// String manipulation
Full Name
UPPER([First Name]) + " " + UPPER([Last Name])

Email Domain
SPLIT([Email], "@", 2)

// Numeric calculations
Profit Margin
[Profit] / [Sales]

Discounted Price
[Price] * (1 - [Discount])

// Date calculations
Days Since Order
DATEDIFF('day', [Order Date], TODAY())

Order Year
YEAR([Order Date])

Order Quarter
"Q" + STR(DATEPART('quarter', [Order Date]))

// Conditional logic
Order Priority
IF [Days Since Order] <= 2 THEN "Urgent"
ELSEIF [Days Since Order] <= 7 THEN "High"
ELSEIF [Days Since Order] <= 14 THEN "Medium"
ELSE "Low"
END

// Case statement
Customer Segment
CASE [Lifetime Value]
    WHEN >= 10000 THEN "VIP"
    WHEN >= 5000 THEN "High Value"
    WHEN >= 1000 THEN "Medium Value"
    ELSE "Low Value"
END

// Aggregations
Total Revenue
SUM([Order Amount])

Average Order Value
AVG([Order Amount])

Distinct Customer Count
COUNTD([Customer ID])

// Window calculations
Running Total
RUNNING_SUM(SUM([Sales]))

Moving Average (7 days)
WINDOW_AVG(SUM([Sales]), -6, 0)

Percent of Total
SUM([Sales]) / TOTAL(SUM([Sales]))

Rank by Sales
RANK_UNIQUE(SUM([Sales]), 'desc')

Previous Period Sales
LOOKUP(SUM([Sales]), -1)

// Quick table calculations
// Right-click measure -> Quick Table Calculation
// - Running Total
// - Difference
// - Percent Difference
// - Percent of Total
// - Rank
// - Percentile
// - Moving Average

// Year over Year Growth
YoY Growth
(SUM([Sales]) - LOOKUP(SUM([Sales]), -12)) / LOOKUP(SUM([Sales]), -12)

// Compound growth rate
CAGR
POWER(
    SUM([Current Year Sales]) / SUM([First Year Sales]),
    1 / [Years]
) - 1

// Customer lifetime value (fixed at customer level)
{ FIXED [Customer ID] : SUM([Order Amount]) }

// First order date per customer
{ FIXED [Customer ID] : MIN([Order Date]) }

// Category-level average (ignore other dimensions)
{ FIXED [Category] : AVG([Sales]) }

// Overall average (ignore all dimensions)
{ FIXED : AVG([Sales]) }

// Cohort analysis
Cohort Month
{ FIXED [Customer ID] : MIN(DATETRUNC('month', [Order Date])) }

// Customer acquisition cost per month
{ FIXED [Acquisition Month] : SUM([Marketing Spend]) / COUNTD([Customer ID]) }

// Add dimension to aggregation
{ INCLUDE [Region] : SUM([Sales]) }

// Product sales including subcategory
{ INCLUDE [Sub-Category] : SUM([Sales]) }

// Use case: Show product sales with category total
Product Sales
SUM([Sales])

Category Sales
{ INCLUDE [Category] : SUM([Sales]) }

Percent of Category
[Product Sales] / [Category Sales]

// Remove dimension from aggregation
{ EXCLUDE [Region] : SUM([Sales]) }

// Total sales excluding customer dimension
{ EXCLUDE [Customer ID] : SUM([Sales]) }

// Use case: Compare individual to group
Individual Sales
SUM([Sales])

Group Average (excluding individual)
{ EXCLUDE [Salesperson] : AVG([Sales]) }

Performance vs Group
[Individual Sales] - [Group Average]

// New vs Returning Customers
Is First Order
{ FIXED [Customer ID] : MIN([Order Date]) } = [Order Date]

New Customers
IF [Is First Order] THEN 1 ELSE 0 END

// Customer lifetime metrics
Orders Per Customer
{ FIXED [Customer ID] : COUNTD([Order ID]) }

Days Since First Order
DATEDIFF('day',
    { FIXED [Customer ID] : MIN([Order Date]) },
    [Order Date]
)

// Cohort retention
Months Since First Order
DATEDIFF('month',
    { FIXED [Customer ID] : MIN([Order Date]) },
    [Order Date]
)

Cohort Size
{ FIXED [Cohort Month] : COUNTD([Customer ID]) }

Retention Rate
COUNTD([Customer ID]) / [Cohort Size]

// Top N with LOD
Top 10 Products by Revenue
{ FIXED [Product] : SUM([Revenue]) }

Is Top 10
RANK_UNIQUE([Top 10 Products by Revenue]) <= 10

// Percentile calculation
Revenue Percentile
{ FIXED [Customer ID] : SUM([Revenue]) }

Customer Percentile
IF PERCENTILE([Revenue Percentile], 0.9) THEN "Top 10%"
ELSEIF PERCENTILE([Revenue Percentile], 0.75) THEN "Top 25%"
ELSE "Other"
END

// Metric selector parameter
Metric Selector (String)
Values: Revenue, Profit, Quantity, Orders

// Dynamic measure based on parameter
Selected Metric
CASE [Metric Selector]
    WHEN "Revenue" THEN SUM([Sales])
    WHEN "Profit" THEN SUM([Profit])
    WHEN "Quantity" THEN SUM([Quantity])
    WHEN "Orders" THEN COUNTD([Order ID])
END

// Date range parameter
Number of Days (Integer)
Current value: 30
Range: 7 to 365

// Filter with parameter
Order Date Filter
[Order Date] >= DATEADD('day', -[Number of Days], TODAY())

// Top N parameter
Top N (Integer)
Current value: 10
Range: 5 to 50

// Top N filter
Top N Products
RANK_UNIQUE(SUM([Sales]), 'desc') <= [Top N]

// Timeframe parameter
Time Dimension (String)
Values: Day, Week, Month, Quarter, Year

// Dynamic timeframe
Dynamic Time
CASE [Time Dimension]
    WHEN "Day" THEN STR([Order Date])
    WHEN "Week" THEN "Week " + STR(DATEPART('week', [Order Date]))
    WHEN "Month" THEN DATENAME('month', [Order Date]) + " " + STR(YEAR([Order Date]))
    WHEN "Quarter" THEN "Q" + STR(DATEPART('quarter', [Order Date])) + " " + STR(YEAR([Order Date]))
    WHEN "Year" THEN STR(YEAR([Order Date]))
END

// Comparison period parameter
Compare To (String)
Values: Previous Period, Previous Year, Custom

// Comparison calculation
Previous Period Sales
CASE [Compare To]
    WHEN "Previous Period" THEN
        LOOKUP(SUM([Sales]), -1)
    WHEN "Previous Year" THEN
        LOOKUP(SUM([Sales]), -12)
    WHEN "Custom" THEN
        // Use another parameter for custom offset
        LOOKUP(SUM([Sales]), -[Custom Offset])
END

Percent Change
(SUM([Sales]) - [Previous Period Sales]) / [Previous Period Sales]

// Threshold parameter
Sales Threshold (Float)
Current value: 1000
Range: 0 to 10000

// Color coding with parameter
Sales Performance
IF SUM([Sales]) >= [Sales Threshold] THEN "Above Target"
ELSE "Below Target"
END

// Multiple metric comparison
Metric 1 (String)
Metric 2 (String)

Metric 1 Value
CASE [Metric 1]
    WHEN "Revenue" THEN SUM([Sales])
    WHEN "Profit" THEN SUM([Profit])
    WHEN "Orders" THEN COUNTD([Order ID])
END

Metric 2 Value
CASE [Metric 2]
    WHEN "Revenue" THEN SUM([Sales])
    WHEN "Profit" THEN SUM([Profit])
    WHEN "Orders" THEN COUNTD([Order ID])
END

// Physical layer: Tables joined
Sales (LEFT JOIN) Returns ON Sales.Order ID = Returns.Order ID

// Logical layer: Relationships
Orders -> Order Items (Order ID)
Orders -> Customers (Customer ID)
Products -> Order Items (Product ID)

// Multi-fact analysis with relationships
// Automatically handles different grain levels
Revenue from Orders
SUM([Orders].[Amount])

Return Rate from Returns
COUNTD([Returns].[Return ID]) / COUNTD([Orders].[Order ID])

// Primary data source: Sales
// Secondary data source: Targets

// Linked fields (blend on):
- Date (linked)
- Region (linked)

// Blended calculation
Sales vs Target
SUM([Sales].[Revenue]) - SUM([Targets].[Target Amount])

Target Achievement
SUM([Sales].[Revenue]) / SUM([Targets].[Target Amount])

// Handling missing data in blend
Revenue with Default
IFNULL(SUM([Sales].[Revenue]), 0)

// Join across different databases
PostgreSQL: Orders
MySQL: Customer Attributes
Snowflake: Product Catalog

// Join conditions
Orders.customer_id = Customer Attributes.id
Orders.product_id = Product Catalog.product_id

// Layout containers
Horizontal container
    - Title (text)
    - Filters (vertical container)
Vertical container
    - KPI cards (horizontal container)
    - Main visualization
    - Detail table

// Actions
Filter action:
    Source: Map
    Target: Detail table
    Run on: Select
    Clear selection: Show all values

Highlight action:
    Source: Bar chart
    Target: Line chart
    Run on: Hover
    Clear selection: Leave highlighted

URL action:
    Name: View Customer Details
    URL: https://crm.company.com/customer?id=<Customer ID>
    Run on: Menu

Set action:
    Source: Product list
    Target: Set field
    Run on: Select
    Use: Compare products

// Dashboard sizing
Fixed size: 1200 x 800 (desktop)
Automatic: Responsive
Range: 800-1200 (tablet)

// Device designer
Desktop layout (default)
Tablet layout (hide some filters, stack vertically)
Phone layout (single column, essential metrics only)

// KPI cards
Total Revenue
SUM([Sales])
Format: Currency, $#,##0K

YoY Growth
([Current Year Revenue] - [Last Year Revenue]) / [Last Year Revenue]
Format: Percentage, 0.0%

// Trend with reference line
Line chart: Sales by Month
Reference line: Average
Trend line: Linear

// Top performers
Bar chart: Top 10 Products by Revenue
Filter: [Top N Products] = True
Sort: Descending by Revenue
Color: Profit Ratio (diverging)

// Comparison
Butterfly chart: Sales vs Budget by Category
Bars: Positive = Sales, Negative = Budget
Sort: By variance

// Geographic
Map: Sales by State
Color: Sales (gradient)
Size: Profit
Tooltip: State, Sales, Profit, Orders

// Drill-down hierarchy
Category -> Sub-Category -> Product
Action: Drill down on click

// Compute using options:
// - Table (across)
// - Table (down)
// - Pane (across)
// - Pane (down)
// - Cell
// - Specific dimensions

// Running total partitioned by category
Compute using: Category (restart for each category)

// Percent of total within partition
Compute using: Pane (down)

// Rank by region
RANK_UNIQUE(SUM([Sales]))
Compute using: Region

// Window calculation with specific dimensions
Window average
Compute using: Month, Product
Addressing: Month
Partitioning: Product

// First/Last in partition
Is First Order
FIRST() = 0

Is Last Order
LAST() = 0

// Index for row numbering
Row Number
INDEX()

// Size of partition
Total Rows
SIZE()

// Cumulative percent
Running Percent
RUNNING_SUM(SUM([Sales])) / TOTAL(SUM([Sales]))

// Period over period percent change
// Compute using: Month
Period Change
(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1)) /
ABS(LOOKUP(ZN(SUM([Sales])), -1))

// Month over month growth rate
// Partitioned by category
MoM Growth
Compute using: Table (across)
Addressing: Month
Partitioning: Category

// Extract filters
// Filter 1: Date >= 2020-01-01
// Filter 2: Country IN ['US', 'UK', 'CA']
// Filter 3: Is_Deleted = False

// Aggregation
Aggregate visible dimensions
Roll up dates to: Month

// Extract refresh
Full refresh: Replace all data
Incremental refresh: Add rows where Date > MAX(Date)

// Hyper extract
File format: .hyper (Tableau 10.5+)
Compression: High

// Data source filters (apply early)
Data Source Filter:
    [Order Date] >= DATE('2020-01-01')
    AND [Is_Deleted] = FALSE

// Context filters (create temp table)
Context: [Region] IN ['North', 'South']

// Filter order of operations:
1. Extract filters
2. Data source filters
3. Context filters
4. Dimension filters
5. Measure filters
6. Table calc filters

// Optimize calculations
// Bad: Nested LODs
{ FIXED [Customer] : MAX({ FIXED [Order] : SUM([Sales]) }) }

// Good: Single LOD with nested aggregation
{ FIXED [Customer] : SUM([Sales]) }

// Use boolean instead of string
// Bad:
Status = "Active"

// Good:
Is Active (boolean field)

// Reduce mark count
// Use aggregated data
// Filter to relevant data only
// Use extracts for large datasets

// Optimize dashboard
// Limit number of worksheets
// Use dashboard actions instead of filters
// Hide unused fields
// Reduce number of marks (<1000 ideal)

// Static set
Top 10 Customers
Condition: By field, Top 10 by SUM([Sales])

// Dynamic set
High Value Orders
Condition: SUM([Order Amount]) > [Threshold Parameter]

// Combined sets
VIP and Recent
[Top Customers] AND [Recent Purchasers]

// Set action
// Allow users to select items to add to set
Action: Add/Remove from Set
Source: Product list
Target Set: Selected Products
Run on: Select

// Using sets in calculations
Is Top Customer
[Customer] IN [Top 10 Customers]

Customer Type
IF [Customer] IN [VIP Set] THEN "VIP"
ELSEIF [Customer] IN [Top 100 Set] THEN "High Value"
ELSE "Standard"
END

// Reference lines
Average line: AVG(SUM([Sales]))
Median line: MEDIAN(SUM([Sales]))
Constant: [Target Parameter]

// Reference bands
Quartiles: 25th to 75th percentile
Custom: [Low Threshold] to [High Threshold]

// Distribution bands
Percentages: 60%, 80%, 95%
Standard deviation: 1, 2, 3 sigma

// Box plot
Whiskers: 1.5 * IQR
Outliers: Points beyond whiskers

// Trend lines
Linear, Logarithmic, Exponential, Polynomial
Show equation
Show R-squared value
Forecast: 12 months forward
Confidence interval: 95%

// Forecast
Automatic: Tableau selects model
Ignore last: N periods (for backtesting)

// Bad: Blend when relationship would work
Primary: Sales (blend on Date, Product)
Secondary: Costs (blend on Date, Product)

// Good: Use relationship or join
Sales <- (Product ID) -> Costs

// Bad: Nested LODs
{ FIXED [Customer] :
    MAX({ FIXED [Order] : SUM([Amount]) })
}

// Good: Single LOD
{ FIXED [Customer] : SUM([Amount]) }

// Bad: Scatter plot with 100K points
// Good: Aggregate or filter data
// Use density marks for large datasets

// Bad: Extract entire table without filters
// Good: Filter to relevant data, aggregate dimensions