percent_of_total

Definition

percent_of_total calculates the percentage of a metric relative to a specified total type. This function simplifies percentage calculations by automatically handling the division and dimension context in visualizations.

Syntax

percent_of_total(metric, total_type)

Examples
// Calculate percentage of each row against row total
percent_of_total(orders.total_revenue, 'row_total')

// Calculate percentage of each column against column total
percent_of_total(users.count, 'column_total')

// Calculate percentage against grand total
percent_of_total(products.sales_amount, 'grand_total')

// Using axis aliases for better readability
percent_of_total(orders.count, 'x_axis_total')   // same as 'row_total'
percent_of_total(orders.count, 'legend_total')   // same as 'column_total'

Input

metric: A metric or aggregation expression that you want to calculate the percentage for
total_type: A string specifying which total to use as the denominator:
- 'row_total' or 'x_axis_total': Percentage of row total (across all columns in that row)
- 'column_total' or 'legend_total': Percentage of column total (across all rows in that column)
- 'grand_total': Percentage of the overall total

Output

A percentage value representing the metric's proportion of the specified total. The result is automatically formatted as a percentage (0-100).

Sample Usages

Pivot Table with Row and Column Percentages

When working with pivot tables, you can calculate percentages across different dimensions:

metric users_count = count(ecommerce_users.id);

explore {
  dimensions {
    rows {
      _year: ecommerce_users.created_at | year()
    }
    columns {
      _gender: ecommerce_users.gender
    }
  }

  measures {
    users_count: users_count,
    pct_row: percent_of_total(users_count, 'row_total'),      // % by gender within each year
    pct_col: percent_of_total(users_count, 'column_total'),   // % by year within each gender
    pct_grand: percent_of_total(users_count, 'grand_total'),  // % by both year and gender across all users
  }
}

This creates a pivot table showing:

pct_row: What percentage of users in each year belong to each gender
pct_col: What percentage of users of each gender joined in each year
pct_grand: What percentage each cell represents of the total user count

Using Alias Names

For better readability in visualizations, you can use alias names that correspond to chart axes:

percent_of_total(users_count, 'x_axis_total')   // same as 'row_total'
percent_of_total(users_count, 'legend_total') // same as 'column_total'

Non-Pivot Tables

When using percent_of_total in a regular (non-pivot) table:

metric users_count = count(ecommerce_users.id);

explore {
  dimensions {
    _year: ecommerce_users.created_at | year(),
    _gender: ecommerce_users.gender
  }

  measures {
    users_count: users_count,
    pct_row: percent_of_total(users_count, 'row_total'),     // 100% (no column grouping)
    pct_col: percent_of_total(users_count, 'column_total'),  // % of grand total
    pct_grand: percent_of_total(users_count, 'grand_total'), // % of grand total
  }
}

In non-pivot tables:

column_total and grand_total calculate the percentage of the overall total
row_total and x_axis_total default to 100% since there is only one column per metric, making the row total equal to the metric value itself

Definition​

Syntax​

Input​

Output​

Sample Usages​

Pivot Table with Row and Column Percentages​

Using Alias Names​

Non-Pivot Tables​

See Also​