Hey there! Looking to summarize your data distribution in a neat and visual way? Box plots are a fantastic way to do just that! In this guide, we'll walk you through creating and customizing box plots using Vega-Lite. By the end, you'll be a box plot pro!

What is a Box Plot?

A box plot summarizes the distribution of a set of quantitative values using several key summary statistics:

• Median: The central value of the dataset.
• Quartiles (Q1, Q3): The values that divide the dataset into quarters.
• Whiskers: Lines that represent variability outside the upper and lower quartiles.

Depending on the type of box plot, the whiskers can represent different things. Let's dive into the how-to!

Basic Box Plot Setup

Here's the minimal code to set up a box plot:

{
  "data": ... , 
  "mark": "boxplot", 
  "encoding": ... , 
  ...
}

Simply set the mark property to boxplot. Let's break down the different properties and customization options you have.

Box Plot Properties

Box plot marks come with a variety of properties you can tweak:

• type: The type of box plot.
• extent: Determines the whisker span.
• orient: Box plot orientation (vertical or horizontal).
• size: Size of the box.
• color: Color of the box.
• opacity: Opacity of the box.
• invalid: How to handle invalid data points.

Apart from these, you can customize individual parts like the box, median, whiskers, and outliers.

Types of Box Plots

Tukey Box Plot

This is the default type. The whiskers extend to cover the range [Q1 - 1.5 * IQR, Q3 + 1.5 * IQR]. Outlier points beyond the whiskers are plotted separately.

Check out this example:

{
  "data": { /* your data here */ },
  "mark": "boxplot",
  "encoding": { /* your encoding here */ },
  "extent": 1.5 // default value
}

Min-Max Box Plot

In this type, the whiskers extend from the minimum to the maximum data points, with no outliers marked.

Example:

{
  "data": { /* your data here */ },
  "mark": { "type": "boxplot", "extent": "min-max" },
  "encoding": { /* your encoding here */ }
}

Dimensions & Orientation

1D Box Plot

Shows distribution of a single continuous field.

{
  "data": { /* your data here */ },
  "mark": "boxplot",
  "encoding": {
    "x": { "field": "value", "type": "quantitative" }
  }
}

2D Box Plot

Displays distribution of a continuous field, broken down by categories.

{
  "data": { /* your data here */ },
  "mark": "boxplot",
  "encoding": {
    "x": { "field": "category", "type": "nominal" },
    "y": { "field": "value", "type": "quantitative" }
  }
}

Customizing Box Plot Parts

You can style different parts of your box plot.

{
  "data": { /* your data here */ },
  "mark": {
    "type": "boxplot",
    "color": "red",
    "median": { "color": "red" }
  },
  "encoding": { /* your encoding here */ }
}

Color, Size, and Opacity

Customize these properties using the encoding channels:

{
  "data": { /* your data here */ },
  "mark": "boxplot",
  "encoding": {
    "x": { "field": "category", "type": "nominal" },
    "y": { "field": "value", "type": "quantitative" },
    "color": { "value": "blue" },
    "size": { "value": 5 },
    "opacity": { "value": 0.7 }
  }
}

Tooltips

Add custom tooltips to your box plots.

{
  "data": { /* your data here */ },
  "mark": "boxplot",
  "encoding": {
    "x": { "field": "category", "type": "nominal" },
    "y": { "field": "value", "type": "quantitative" },
    "tooltip": { "aggregate": "mean", "field": "value", "type": "quantitative" }
  }
}

FAQs

What is a box plot good for?

A box plot is great for summarizing the distribution of a dataset along with its variability via quartiles and outliers.

How do I handle outliers in a box plot?

In Tukey box plots, outliers are automatically plotted beyond the whiskers. For min-max box plots, there are no outlier points.

Can I use pre-calculated summary statistics?

Yes, you can use a layered approach with pre-calculated summaries to create custom box plots.

Conclusion

And there you have it! A comprehensive guide to creating and customizing box plots with Vega-Lite. With these tips and examples, you're ready to visualize the distribution of your data like a pro. Happy plotting!

Feel free to let us know if you have any questions or need further clarifications. Happy Visualizing!