Box Plot
A Box Plot (also known as a Whisker Plot) is a statistical chart that displays the distribution of a dataset based on five key summary metrics:
Minimum (Min)
First Quartile (Q1)
Median (Q2)
Third Quartile (Q3)
Maximum (Max)
The chart also highlights outliers beyond the whiskers, making it an effective tool for identifying variability, spread, and anomalies within the data.
For example, a Box Plot can show the distribution of exam scores across different student groups, helping identify performance ranges and extreme values.
Best Situations to Use
Use a Box Plot chart when you want to:
Show distributions of numeric data across one or more categories.
Compare multiple groups (e.g., Male vs. Female scores, Region A vs. Region B).
Identify variability within groups, including outliers.
Highlight skewness or spread in data values.
Variations
Letter-Value Plots: An extension of the standard Box Plot that uses multiple nested boxes to enclose progressively larger portions of the dataset. Useful for very large datasets.
Violin Plots: Combines Box Plot with a density curve to provide richer insights into the distribution shape.
Properties of the Box Plot Chart
General
Component Name: Unique identifier for the chart.
Left / Top: Position of the chart on the canvas.
Height / Width: Chart dimensions.
Initial Visibility: Toggle chart visibility on first render.
Max Button: Enable a maximize option.
Fill Colors: Configure fill colors for box areas.
Stroke Color: Define outline/border color for elements.
Background
Gradient Rotation: Adjust background gradient angle.
Opacity: Control transparency.
Gradient: Select gradient type and direction.
Border / Border Color / Border Radius: Configure chart border style, color, and corner curvature.
Shadow / Shadow Color / Transparency: Add drop shadow with custom settings.
Tooltip
Show Tool Tip: Enable or disable tooltips.
Configuration:
Background Color
Opacity
Border Color
Font Size
Box Width
Precision (decimal places for values)
Title & Sub-Title
Show Title Box: Toggle visibility of title container.
Title Settings: Font, size, style, weight, family, alignment, decoration.
Title Bar Height: Adjust height.
Description: Add explanatory text.
Dataset Description: Option to populate from mapped dataset.
Sub-Title: Similar customization for sub-title text.
Axis Configuration
X-Axis
Show / Hide axis line.
Axis Line Color.
Label settings: font color, size, weight, family, style, decoration, rotation, tilt.
Axis description text.
Category Tick Marks & Marker Color.
Show Dataset Description.
Y-Axis
Similar settings as X-axis: line visibility, label configuration, description, dataset description.
Legend
Show Legend: Toggle legend visibility.
Font Properties: Customize color, size, weight, family, style, and decoration.
Hide on Load: Optionally hide legend at chart load.
Formatter
Unit: Define measurement unit.
Precision: Decimal places for numeric values.
Currency: Format numbers as currency with appropriate symbol.
Position: Place currency/unit before or after values.
Number Formatter: Choose International or Indian numeric format.
Axis Setup
Auto Axis Setup: Automatically adjust axis ranges.
Base Zero: Start axis at zero for consistency.
Min / Max Value: Set custom axis ranges.
Reference Lines: Add horizontal/vertical/zero marker lines.
Zero Marker Color: Customize baseline marker color.
Opacity: Adjust transparency for markers/axes.
Export Options
Context Menu: Enable export via right-click.
Supported Formats: Excel (.xlsx), CSV (.csv), JPEG (.jpg), PNG (.png), PPTX (.pptx), PDF (.pdf).
Print Option: Directly print chart.
Export Metadata: Add custom heading, subheading, filename.
Global Export Type: Choose default export style (Screenshot or Tabular).
Dataset Properties
When configuring a Box Plot:
Category: e.g., Gender.
Subcategory: e.g., Grade.
Metrics: Map numerical values such as Min, Max, Q1, Median, Q3.
Additional support for:
Outlier Properties: Configure visual style for outliers.
Example
Using the sample dataset:
Category: Gender
Subcategory: Grade
Values: Map Min → Min field, Max → Max field, and Quartiles → Q1, Median, Q3 fields.
This produces a Box Plot highlighting score distributions for different categories, including outliers.