📊Graphical Representation: Graphics We Live By (Part VIII: List of Items in Power BI)

Graphical Representation Series

Introduction

There are situations in which one needs to visualize only the rating, other values, or ranking of a list of items (e.g. shopping cart, survey items) on a scale (e.g. 1 to 100, 1 to 10) for a given dimension (e.g. country, department). Besides tables, in Power BI there are 3 main visuals that can be used for this purpose: the clustered bar chart, the line chart (aka line graph), respectively the slopegraph:

Main Display Methods

Main Display Methods

For a small list of items and dimension values probably the best choice would be to use a clustered bar chart (see A). If the chart is big enough, one can display also the values as above. However, the more items in the list, respectively values in the dimension, the more space is needed. One can maybe focus then only on a subset of items from the list (e.g. by grouping several items under a category), respectively choose which dimension values to consider. Another important downside of this method is that one needs to remember the color encodings. 

This downside applies also to the next method - the use of a line chart (see B) with categorical data, however applying labels to each line simplifies its navigation and decoding. With line charts the audience can directly see the order of the items, the local and general trends. Moreover, a line chart can better scale with the number of items and dimension values.

The third option (see C), the slopegraph, looks like a line chart though it focuses only on two dimension values (points) and categorizes the line as "down" (downward slope), "neutral" (no change) and "up" (upward slope). For this purpose, one can use parameters fields with measures. Unfortunately, the slopegraph implementation is pretty basic and the labels overlap which makes the graph more difficult to read. Probably, with the new set of changes planned by Microsoft, the use of conditional formatting of lines would allow to implement slope graphs with line charts, creating thus a mix between (B) and (C).

This is one of the cases in which the Y-axis (see B and C) could be broken and start with the meaningful values. 

Table Based Displays

Especially when combined with color encodings (see C & G) to create heatmap-like displays or sparklines (see E), tables can provide an alternative navigation of the same data. The color encodings allow to identify the areas of focus (low, average, or high values), while the sparklines allow to show inline the trends. Ideally, it should be possible to combine the two displays.  

Table Displays and the Aster Plot

One can vary the use of tables. For example, one can display only the deviations from one of the data series (see F), where the values for the other countries are based on AUS. In (G), with the help of visual calculations one can also display values' ranking. 

Pie Charts

Pie charts and their variations appear nowadays almost everywhere. The Aster plot is a variation of the pie charts in which the values are encoded in the height of the pieces. This method was considered because the data used above were encoded in 4 similar plots. Unfortunately, the settings available in Power BI are quite basic - it's not possible to use gradient colors or link the labels as below:

Source Data as Aster Plots

Sankey Diagram

A Sankey diagram is a data visualization method that emphasizes the flow or change from one state (the source) to another (the destination). In theory it could be used to map the items to the dimensions and encode the values in the width of the lines (see I). Unfortunately, the diagram becomes challenging to read because all the lines and most of the labels intersect. Probably this could be solved with more flexible formatting and a rework of the algorithm used for the display of the labels (e.g. align the labels for AUS to the left, while the ones for CAN to the right).

Sankey Diagram

Data Preparation

A variation of the above image with the Aster Plots which contains only the plots was used in ChatGPT to generate the basis data as a table via the following prompts:

• retrieve the labels from the four charts by country and value in a table
• consolidate the values in a matrix table by label country and value

The first step generated 4 tables, which were consolidated in a matrix table in the second step. Frankly, the data generated in the first step should have been enough because using the matrix table required an additional step in DAX.

Here is the data imported in Power BI as the Industries query:

let
    Source = #table({"Label","Australia","Canada","U.S.","Japan"}
, {
 {"Credit card","67","64","66","68"}
, {"Online retail","55","57","48","53"}
, {"Banking","58","53","57","48"}
, {"Mobile phone","62","55","44","48"}
, {"Social media","74","72","62","47"}
, {"Search engine","66","64","56","42"}
, {"Government","52","52","58","39"}
, {"Health insurance","44","48","50","36"}
, {"Media","52","50","39","23"}
, {"Retail store","44","40","33","23"}
, {"Car manufacturing","29","29","26","20"}
, {"Airline/hotel","35","37","29","16"}
, {"Branded manufacturing","36","33","25","16"}
, {"Loyalty program","45","41","32","12"}
, {"Cable","40","39","29","9"}
}
),
    #"Changed Types" = Table.TransformColumnTypes(Source,{{"Australia", Int64.Type}, {"Canada", Int64.Type}, {"U.S.", Number.Type}, {"Japan", Number.Type}})
in
    #"Changed Types"

Transforming (unpivoting) the matrix to a table with the values by country:

IndustriesT = UNION (
    SUMMARIZECOLUMNS(
     Industries[Label]
     , Industries[Australia]
     , "Country", "Australia"
    )
    , SUMMARIZECOLUMNS(
     Industries[Label]
     , Industries[Canada]
     , "Country", "Canada"
    )
    , SUMMARIZECOLUMNS(
     Industries[Label]
     , Industries[U.S.]
     , "Country", "U.S."
    )
    ,  SUMMARIZECOLUMNS(
     Industries[Label]
     , Industries[Japan]
     , "Country", "Japan"
    )
)

Notes:

The slopechart from MAQ Software requires several R language libraries to be installed (see how to install the R language and optionally the RStudio). Run the following scripts, then reopen Power BI Desktop and enable running visual's scripts.

install.packages("XML")
install.packages("htmlwidgets")
install.packages("ggplot2")
install.packages("plotly")

Happy (de)coding!

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⚡️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)

⚡️🗒️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 

⚡️🗒️Power BI: Working with Visual Calculations (Part I: Test Drive) 🆕

Introduction

I recently watched a webcast with Jeroen (Jay) ter Heerdt (see [2]) in which he introduces visual calculations, a type of DAX calculation that's defined and executed directly on a visual [1]. Visual calculations provide an approach of treating a set of data much like an Excel table, allowing to refer to any field available on a visual and write formulas, which simplifies considerably the solutions used currently for ranking records, running averages and other windowing functions. 

The records behind a visual can be mentally represented as a matrix, while the visual calculations can refer to any column from the matrix, allowing to add new columns and include the respective columns in further calculations. Moreover, if a column is used in a formula, it's not recalculated as is the case of measures, which should improve the performance of DAX formulas considerably. 

Currently, one can copy a formula between visuals and if the formula contains fields not available in the targeted visual, they are added as well. Conversely, it's possible to build such a visual, copy it and then replace the dimension on which the analysis is made (e.g. Customer with Product), without being needed to make further changes. Unfortunately, there are also downsides: (1) the calculations are visible only within the visual in which were defined; (2) currently, the visual's data can't be exported if a visual calculation is added; (3) no formatting is supported, etc.

Ranking and Differences

I started to build a solution based on publicly available sales data, which offers a good basis for testing the use of visual calculations. Based on a Power BI visual table made of [Customer Name], [Sales Amount], [Revenue] and [Total Discount], I've added several calculations:

-- percentages
Sales % = 100*DIVIDE([Sales Amount], COLLAPSE([Sales Amount], ROWS))
Revenue % = 100*DIVIDE([Revenue],[Sales Amount])
Discount % = 100*DIVIDE([Total  Discount], [Total  Discount]+[Sales Amount])

-- rankings 
Rank Sales = Rank(DENSE, ORDERBY([Sales Amount], DESC))
Rank Revenue = Rank(DENSE, ORDERBY([Revenue], DESC))

-- differences between consecutive values
Diff. to Prev. Sales = IF([Rank Sales]>1, INDEX([Rank Sales]-1, , ORDERBY([Sales Amount], DESC)) - [Sales Amount] , BLANK())
Diff. to Prev. Rev. = IF([Rank Revenue]>1, INDEX([Rank Revenue]-1, , ORDERBY([Revenue], DESC)) - [Revenue] , BLANK())

Here's the output considered only for the first 10 records sorted by [Sales Amount]:

Customer Name	Sales Amount	Sales %	Revenue	Revenue %	Total Discount	Discount %	Rank Sales	Diff. to Prev. Sales.	Rank Rev.	Diff. to Prev. Rev.
Medline	1058923.78	3.76	307761.99	3.75	126601.02	10.68	1		1
Ei	707663.21	2.51	229866.98	2.8	95124.09	11.85	2	351260.57	2	77895.01
Elorac, Corp	702911.91	2.49	209078.76	2.55	83192.39	10.58	3	4751.3	6	20788.22
Sundial	694918.98	2.47	213362.1	2.6	78401.72	10.14	4	7992.93	4	-4283.34
OUR Ltd	691687.4	2.45	196396.26	2.4	78732.2	10.22	5	3231.58	10	16965.84
Eminence Corp	681612.78	2.42	213002.78	2.6	86904.03	11.31	6	10074.62	5	-16606.52
Apotheca, Ltd	667283.99	2.37	157435.56	1.92	101453.91	13.2	7	14328.79	31	55567.22
Rochester Ltd	662943.9	2.35	224918.2	2.74	81158.11	10.91	8	4340.09	3	-67482.64
ETUDE Ltd	658370.48	2.34	205432.79	2.51	89322.72	11.95	9	4573.42	9	19485.41
Llorens Ltd	646779.31	2.29	206567.4	2.52	82897.59	11.36	10	11591.17	8	-1134.61

Comments:
1) One could use [Total Amount] = [Total  Discount]+[Sales Amount] as a separate column.
2) The [Rank Sales] is different from the [Rank Rev.] because of the discount applied.
3) In the last two formulas a blank was considered for the first item from the ranking.
4) It's not possible to control when the totals should be displayed, however one can change the color for the not needed total to match the background.

Visualizing Differences 

Once the formulas are added, one can hide the basis columns and visualize the data as needed. To obtain the below chart I copied the visual and changed the column as follows:

Diff. to Prev. Rev. = IF([Rank Revenue]>1, [Revenue]- INDEX([Rank Revenue]-1, , ORDERBY([Revenue], DESC)) , [Revenue]) -- modified column

Comments:

1) Instead of showing the full revenue, the chart shows only the differences from the highest revenue, where the column in green is the highest revenue, while the columns in red are the differences of the current customer's revenue to the previous customer, as the data are sorted by the highest revenue. At least in this case it results in a lower data-ink ratio (see Tufte).

2) The values are sorted by the [Revenue] descending. 

3) Unfortunately, it's not possible to change the names from the legend.

Simple Moving Averages (SMAs)

Based on the [Sales Amount], [Revenue] and [Month] one can add the following DAX formulas to the table for calculating the SMA:

Sales Amount (SMA) = MOVINGAVERAGE([Sales Amount],6)
Revenue (SMA) = MOVINGAVERAGE([Revenue],6)

The chart becomes:

Comments:

1) Unfortunately, the formula can't project the values into the feature, at least not without the proper dates.
2) "Show items with not data" feature seems to be disabled when visual calculations are used.

3) The SMA was created via a template formula. Similarly, calculating a running sum is reduced to applying a formula:

Running Sales Amount = RUNNINGSUM([Sales Amount])

Wrap Up

It's easier to start with a table for the visual, construct the needed formulas and then use the proper visual while eliminating the not needed fields. 

The feature is still in public preview and changes can still occur. Unfortunately, there's still no information available on the general availability date. From the first tests, it provides considerable power with a minimum of effort, which is great! I don't want to think how long I would have needed to obtain the same results without it!

Happy coding!

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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)