A dimension is a qualitative or categorical attribute used to categorize, group, or segment data. Dimensions provide context and describe the characteristics of the data, allowing for the organization and interpretation of information. They represent discrete, non-numeric data points and are often used for grouping, filtering, and organizing data for analysis and visualization purposes. Dimensions help answer questions like "how" or "what" and are typically displayed on the rows or columns shelves in data visualization tools like Tableau.
Common examples of dimensions include geographic locations, time periods, categories, and qualitative attributes.
Dimensions are fundamental to data analysis and visualization as they provide the necessary context to categorize and understand data, enabling users to create meaningful and informative visual representations of their data.
Here are some good dimensions just for us to understand better.
Product Category:
Example: If you have a sales dataset, the "Product Category" dimension could categorize products into groups such as "Electronics," "Clothing," and "Home Appliances."
Use: You can use this dimension to create bar charts, pie charts, or treemaps to compare sales performance across different product categories.
Geographic Location:
Example: "City," "State," or "Country" dimensions provide geographic context to your data. For instance, "City" might include values like "New York," "Los Angeles," and "Chicago."
Use: You can create maps or heat maps using geographic dimensions to visualize sales, population, or other data geographically.
Time Dimensions:
Examples: "Year," "Quarter," "Month," and "Day of Week" dimensions help you analyze data over time.
Use: You can create time series charts, line graphs, or histograms to understand trends and seasonality in your data.