Configuring Default Aggregations

Aggregations help summarize quantitative data across different groupings or time periods.

Understanding Aggregations

When to Use Aggregations

Aggregations are essential when you need to:

• Calculate sums of quantitative values (e.g., total sales)
• Find averages across groups (e.g., average temperature by city)
• Determine extreme values (e.g., maximum price per category)
• Count occurrences within groups

Configuring Default Aggregations

Setting Up in Schema

Use the defAggFn attribute in your schema to specify default aggregation behavior:

const schema = [
  {
    name: "Horsepower",
    type: "measure",
    defAggFn: "avg",
  },
  {
    name: "Maker",
    type: "dimension",
  },
];

info

When a default aggregation is specified, DataModel automatically applies it during any aggregation operation on that field.

Available Aggregation Functions

Access these via DataModel.AggregationFunctions:

Function	Description	Use Case
SUM	Sum of all values	Total sales, total quantity
AVG	Average of all values	Average price, mean temperature
MAX	Maximum value	Highest score, peak value
MIN	Minimum value	Lowest price, minimum rating
COUNT	Count of all values	Number of transactions
STD	Standard deviation	Data spread, variation analysis

Practical Example

Let's create a visualization showing average horsepower by number of cylinders:

const { muze, getDataFromSearchQuery, env } = viz;
const DataModel = muze.DataModel;

// Sample data
const data = [
  {
    Name: "chevrolet chevelle malibu",
    Maker: "chevrolet",
    Horsepower: 130,
    Cylinders: 6,
  },
  {
    Name: "buick skylark 320",
    Maker: "buick",
    Horsepower: 165,
    Cylinders: 8,
  },
  {
    Name: "datsun pl510",
    Maker: "datsun",
    Horsepower: 88,
    Cylinders: 4,
  },
];

// Schema with default aggregation
const schema = [
  {
    name: "Cylinders",
    type: "dimension",
  },
  {
    name: "Horsepower",
    type: "measure",
    defAggFn: "avg", // Default aggregation set to average
  },
];

// Create visualization
const parsedData = await DataModel.loadData(data, schema);
const dm = new DataModel(parsedData);

muze
  .canvas()
  .rows(["Horsepower"])
  .columns(["Cylinders"])
  .data(dm)
  .mount("#chart");

Best Practices

1. Choose Appropriate Aggregations

   • Consider the nature of your data
   • Think about the insights you want to convey
   • Use aggregations that make sense for your metrics

2. Schema Design

   • Define aggregations for all relevant measure fields
   • Keep aggregations consistent across related metrics
   • Document your aggregation choices

3. Performance

   • Use aggregations to reduce data points when dealing with large datasets
   • Consider pre-aggregating data when possible

Common Pitfalls

• Applying sum aggregation to already averaged data
• Using count for normalized values
• Mixing different types of aggregations in the same visualization