Sales per product subcategory and region. â Adds up sales for all rows in each cell. â This is now combination of ro
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How Tableau renders data Aka underlying data vs aggregated data
How Tableau renders data aka underlying data vs aggregated data Understanding how table calculations work with Tableau’s data aggregation is key to understanding and troubleshooting table calculations.
Let’s look at sales per region from our favorite data set… $
How does a sales order actually happen in the real world?
How does a sales order actually happen in the real world? [someone]
How does a sales order actually happen in the real world? [from somewhere]
[someone]
How does a sales order actually happen in the real world? [from somewhere]
[someone]
[buys something]
How does a sales order actually happen in the real world? [from somewhere]
[someone]
[buys something] [with some money]
$
$
Underlying Data
Order ID
Product Name
Customer Name
Region
Sales
CA-2011102652
Tenex B1-RE Series Chair Mats for Low Pile Carpets
Andy Yotov
West
91.96
CA-2011102652
C-Line Cubicle Keepers Polyproplyene Holder With Velcro Backings
Andy Yotov
West
33.11
CA-2011103219
Eldon 200 Class Desk Accessories, Black
Benjamin Farhat
East
56.52
CA-2011103317
Advantus Panel Wall Certificate Holder - 8.5x11
Don Miller
South
19.52
CA-2011103317
Luxo Adjustable Task Clamp Lamp
Don Miller
South
213.22
Underlying Data When we look at the underlying data for that customer’s order, we see a row for each product purchased in that order But when we analyze Superstore sales, we probably want to look at multiple customer orders That’s where aggregated data comes in…
Aggregated data
Sales per product subcategory ◉
Crosstab aggregates the underlying data
◉
Adds up all rows in each product subcategory
◉
1 “row” is now lots and lots of actual rows
Tableau sees it a little differently
Tableau sees it a little differently
Chairs {$328,449.10} Tables {$206,965.53} Bookcases {$114,880.00} Furnishings {$91,705.16}
◉ ◉
Data set with 4 rows 4 vectors of 1 element each
A (slightly) more complex example
Adding a dimension to the columns makes the view a bit more complex In our example, we’ll now be looking at total sales for each product subcategory in each sales region
A (slightly) more complex example
Sales per product subcategory and region ◉
Adds up sales for all rows in each cell
◉
This is now combination of row & column fields
◉
Tableau sees 4 vectors of 4 elements each
And Tableau sees… Chairs {$85,230.65, $96,260.68, $45,176.45, $101,781.3} Tables {$39,154.97, $39,139.81, $43,916.19, $84,754.56} Bookcases {$24,157.18, $43,819.33, $10,899.36, $36,004.12} Furnishings {$15,254.37, $29,071.38, $17,306.68, $30,072.73}
Table calculations allow Tableau to lookup values in multiple rows and columns
“
When we write calculations, we’re used to thinking about it in terms of how we want to add up the underlying rows of data
“
To level up on table calculations, you have to start thinking about it from Tableau’s perspective: what are the (aggregated) rows and columns in the world of this worksheet?
“
Anatomy of a table calculation Percent of total
Anatomy of a table calculation Percent of total
SUM([Sales])]/ TOTAL(SUM([Sales]))
Percent of total example
What percent of total sales does each product subcategory contribute?
Percent of total example SUM([Sales]) / TOTAL(SUM([Sales]))
Percent of total example SUM([Sales]) / TOTAL(SUM([Sales]))
Aggregation from 1 cell
Percent of total example SUM([Sales]) / TOTAL(SUM([Sales]))
Aggregation from 1 cell
Aggregation from multiple cells
INDEX()
The magic of the index calculation aka Tableau’s row counting function Use built-in counting functionality to: ◉ Understand how Tableau will see the data structure of your worksheet ◉ Figure out how that structure will be used to compute your table calculations
