various operations that can be performed with visual calculations on simple tables based on square numbers. Changing the simple table to a matrix table doesn't bring any benefit. The real benefit comes when one restructures the table to store only a cell per row in a table. 

Data Modelling

For this the Magic5 table can be transformed via the following code, which creates a second table (e.g. M5):

M5 = UNION (
    SUMMARIZECOLUMNS(
     Magic5[Id]
     , Magic5[R]
     , Magic5[Index]
     , Magic5[C1]
     , "Col", "C1"
    )
    , SUMMARIZECOLUMNS(
     Magic5[Id]
     , Magic5[R]
     , Magic5[Index]
     , Magic5[C2]
     , "Col", "C2"
    )
    , SUMMARIZECOLUMNS(
     Magic5[Id]
     , Magic5[R]
     , Magic5[Index]
     , Magic5[C3]
     , "Col", "C3"
    )
    ,  SUMMARIZECOLUMNS(
     Magic5[Id]
     , Magic5[R]
     , Magic5[Index]
     , Magic5[C4]
     , "Col", "C4"
    )
    , SUMMARIZECOLUMNS(
      Magic5[Id]
     , Magic5[R]
     , Magic5[Index]
     , Magic5[C5]
     , "Col", "C5"
    )
)

Once this done, one can add the column [Col] as values for the matrix in a new visual. From now on, all the calculations can be done on copies of this visual. 

Simple Operations

The behavior of the RUNNINGSUM and other functions is different when applied on a matrix table because the formula is applied to every cell of the N*N table, a column with the result being added for each existing column of the matrix.

Moreover, there are four different ways of applying the formula based on the Axis used. ROW calculates the formula by the row within a column:

Run SumByRow(C) = RUNNINGSUM([C], ROWS)

Output:

R	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)
R1	18	18	25	25	2	2	9	9	11	11
R2	4	22	6	31	13	15	20	29	22	33
R3	15	37	17	48	24	39	1	30	8	41
R4	21	58	3	51	10	49	12	42	19	60
R5	7	65	14	65	16	65	23	65	5	65

By providing COLUMNS as parameter for the Axis makes the calculation run by the column within a row: 

Run SumByCol(C) = RUNNINGSUM([C], COLUMNS)

Output:

R	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)
R1	18	18	25	43	2	45	9	54	11	65
R2	4	4	6	10	13	23	20	43	22	65
R3	15	15	17	32	24	56	1	57	8	65
R4	21	21	3	24	10	34	12	46	19	65
R5	7	7	14	21	16	37	23	60	5	65

By providing ROW COLUMNS as parameter for the Axis makes the calculation run by the column and then continuing the next column (without resetting the value at the end of the column):

Run SumByRow-Col(C) = RUNNINGSUM([C],ROWS COLUMNS)

Output:

R	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)
R1	18	18	25	90	2	132	9	204	11	271
R2	4	22	6	96	13	145	20	224	22	293
R3	15	37	17	113	24	169	1	225	8	301
R4	21	58	3	116	10	179	12	237	19	320
R5	7	65	14	130	16	195	23	260	5	325

By providing COLUMNS ROWS as parameter for the Axis makes the calculation run by the row and then continuing the next row (without resetting the value at the end of the column):

Run SumByCol-Row = RUNNINGSUM([C],COLUMNS ROWS)

Output:

R	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)	C	Run Sum(C)
R1	18	18	25	43	2	45	9	54	11	65
R2	4	69	6	75	13	88	20	108	22	130
R3	15	145	17	162	24	186	1	187	8	195
R4	21	216	3	219	10	229	12	241	19	260
R5	7	267	14	281	16	297	23	320	5	325

Ranking

RANK can be applied independent of the values, or considering the value with ASC or DESC sorting:

RankByRow = RANK(DENSE,ROWS) -- ranking by row independent of values
RankByRow ASC = RANK(DENSE,ROWS, ORDERBY([C],ASC)) -- ranking by row ascending
RankByRow DESC = RANK(DENSE,ROWS, ORDERBY([C], DESC)) -- ranking by row descending
RankByRow-Col ASC = RANK(DENSE,ROWS COLUMNS, ORDERBY([C],ASC)) -- ranking by row columns ascending
RankByRow-Col DESC = RANK(DENSE,ROWS COLUMNS, ORDERBY([C], DESC)) -- ranking by row columns ascending

[RankByRow-Col ASC] matches the actual numbers from the matrix and is thus useful when sorting any numbers accordingly. 

Differences

Differences can be calculated between any of the cells of the matrix:

DiffToPrevByRow = [C] - PREVIOUS([C])  -- difference to previous record
DiffToPrevByRow* = IF(NOT(IsBlank(PREVIOUS([C]))), [C] - PREVIOUS([C])) -- extended difference to previous record
DiffToPrevByRow-Col = [C] - PREVIOUS([C],, ROWS COLUMNS) -- difference to previous record by ROWS COLUMNS
DiffToFirstByRow = [C] - FIRST([C]) -- difference to first record
DiffToPrevByCol = [C] - FIRST([C], COLUMNS) -- difference to previous record COLUMNS

Ranking = RANK(DENSE, ROWS COLUMNS, ORDERBY([C], ASC)) -- ranking of values by ROWS COLUMNS
OffsetDiffToPrevByRow = [C] - calculate([C], OFFSET(1, ROWS, ORDERBY([Ranking],DESC))) -- difference to the previous record by ROW
OffsetDiffToPrevByRow-Col = [C] - calculate([C], OFFSET(1, ROWS COLUMNS, ORDERBY([Ranking],DESC))) -- difference to the previous record by ROW

Ranking has been introduced to facilitate the calculations based on OFFSET.

The other functions [1] can be applied similarly.

Happy coding!

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References:
[1] Microsoft Learn (2024) Power BI: Using visual calculations [preview] (link)

⚡️Power BI: Working with Visual Calculations (Part II: Simple Tables with Square Numbers as Example) 🆕

Introduction

The records behind a visual can be mentally represented as a matrix, the visual calculations allowing to tap into this structure intuitively and simplify many of the visualizations used. After a general test drive of the functionality, it makes sense to dive deeper into the topic to understand more about the limitations, functions behavior and what it takes to fill the gaps. This post focuses on simple tables, following in a next post to focus on matrices and a few other topics. 

For exemplification, it makes sense to use a simple set of small numbers that are easy to work with, and magic squares seem to match this profile. A magic square is a matrix of positive sequential numbers in which each row, each column, and both main diagonals are the same [1]. Thus, a square of order N has N*N numbers from 1 to N*N, the non-trivial case being order 3. However, from the case of non-trivial squares, the one of order 5 provides a low order and allows hopefully the minimum needed for exemplification:

18	25	2	9	11
4	6	13	20	22
15	17	24	1	8
21	3	10	12	19
7	14	16	23	5
1	7	13	19	25

Data Modeling

One magic square should be enough to exemplify the various operations, though for testing purposes it makes sense to have a few more squares readily available. Each square has an [Id], [C1] to [C5] corresponds to matrix's columns, while [R] stores a row identifier which allows to sort the values the way they are stored in the matrix:

let
    Source = #table({"Id","C1","C2","C3","C4","C5","R"}
, {
{1,18,25,2,9,11,"R1"},
{1,4,6,13,20,22,"R2"},
{1,15,17,24,1,8,"R3"},
{1,21,3,10,12,19,"R4"},
{1,7,14,16,23,5,"R5"},
{2,1,7,13,19,25,"R1"},
{2,14,20,21,2,5,"R2"},
{2,22,3,9,15,16,"R3"},
{2,10,11,17,23,4,"R4"},
{2,18,24,5,6,12,"R5"},
{3,1,2,22,25,15,"R1"},
{3,9,10,16,11,19,"R2"},
{3,17,23,13,5,7,"R3"},
{3,24,12,6,20,3,"R4"},
{3,14,18,8,4,21,"R5"},
{4,22,6,3,18,16,"R1"},
{4,4,14,11,15,21,"R2"},
{4,5,8,12,23,17,"R3"},
{4,25,13,19,7,1,"R4"},
{4,9,24,20,2,10,"R5"},
{5,5,9,20,25,6,"R1"},
{5,13,15,2,11,24,"R2"},
{5,21,1,23,3,17,"R3"},
{5,19,18,4,14,10,"R4"},
{5,7,22,16,12,8,"R5"}
}
),
    #"Changed Type to Number" = Table.TransformColumnTypes(Source,{{"C1", Int64.Type}, {"C2", Int64.Type}, {"C3", Int64.Type}, {"C4", Int64.Type}, {"C5", Int64.Type}}),
    #"Sorted Rows" = Table.Sort(#"Changed Type to Number",{{"Id", Order.Ascending}, {"R", Order.Ascending}}),
    #"Added Index" = Table.AddIndexColumn(#"Sorted Rows", "Index", 0, 1, Int64.Type)
in
    #"Added Index"

The column names and the row identifier could have been numeric values from 1 to 5, though it could have been confounded with the actual numeric values.

In addition, the columns [C1] to [C5] were formatted as integers and an index was added after sorting the values after [Id] and [R]. Copy the above code as a Blank Query in Power BI and change the name to Magic5. 

Prerequisites

For the further steps you'll need to enable visual calculations in Power BI Developer via:
File >> Options and settings >> Options >> Preview features >> Visual calculations >> (check)

Into a Table visual drag and drop [R], [C1] to [C5] as column and make sure that the records are sorted ascending by [R]. To select only a square, add a filter based on the [Id] and select the first square. Use further copies of this visual for further tests. 

Some basic notions of Algebra are recommended but not a must. If you worked with formulas in Excel, then you are set to go. 

In Mathematics a matrix starts from the top left side and one moves on the rows (e.g. 18, 25, 2, ...) and then on the columns. With a few exceptions in which the reference is based on the latest value from a series (see Exchange rates), this is the direction that will be followed. 

Basic Operations

Same as in Excel [C1] + [C2] creates a third column in the matrix that stores the sum of the two. The sum can be further applies to all the columns:

Sum(C) = [C1] + [C2] + [C3] + [C4] + [C5] -- sum of all columns (should amount to 65)

The column can be called "Sum", "Sum(C)" or any other allowed unique name, though the names should be meaningful, useful, and succinct, when possible.

Similarly, one can work with constants, linear or nonlinear transformations (each formula is a distinct calculation):

constant = 1 -- constant value
linear = 2*[C1] + 1 -- linear translation: 2*x+1

linear2 = 2*[C1] + [constant] -- linear translation: 2*x+1
quadratic = Power([C1],2) + 2*[C1] + 1 -- quadratic translation: x^2+2*x+1
quadratic2 = Power([C1],2) + [linear] -- quadratic translation: x^2+2*x+1

Output:

R	C1	constant	linear	linear2	quadratic	quadratic2
R1	18	1	37	37	361	361
R2	4	1	9	9	25	25
R3	15	1	31	31	256	256
R4	21	1	43	43	484	484
R5	7	1	15	15	64	64

Please note that the output was duplicated in Excel (instead of making screenshots).

Similarly, can be build any type of formulas based on one or more columns.

With a simple trick, one can use DAX functions like SUMX, PRODUCTX, MINX or MAXX as well:

Sum2(C) = SUMX({[C1], [C2], [C3], [C4], [C5]}, [Value]) -- sum of all columns

Prod(C) = PRODUCTX({[C1], [C2], [C3], [C4], [C5]}, [Value]) -- product of all columns

Avg(C) = AVERAGEX({[C1], [C2], [C3], [C4], [C5]}, [Value]) -- average of all columns

Min(C) = MINX({[C1], [C2], [C3], [C4], [C5]}, [Value]) -- minimum value of all columns

Max(C) = MAXX({[C1], [C2], [C3], [C4], [C5]}, [Value]) -- maximum value of all columns
Count(C) = COUNTX({[C1], [C2], [C3], [C4], [C5]},[Value]) -- counts the number of columns

Output:

C1	C2	C3	C4	C5	Sum(C)	Avg(C)	Prod(C)	Min(C)	Max(C)	Count(C)
18	25	2	9	11	65	13	89100	2	25	5
4	6	13	20	22	65	13	137280	4	22	5
15	17	24	1	8	65	13	48960	1	24	5
21	3	10	12	19	65	13	143640	3	21	5
7	14	16	23	5	65	13	180320	5	23	5

Unfortunately, currently there seems to be no way available for applying such calculations without referencing the individual columns. 

Working across Rows

ROWNUMBER and RANK allow to rank a cell within a column independently, respectively dependently of its value:

Ranking = ROWNUMBER() -- returns the rank in the column (independently of the value)
RankA(C) = RANK(DENSE, ORDERBY([C1], ASC)) -- ranking of the value (ascending) 

RankD(C) = RANK(DENSE, ORDERBY([C1], DESC)) -- ranking of the value (descending) 

Output:

R	C1	Ranking	RankA(C)	RankD(C)
R1	18	1	4	2
R2	4	2	1	5
R3	15	3	3	3
R4	21	4	5	1
R5	7	5	2	4

PREVIOUS, NEXT, LAST and FIRST allow to refer to the values of other cells within the same column:

Prev(C) = PREVIOUS([C1]) -- previous cell
Next(C) = NEXT([C1])  -- next cell

First(C) = FIRST([C1]) -- first cell
Last(C) = LAST([C1]) -- last cell

Output:

R	C1	Prev(C)	NextC)	First(C)	Last(C)
R1	18		4	18	7
R2	4	18	15	18	7
R3	15	4	21	18	7
R4	21	15	7	18	7
R5	7	21		18	7

OFFSET is a generalization of these functions

offset(2) = calculate([C1], offset(2)) -- 
offset(-2) = calculate([C1], offset(-2))
Ind = ROWNUMBER() -- index
inverse = calculate([C1], offset(6-2*[Ind])) -- inversing the values based on index

Output:

R	C1	offset(2)	offset(-2)	ind	inverse
R1	18	15		1	7
R2	4	21		2	21
R3	15	7	18	3	15
R4	21		4	4	4
R5	7		15	5	18

The same functions allow to calculate the differences for consecutive values:

DiffToPrev(C) = [C1] - PREVIOUS([C1]) -- difference to previous 
DiffToNext(C) = [C1] - PREVIOUS([C1]) -- difference to next 
DiffTtoFirst(C) = [C1] - FIRST([C1]) -- difference to first
DiffToLast(C) = [C1] - LAST([C1]) -- difference to last

Output:

R	C1	DiffToPrev(C)	DiffToNextC)	DiffToFirst(C)	DiffToLast(C)
R1	18	18	14	0	11
R2	4	-14	-11	-14	-3
R3	15	11	-6	-3	8
R4	21	6	14	3	14
R5	7	-14	7	-11	0

DAX makes available several functions for working across the rows of the same column. Two of the useful functions are RUNNINGSUM and MOVINGAVERAGE:

Run Sum(C) = RUNNINGSUM([C1]) -- running sum

Moving Avg3(C) = MOVINGAVERAGE([C1], 3) -- moving average for the past 3 values

Moving Avg2(C) = MOVINGAVERAGE([C1], 2) -- moving average for the past 2 values

Unfortunately, one can use only the default sorting of the table with the functions that don't support the ORDERBY parameter. Therefore, when the table needs to be sorted descending and the RUNNINGSUM calculated ascending, for the moment there's no solution to achieve this behavior. However, it appears that Microsoft is planning to implement a solution for this issue.

RUNNINGSUM together with ROWNUMBER can be used to calculate a running average:

Run Avg(C) = DIVIDE(RUNNINGSUM([C1]), ROWNUMBER()) -- running average

Output:

R	C1	Run Sum(C)	Moving Avg3(C)	Moving Avg2(C)	Run Avg(C)
R1	18	18	18	18	18
R2	4	22	11	11	11
R3	15	37	12.33	9.5	12.33
R4	21	58	13.33	18	14.5
R5	7	65	14.33	14	13

With a mathematical trick that allows to transform a product into a sum of elements by applying the Exp (exponential) and Log (logarithm) functions (see the solution in SQL), one can run the PRODUCT across rows, though the values must be small enough to allow their multiplication without running into issues:

Ln(C) = IFERROR(LN([C1]), Blank()) -- applying the natural logarithm
Sum(Ln(C)) = RUNNINGSUM([Ln(C)]) -- running sum
Run Prod(C) = IF(NOT(ISBLANK([Sum(Ln(C))])), Exp([Sum(Ln(C))])) -- product across rows

Output:

R	C1	Ln(C)	Sum(Ln(C))	Run Prod(C)
R1	18	2.89	2.89	18
R2	4	1.39	4.28	72
R3	15	2.71	6.98	1080
R4	21	3.04	10.03	22680
R5	7	1.95	11.98	158760

These three calculations could be brought into a single formula, though the result could be more difficult to troubleshoot. The test via IsBlank is necessary because otherwise the exponential for the total raises an error. 

Considering that when traversing a column it's enough to remember the previous value, one can build MIN and MAX functionality across a column: 

Run Min = IF(OR(Previous([C1]) > [C1], IsBlank(Previous([C1]))), [C1], Previous([C1])) -- minimum value across rows
Run Max = IF(OR(Previous([C1]) < [C1], IsBlank(Previous([C1]))), [C1], Previous([C1])) -- maximum across rows

Happy coding!

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References:
[1] Wikipedia (2024) Magic Squares (online)
[2] Microsoft Learn (2024) Power BI: Using visual calculations [preview] (link)

📊Graphical Representation: Graphics We Live By (Part III: Exchange Rates in Power BI)

Graphical Representation Series

An exchange rate (XR) is the rate at which one currency will be exchanged for another currency, and thus XRs are used in everything related to trades, several processes in Finance relying on them. There are various sources for the XR like the European Central Bank (ECB) that provide the row data and various analyses including graphical representations varying in complexity. Conversely, XRs' processing offers some opportunities for learning techniques for data visualization. 

On ECB there are monthly, yearly, daily and biannually XRs from EUR to the various currencies which by triangulation allow to create XRs for any of the currencies involved. If N currencies are involved for one time unit in the process (e.g. N-1 XRs) , the triangulation generates NxN values for only one time division, the result being tedious to navigate. A matrix like the one below facilitates identifying the value between any of the currencies:

The table needs to be multiplied by 12, the number of months, respectively by the number of years, and filter allowing to navigate the data as needed. For many operations is just needed to look use the EX for a given time division. There are however operations in which is needed to have a deeper understanding of one or more XR's evolution over time (e.g. GBP to NOK). 

Moreover, for some operations is enough to work with two decimals, while for others one needs to use up to 6 or even more decimals for each XR. Occasionally, one can compromise and use 3 decimals, which should be enough for most of the scenarios. Making sense of such numbers is not easy for most of us, especially when is needed to compare at first sight values across multiple columns. Summary tables can help:

Statistics like Min. (minimum), Max. (maximum), Max. - Min. (range), Avg. (average) or even StdDev. (standard deviation) can provide some basis for further analysis, while sparklines are ideal for showing trends over a time interval (e.g. months).

Usually, a heatmap helps to some degree to navigate the data, especially when there's a plot associated with it:

In this case filtering by column in the heatmap allows to see how an XR changed for the same month over the years, while the trendline allows to identify the overall tendency (which is sensitive to the number of years considered). Showing tendencies or patterns for the same month over several years complements the yearly perspective shown via sparklines.

Fortunately, there are techniques to reduce the representational complexity of such numbers. For example, one can use as basis the XRs for January (see Base Jan), and represent the other XRs only as differences from the respective XR. Thus, in the below table for February is shown the XR difference between February and January (13.32-13.22=0.10). The column for January is zero and could be omitted, though it can still be useful in further calculations (e.g. in the calculation of averages) based on the respective data..

This technique works when the variations are relatively small (e.g. the values vary around 0). The above plots show the respective differences for the whole year, respectively only for four months. Given a bigger sequence (e.g. 24, 28 months) one can attempt to use the same technique, though there's a point beyond which it becomes difficult to make sense of the results. One can also use the year end XR or even the yearly average for the same, though it adds unnecessary complexity to the calculations when the values for the whole year aren't available. 

Usually, it's recommended to show only 3-5 series in a plot, as one can better distinguish the trends. However, plotting all series allows to grasp the overall pattern, if any. Thus, in the first plot is not important to identify the individual series but to see their tendencies. The two perspectives can be aggregated into one plot obtained by applying different filtering. 

Of course, a similar perspective can be obtained by looking at the whole XRs:

The Max.-Min. and StdDev (standard deviation for population) between the last and previous tables must match. 

Certain operations require comparing the trends of two currencies. The first plot shows the evolution NOK and SEK in respect to EUR, while the second shows only the differences between the two XRs:

The first plot will show different values when performed against other currency (e.g. USD), however the second plot will look similarly, even if the points deviate slightly:

Another important difference is the one between monthly and yearly XRs, difference depicted by the below plot:

The value differences between the two XR types can have considerable impact on reporting. Therefore, one must reflect in analyses the rate type used in the actual process. 

Attempting to project data into the future can require complex techniques, however, sometimes is enough to highlight a probable area, which depends also on the confidence interval (e.g. 85%) and the forecast length (e.g. 10 months):

Every perspective into the data tends to provide something new that helps in sense-making. For some users the first table with flexible filtering (e.g. time unit, currency type, currency from/to) is enough, while for others multiple perspectives are needed. When possible, one should  allow users to explore the various perspectives and use the feedback to remove or even add more perspectives. Including a feedback loop in graphical representation is important not only for tailoring the visuals to users' needs but also for managing their expectations,  respectively of learning what works and what doesn't.

Comments:
1) I used GBP to NOK XRs to provide an example based on  triangulation.
2) Some experts advise against using borders or grid lines. Borders, as the name indicates allow to delimitate between various areas, while grid lines allow to make comparisons within a section without needing to sway between broader areas, adding thus precision to our senses-making. Choosing grey as color for the elements from the background minimizes the overhead for coping with more information while allowing to better use the available space.
3) Trend lines are recommended where the number of points is relatively small and only one series is involved, though, as always, there are exceptions too. 
4) In heatmaps one can use a gradient between two colors to show the tendencies of moving toward an extreme or another. One should avoid colors like red or green.
5) Ideally, a color should be used for only one encoding (e.g. one color for the same month across all graphics), though the more elements need to be encoded, the more difficult it becomes to respect this rule. The above graphics might slightly deviate from this as the purpose is to show a representation technique. 
6) In some graphics the XRs are displayed only with two decimals because currently the technique used (visual calculations) doesn't support formatting.
7) All the above graphical elements are based on a Power BI solution. Unfortunately, the tool has its representational limitations, especially when one wants to add additional information into the plots. 
8) Unfortunately, the daily XR values are not easily available from the same source. There are special scenarios for which a daily, hourly or even minute-based analysis is needed.
9) It's a good idea to validate the results against the similar results available on the web (see the ECB website).

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⚡️🗒️Power BI: Visual Calculations [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources for learning purposes. For the latest information please consult the documentation (see the links below)! 

Last updated: 4-Jul-2026

[feature] Visual Calculations (aka Visual Calcs)

• {definition} a type of DAX calculation that's defined and executed in the scope of a visual [1] [5]
• are new columns added to this virtual table of filtered and aggregated data, which is not directly connected to the rest of the semantic model [5]
• {benefit} make it easier to create calculations (that were previously hard to create) 
• leads to simpler DAX, easier maintenance, and better performance [1]
• reuse the results from its components [2]
• simpler than measures, more trustworthy than Excel [5]
• {benefit} the visual context in which they operate not only describes what data is on the visual (which is then iterated over in a row context) but also the structure of the visual 
• ⇒a visual calculation can refer to the axes of a visual, such as the x-axis or the y-axis instead of the actual field [5]
• ⇐ enables visual calculations to be highly flexible [5]
• ⇐ a visual calculation can continue to work even if the field that is on the x-axis changes by simply referring to the axis instead of the actual field [5]
• {benefit} highly visual [5] always show the data the calculation works on, and tools are provided to easily validate the results [5]
• {benefit} should perform better than measures 
• ⇐because they’re executed as part of the DAX query to fetch the results instead of independently [5]
• a single query is sent to the source 
• ⇐ no such guarantee exists for analysis that relies on measures [5] 
• ⇐ how pronounced the effects are depends on the size of your data and the complexity of the DAX used [5]
• defined and evaluated only on the filtered and aggregated data that is visible in the visual and thus in the visual matrix in which the visual calculation is created [5] aka calculations table that’s defined in the DAX query [5]
• evaluate at runtime (like measures do) 
• ⇒they can be fully dynamic in the context of the visual [5]
• {limitation} unaware of the semantic model (as they are not part of the semantic model) [5]
• ⇒ DAX functions that perform actions on that model don’t work (return an error) [5]
•  e. g. RELATED, USERELATIONSHIP
• {limitation} can’t be reused across multiple reports [5] 
• ⇒ leads to code duplication [5]
• <-- only live on the visual, which will avoid cluttering of the semantic model [5]
• they’re saved in the metadata of the report, inside the visual definition. [5]
• can only be used inside one report and that definitions cannot be shared across multiple reports that share the same semantic model [5]
• (limitation) do not work together with the 'show items with no data' option, because it would result in performance issues.[5]
• {recommended} create measures for any key calculations that should be reused across multiple reports [5]
• {default} executed on a row-by-row basis, much like a calculated column [5]
• calculated on the fly, like a measure [5]
• are stored on the visual
• ⇒aren’t part of the semantic model [5]
• ⇒can refer to any data in the visual 
• incl. columns, measures, or other visual calculations [1]
• ⇒ anything in the model must be added to the visual before the visual calculation can refer to it [1]
• ⇒ no need to worry about filter context [5]
• the filter context dictates what the measures and fields on the visual return, and the visual calculation takes those values as input for its evaluation [5]
• ⇐ a visual calculation is only indirectly affected by filter context, not directly, the way a measure or field reference is [5]
•  ⇒ they can refer to the visual structure
• ⇒ leads to more flexibility
• combine the simplicity of context from calculated columns with the on-demand calculation flexibility from measures [1]
• ⇐ the context is 'visible'
• share behaviors with calculated columns and measures but also have important differences, particularly in how they can be used, where they are stored, and when they are computed [5]
• operate on aggregated data instead of the detail level [1]
• ⇒ leads to performance benefits
• introduce a set of functions specific to visual calculations [1]
• {category} medium-level functions
• {function} COLLAPSE
• the calculation is evaluated at a higher level of the axis [1]
• navigate to a higher level in the lattice formed by the fields on the axes of the visual matrix [5] 
• most often used for percentage of parent, grandparent, and total calculations [5]
• {function} COLLAPSEALL
• the calculation is evaluated at the total level of the axis [1]
• does not take extra parameters because it always moves to the highest level on the axis [5] navigate to the highest level of the lattice on the axis specified [5]
• {function} EXPAND
• the calculation is evaluated at a lower level of the axis [1]
• navigate to a lower level in the lattice formed by the fields on the axes of the visual matrix [5]
• often used for aggregated descendant averages [5]
• the reverse of COLLAPSE
• {function}EXPANDALL
• the calculation is evaluated at the leaf level of the axis [1]
• always moves down to the lowest level (leaf level) on the axis and doesn’t take these parameters [5]
• the reverse of COLLAPSEALL [5]
• {function} FIRST
• retrieves a value from the first element on a specified axis [5]
• often used to compare against a base period or entity [5]
• retrieves the value of the column in the first element on the specified axis since the last time the calculation was reset [5]
• an easier-to-use shortcut to the INDEX function with the position parameter set to 1 [5]
• {function} ISATLEVEL
• checks whether specified columns are on the current level of the axis [5]
• returns a Boolean (True/False) value
• guaranteed to work correctly in visual calculations with functions that navigate the levels of the lattice [5]
• unlike ISINSCOPE, ISFILTERED inspection functions [5]
• {function} LAST
• refers to the last row of an axis [1]
• retrieves a value from the last element on a specified axis [5]
• the reverse of FIRST
• often used to compare against the most recent entry [5]
• {function} LOOKUP
• evaluates an expression with a value from a cell in the provided visual matrix using filters [5]
• anything that is not specified is inferred from the context [5]
• often used to compare against a specific value in the visual matrix [5]
• {function} LOOKUPWITHTOTALS
• uses the total for any filter that is not specified [5]
• returns the value of the expression provided at the specified coordinates after filters have been applied [5]
• any filter that is not specified is treated as referring to the total
• if no single value can be determined, an error is returned [5]
• ⇐ instead of using the context to infer any filter that is not specified like LOOKUP does [5]
• relies on absolute navigation in the visual matrix [5]
• ⇐ COLLAPSE and COLLAPSEALL rely on relative navigation on the lattice [5]
• ⇐ upon case can be used to retrieve same result [5]
• {function} NEXT
• retrieves a value from a next element on a specified axis [5]
• retrieves the value of the column from a next element on the specified axis since the last time the calculation was reset [5]
• provides a shortcut to the OFFSET function with a positive value provided for the delta parameter [5]
• {function} PREVIOUS
• retrieves a value from a previous element on a specified axis [5]
• retrieves the value of the column from an earlier element on the specified axis since the last time the calculation was reset [5]
• provides a shortcut to the OFFSET function with a negative value provided for the delta parameter [5]
• {function} RANGE
• provides a range of rows relative from the current position on the axis [5]
• shortcut to WINDOW 
• returns a context 
• ⇒ it must be used with other functions, such as CALCULATE, to actually perform a calculation [5]
• often employed to calculate a moving sum [5]
• {category} high-level functions
• {function} MOVINGAVERAGE
• adds a moving average on an axis [1]
• involves selecting a slice of the values on an axis and returning the average over that slice [5]
• most often used to calculate averages across periods [5]
• provides an easier-to-use shortcut to the WINDOW function [5]
• {function} RUNNINGSUM
• returns the sum of all values in a column on the axis since the last time the calculation was reset, up to and including the current value [5]
• if no reset is defined, RUNNINGSUM starts at the top of the visual matrix and continues to the end, following the sort order [5]
• created specifically for visual calculations [5]
• often used for Pareto analysis [5]
• provides an easier-to-use shortcut to the WINDOW function [5]
• it’s possible to write a running sum in a measure, though the code becomes more complex to write [5] 
• ⇐ include explicit references to columns on which the calculation works [5]
• ⇒if the user changes the columns on the visual, the measure will return unexpected results and will have to be updated to reflect the changes [5]
• {category} low-level functions
• ⇐ {exception} are available in standard DAX
• these functions are easier to use, though they are less flexible than their foundational counterparts [5]
• {recommendation} rewrite visual calculation using DAX for more flexibility [5]
• {recommendation} start with the easier-to-use visual calculations exclusive functions and resort to other functions only when needed [5]
• {category}foundational functions
• {parameter} relation 
• table expression that defines from which output a value is returned, namely a table expression [5]
• name of a table or a DAX statement that returns a table, such as ADDCOLUMNS or SUMMARIZECOLUMNS
• any columns specified in the partitionBy parameter must come from the relation parameter or from a related table [5]
• {parameter} orderBy
• specifies how each partition on the relation or axis is sorted [5]
• accepts only the ORDERBY function
• partitions are defined by using either partitionBy or reset [5]
• {default} ordering by every column that is in the relation or on the axis that is not specified in partitionBy or reset [5]
• {parameter} blanks
• specify how blank values on the axis should be ordered while the calculation traverses the axis (in a visual calculation) or the relation [5]
• does not sort anything in the values of the visual matrix (e.g. visual calculations or measures) [5] it sorts the values of the fields on the axis used [5]
• {value} DEFAULT
• indicates that blank numerical values are ordered between zero and negative values. For blank textual values, the blank values are ordered before all text values, including empty text values [5]
• {value} FIRST 
• blank values are always ordered at the beginning, regardless of ascending or descending sorting order [5]
• {value] LAST 
• blank values are always ordered at the end, regardless of ascending or descending sorting order [5]
• {parameter} partitionBy
• specifies how the relation or axis is partitioned [5]
• accepts the PARTITIONBY function [5]
• {recommendation} use reset instead of partitionBy  
• ⇐ it’s the easiest way of achieving the same result [5]
• {parameter} matchBy
• defines how to match data to identify unique rows [5]
• use this parameter if you do not have anything that can uniquely identify the rows in your relation (aka composite key) [5]
• when using axis in visual calculations, there's not need to use matchBy in visual calculations because the axis always has unique identifiers [5]
• if no matchBy is specified and the columns in orderBy and partitionBy cannot uniquely identify every row in the relation, the foundational function will try to find the least number of additional columns required to uniquely identify the rows and append these to the orderBy value (even if you did not specify orderBy) [5] 
• {parameter} resets
• accepts the MATCHBY function
• available only for visual calculations
• the calculation is reset by dividing the data on the axis into slices, the same way partitionBy does [5]
• {limitation} one cannot specify both reset and partitionBy
• one can think of the reset parameter as being mapped to the partitionBy parameter, however reset automatically includes parent levels and partitionBy does not [5]
• {function} INDEX
• returns a row in an absolute position
• position parameter defines the absolute position on the relation or axis from which to obtain the data.
• {function} OFFSET
• returns a row in a relative position
• {parameter} delta
• specifies the relative position on the relation or axis from which to obtain the data [5]
• when specifying a delta that causes a relative movement that does not exist on the partition, when specifying  0 or BLANK(), then OFFSET will not perform a relative movement, and the context is set to the current row [5]
• any DAX expression that returns a scalar value is valid [5]
• {function} RANK
• provides a ranking of each row within a partition, sorted by the specified sort order [5]
• returns a blank value for total rows when used in measures but returns a value on those rows when used in visual calculations [5]
• expects a table and applies an expression to rank the rows in the table [5]
• {warning} it's not the column-based version of RANKX [5]
• {parameter} ties parameter
• {value|default} SKIP
• if two rows end up with the same rank, they will both be assigned the same rank and the next rank number will be skipped [5]
• {value}DENSE
• the next rank number will not be skipped [5]
• {function} ROWNUMBER
• returns a unique ranking of each row within a partition, sorted by the specified sort order [5]
• returns an error if it cannot uniquely identify each row [5]
• ⇒ guarantees that the same number will never be assigned twice [5]
• {function}WINDOW
• returns multiple rows, which are positioned at a selectable absolute or relative interval [5]
• {parameter} from
• indicate where the window start
• {parameter} to
• indicate where the window ends
• {parameter} from_type
• specifies whether the window starts at either 
• {default} relative position (REL)
• then a negative value provided for this parameter specifies the number of rows to go back from the current position to get the first (or last) row in the window [5]
• absolute position (ABS) [4]
• indicate the 1-based absolute position in the current partition of the start and end of the window [4]
• 1= first row, -1 = last row
• {parameter} to_type
• see from_type
• {category} supportive functions
• used as inputs to specific foundational function parameters with the same name [5]
• on their own, these functions provide no value [5]
• {recommendation) specify an axis value for the relation parameter and use reset as needed when using foundational functions in visual calculations [5]
• {function} ORDERBY
• define the sorting order within each partition of the relation or axis on which the function operates [5]
• {function} PARTITIONBY
• indicates if and how to slice up the data in the relation or axis on which the foundational function operates [5]
• it can be skept if no slices are defined [5]
• {function} MATCHBY
• instruct DAX on how to determine the current row [5]
• {category} shared functions
• DAX functions which are shared across all experiences [5]
• {category} exclusive functions 
• functions introduced in DAX solely for visual calculations [5]
• {category} blocked functions 
• functions that reach out to the model [5]
• {default} most of them are evaluated row-by-row [1]
• ⇐ like a calculated column
• there's no need to add an aggregation function [1]
•  it's better not to add such aggregates when they're not necessary [1]
• {operation} create calculation
• adds the visual calculation to the visual
• it's possible to create visual calculations directly in the service [5]
• allows to create very complex visual calculations in steps and hide any irrelevant intermediate results [5]
• creation is not traced as activity in audit logs but is covered in a generic activity named Update Report Content [5]
• {operation} hide calculation
• calculations that aren't needed in the visual can be hidden [2]
• hidden fields enable users to hide elements from the visual [5]
• {operation} copy calculation
• copies the calculation between visuals and if intermediary steps are not there, they will be copied as well [planned] [2]
• {operation} formatting 
• ⇐do not take on the format of any measures used to create them [5]
• {operation} view a visual as a visual matrix 
• enables users to add more calculations, which can be seen as new columns in the visual matrix [5]
• {feature} templates 
• ready available calculation constructs 
• {benefit} make it easier to write common calculations [1]
• {feature|planned} support for Scanner API [2]
• {feature} explore
• new experience that allows to explore data in a focused way [4]
• allows adding visual calculations to visuals [4]
• {feature} parameter pickers
• allows to create visual calculations faster by picking parameters [3]
• {limitation} only available for required parameters on functions that are exclusive to visual calculations (and select other functions) that have a defined list of options [3]
•  required parameters that can take any text, or numerical value will not get a parameter picker, and neither will many DAX functions [3]
• {feature} visual preview 
• shows users what the visual will look like when leaving the visual calculations edit mode and returning to the report [5]
• allows users to see what impact newly added visual calculations have on the visual, and how the visual will look like if certain measures or visual calculations are hidden [5]
• {feature} visual matrix
• the data representation of your visual 
• ⇐ shows you the outcomes of all newly added calculations [5] 
• ⇐ the simplest representation of the data used to create the visual [5]
• offers a way to structure data dynamically based on rows and columns in a WYSIWYG fashion [5]
• doesn't display any formatting that may be applied to the visual itself [5]
• every value in a visual calculation must exist in the visual matrix
• ⇒both the original value residing in the model and the visual calculation will be shown in the resulting visual [5]
• {feature} formula bar 
• allows to write and edit visual calculations [5]
• {parameter} axis 
• influences how the visual calculation traverses the visual matrix [1] 
• ⇐ defines the direction in which the running sum should be calculate [5]
• can be seen as the axis of a chart, which has an x-axis and a y-axis [5]
• not available in measures, calculated columns, or calculated tables [5]
• defines the direction in which the running sum should be calculated: over rows, columns, or a combination [5]
• {default} set to the first axis in the visual
• {value} ROWS
• the visual calculation is evaluated row-by-row in the visual matrix, from top to bottom. [1]
• {value} COLUMNS
• the visual calculation is evaluated row-by-row in the visual matrix, from left to right [1]
• {value} ROWS COLUMNS
• calculates vertically across rows from top to bottom, continuing column by column from left to right [1]
• {value} COLUMNS ROWS
• calculates horizontally across columns from left to right, continuing row by row from top to bottom [1]
• {warning} not all visuals provide all axes, and some visuals provide no axes [1]
• references to a non-existent or invalid axis is permissible and will be ignored [3]
• {parameter} reset 
• influences if and when the function resets its value to 0 or switches to a different scope while traversing the visual matrix [1]
• expects there to be multiple levels on the axis [1]
• ⇐ use PARTITIONBY if there's only one level on the axis [1]
• {value|default} NONE
• means the visual calculation is never restarted [1]
• {value} HIGHESTPARENT 
• resets the calculation when the value of the highest parent on the axis changes [1]
• {value} LOWESTPARENT 
• resets the calculations when the value of the lowest parent on the axis changes [1]
• {value} numerical value
• refers to the fields on the axis, with the highest field being one [1]
• {concept} lattice
• formed by all the fields on all the axes [5]
• visual calculations calculate results on various levels on the lattice [5]
• lattice navigation functions allow users to explicitly move around in the lattice [5]
• e.g. COLLAPSE, COLLAPSEALL, EXPAND, EXPANDALL
• {default} the data type of a visual calculation is decimal number [5]
• {concept} format strings
• allow for a more fine-grained level of formatting
• {feature} traceability 
• prevents redundancy and conflicting definitions by identifying inconsistent calculations [5] 
• using audit trails and versioning systems further enhances accountability and enables swift corrections when needed [5]
• {limitation} functions that rely on model relationships  aren't available
• e.g. USERELATIONSHIP, RELATED or RELATEDTABLE
• {limitation} not all visual types are supported [1]
• ⇐ for the full list of limitations see [1]
• {limitation} one can't filter on visual calculations [1]
• {limitation} underlying data can't be exported [1]
• {limitation} don't support conditional formatting
• {concept} skippable parameters
• introduced with visual calculations [5]
• allow for cleaner code because users can simply omit any unnecessary optional parameters [5]
• ⇐ unlike with DAX functions that do not support skippable parameters [5]
• {concept} telemetry 
• the collection and analysis of data to monitor, measure, and optimize system performance[5]
• can provide valuable insights into the usage patterns, dependencies, and performance of visual calculations [5]
• allows administrators can identify discrepancies and ensure that visual calculations align with organizational standards [5]

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References: 
[1] Microsoft Learn (2024) Power BI: Using visual calculations [preview] [link]
[2] SSBI Central (2024) Visual Calculations - Making DAX easier, with Jeroen ter Heerdt [link]
[3] Microsoft Power BI Updates (2025) Power BI June 2025 Feature Summary [link]
[4] Microsoft Learn (2025) Power BI: Use Explore (preview) in the Power BI service [link]

[5] Jeroen ter Heerdt et al (2026) Microsoft Power BI Visual Calculations: Simplifying DAX