## Dynamics of DAX Analysis: A Journey through CROSSJOIN and CALCULATE

##### Description

The project will exhibit the power of DAX formula language through the dissection and understanding of the given formula: CALCULATE (SUM (Sales[SalesAmount]) / SUM (Budget[BudgetAmount]), CROSSJOIN (Sales, Budget)). This will entail understanding the individual components, their fundamental workings, and most importantly, how they interact within the formula. By analyzing SalesAmount values versus BudgetAmount, the course will also delve into importance of data transformations in shaping your findings, and correlating these with real-life business scenarios.

The original prompt:

Can you explain this formula and concept more. I'm looking to learn more about how CROSSJOIN interacts with CALCULATE specifically

CALCULATE ( SUM ( Sales[SalesAmount] ) / SUM ( Budget[BudgetAmount] ), CROSSJOIN ( Sales, Budget ) )

# DAX: Unravelling the Basics

## Introduction

Data Analysis Expressions (DAX) is a formula language used primarily in Power BI, Analysis Services, and Power Pivot in Excel. DAX includes some of the functions used in Excel formulas, and additional functions designed to work with relational data and perform dynamic aggregation.

## CROSSJOIN Function

The CROSSJOIN function generates a table that is the cartesian product of all rows from the first table with all rows from the second table.

Here's the syntax:

`CROSSJOIN(`, )

This function returns a single column table that is a cross join of all the rows in the input tables. Each row in the first table is joined with each row in the second table.

For example, if `Table1`

has 'A' and 'B', and `Table2`

has '1' and '2', then CROSSJOIN of these two tables generates ['A'&'1', 'A'&'2', 'B'&'1', 'B'&'2'].

## CALCULATE Function

CALCULATE is the most important function in DAX. It allows you to modify the context in which a formula is evaluated.

Here's the syntax:

`CALCULATE(`, , ....)

The `expression`

is any DAX expression that returns a single scalar value (a single value of any type). The `filter`

arguments are applied to the context. If a column name is given, the column must be a scalar Boolean (TRUE/FALSE) column.

For example:

`CALCULATE(SUM(Table[column]), Table[Year] = "2020")`

This formula calculates the sum of all values in the specified column for the year 2020.

## Interplay Between CROSSJOIN and CALCULATE Functions

Combining these two functions can yield powerful results.

For instance, suppose you want to find the profit for each combination of products and locations for a given period. You can use the CROSSJOIN function to create all possible combinations of products and locations. Then, use the CALCULATE function to calculate the profit for each of these combinations.

Here's how you can do it (assume `ProductTable`

, `LocationTable`

, and `SalesTable`

have appropriate data):

```
VAR CrossJoinedTable =
CROSSJOIN(VALUES(ProductTable[ProductName]), VALUES(LocationTable[Location]))
RETURN
ADDCOLUMNS(
CrossJoinedTable,
"Profit",
CALCULATE(
SUMX(SalesTable, SalesTable[Revenue]-SalesTable[Cost]),
FILTER(
SalesTable,
SalesTable[ProductName] = [ProductName] &&
SalesTable[Location] = [Location]
)
)
)
```

This formula first creates a crossjoin table `CrossJoinedTable`

with all combinations of product names and locations.
The `ADDCOLUMNS`

function then adds a new column "Profit" to the `CrossJoinedTable`

. This new column "Profit" is calculated by the `CALCULATE`

function, which sums the profit (Revenue - Cost) from `SalesTable`

for each combination of product name and location.

Remember, in DAX, the calculation of measures and columns is always done in the context where they are called. By using `CALCULATE`

, you can change this, adding filters or changing the existing ones.

In the example, `CALCULATE`

changes the filter context to include only rows where the product name and location match the current row in `CrossJoinedTable`

. This allows calculating profit for each combination of product and location separately.

Note: The `FILTER`

function is used in the `CALCULATE`

parameters to create this custom filter context.

This unravels a basic yet effective use of the powerful interplay between CROSSJOIN and CALCULATE in DAX. It's remarkable how much flexibility these two functions can offer to your data analysis when used in tandem.

# A Comprehensive Exploration Into the Interplay Between CROSSJOIN and CALCULATE Functions in DAX

# Table of Contents

## Introduction to CROSSJOIN

### CROSSJOIN Function Syntax

The `CROSSJOIN`

function in Data Analysis Expressions (DAX) returns a table that is the cartesian product of all rows from two or more tables.

Using `CROSSJOIN`

, you can create a combination of every row from two separate tables, even if there are no shared columns in those tables.

`CROSSJOIN(table1[Column1], table2[Column2], ...)`

### Practical Example of Using CROSSJOIN

Let's suppose we have two tables:

`Table1`

:

Category |
---|

Cat1 |

Cat2 |

`Table2`

:

Type |
---|

Type1 |

Type2 |

We can make use of `CROSSJOIN`

to create a table that combines these two:

```
EVALUATE
CROSSJOIN(Table1, Table2)
```

Result:

Category | Type |
---|---|

Cat1 | Type1 |

Cat1 | Type2 |

Cat2 | Type1 |

Cat2 | Type2 |

## Introduction to CALCULATE

### CALCULATE Function Syntax

`CALCULATE`

is one of the most powerful and commonly used functions in DAX. It allows you to modify the context in which data is aggregated, and it comes with the following syntax:

`CALCULATE(`,
,
, ...
)

Where:

`<expression>`

is any DAX expression that returns a single scalar value.`<filter>`

is a Boolean expression or a table expression that defines a filter.

### Demo of Utilizing CALCULATE

Suppose we have `Sales`

table:

Year | Category | Revenue |
---|---|---|

2020 | Cat1 | 1000 |

2020 | Cat2 | 2000 |

2021 | Cat1 | 1500 |

2021 | Cat2 | 2500 |

To calculate the total revenue for category `Cat1`

in 2021, we can use `CALCULATE`

as follows:

```
CALCULATE (
SUM ( Sales[Revenue] ),
Sales[Year] = 2021,
Sales[Category] = "Cat1"
)
```

This will return 1500, as the `CALCULATE`

function has changed the context to include only the rows where year is 2021 and category is Cat1.

## Interplay between CROSSJOIN and CALCULATE

### Applying CROSSJOIN and CALCULATE Together

Combining `CROSSJOIN`

and `CALCULATE`

brings scalability, as `CROSSJOIN`

broadens the table with added combinations, and `CALCULATE`

filters and performs calculations according to the new set.

Consider `Table1`

(Categories) and `Table2`

(Types) as above, and let's add a third `Sales`

table:

Category | Type | Revenue |
---|---|---|

Cat1 | Type1 | 1000 |

Cat2 | Type2 | 2000 |

Let's say we want to sum the revenue for every category and type combination. We utilize `CROSSJOIN`

to build a new table and `CALCULATE`

to sum the revenue for each cell:

```
EVALUATE
ADDCOLUMNS (
CROSSJOIN ( Table1, Table2 ),
"Total Revenue", CALCULATE ( SUM ( Sales[Revenue] ) )
)
```

This provides a detailed overview by merging rows from `Table1`

and `Table2`

, and calculating the `SUM`

of `Revenue`

for each combination. For the combinations that don't exist in `Sales`

, `Revenue`

will return BLANK().

This approach holds for any kind of operation with `CALCULATE`

and data structure with `CROSSJOIN`

, proving the flexible and dynamic usage of these DAX functions.

# DAX Implementation: Mastering the Art of Utilizing CROSSJOIN and CALCULATE Functions

In this guide, we'll delve into practical implementations that exemplify the complex interplay between CROSSJOIN and CALCULATE functions in DAX (Data Analysis Expressions). That said, for this guide to be useful, it's assumed that you're familiar with the fundamentals of DAX as well as the basics of both CROSSJOIN and CALCULATE functions.

## The Interplay of CROSSJOIN and CALCULATE

### The CROSSJOIN function

Briefly, CROSSJOIN is a table function that outputs the cartesian product of tables provided. Let's start with a simple example representing two tables.

Define these two tables:

```
Product = DATATABLE (
"ProductID", INTEGER,
"Product", STRING,
{
{1, "Apple"},
{2, "Banana"},
{3, "Orange"}
}
)
Country = DATATABLE (
"CountryID", INTEGER,
"Country", STRING,
{
{1, "US"},
{2, "UK"},
{3, "AU"}
}
)
```

Using the CROSSJOIN function, the output would be:

`CROSSJOIN (Product, Country)`

There will be nine rows (3*3) resulting from the combined data of both tables.

### The CALCULATE function

CALCULATE is a function used to modify the context in which data is analyzed, essentially allowing us to create different versions of reality by changing the conditions of calculations.

For instance, if we'd like to compute the total price of each product in every country (context), we'd write the following:

`CALCULATE (SUM (Sales[Price]), 'Product')`

## Interplaying CROSSJOIN and CALCULATE

Let's say we've a sales table, `Sales`

, with `ProductID`

, `CountryID`

, and `Price`

.

Using CROSSJOIN and CALCULATE together, we can create a new table which provides us with a comprehensive sales breakdown by product and country.

```
SalesBreakdown =
ADDCOLUMNS (
CROSSJOIN (Product, Country),
"Total Sales", CALCULATE (SUM (Sales[Price]))
)
```

In this example, the CROSSJOIN function generates a table combining every product with every country. Then, for each row of this newly created table, the CALCULATE function is used to sum the sales prices for the specific product in the specific country ('context') given by the current row.

Therefore, the `SalesBreakdown`

table will return a table that shows the total sales of each product in each country.

Querying `SalesBreakdown`

would result in a breakdown of all sales by product and country. If some combinations do not exist in the Sales table, their 'Total Sales' would be blank.

Please note, however, that this is a heavy operation that can potentially result in a very large table if Product or Country contain lots of items. Always verify your data model and records before performing such calculations.

The interplay of CROSSJOIN and CALCULATE functions in DAX thereby not only increases the depth of data analysis but also provides comprehensive insights into data sets irrespective of their complexity.