Dimensions vs. Measures; Discrete vs. Continuous Fields Dimensions are the building blocks of data visualizations. They define the attributes or ca...
Dimensions are the building blocks of data visualizations. They define the attributes or categories of your data. Think of them as the keys that unlock the information in your data.
Here's an example:
Dimensions in a sales dataset could be "Product", "Customer", and "Date". These represent the categories in the data.
Measures would be the values within each category, such as "Total Sales", "Number of Orders", and "Average Price".
Discrete fields have distinct, separate values for each observation. They are like discrete keys that can be counted. Think of them as lists or sets.
Continuous fields have continuous values that can take any value within a certain range. They are like continuous keys that cannot be counted. Think of them as continuous ranges or intervals.
Here's an analogy:
Dimensions are like the aisles of a store, where each aisle represents a distinct category (e.g., "Product Category").
Measures are like the items in each aisle, representing specific values within that category (e.g., "Total Sales" for "Electronics").
Examples:
Dimensions: Country, City, Product, Date
Measures: Population, Average Income, Price, Sales Amount
Remember, the choice of which fields to use in your visualizations depends on the questions you are trying to answer and the insights you are trying to convey