Data Visualization in Clojure: Incanter, Vega-Lite, and Hanami

14.5.3 Data Visualization

Data visualization is a powerful tool for understanding and communicating insights from data. In this section, we’ll explore how to create visualizations in Clojure using three popular libraries: Incanter, Vega/Vega-Lite with the oz library, and Hanami. We’ll provide examples of generating common chart types and discuss how these tools can be integrated into your Clojure projects.

Introduction to Data Visualization in Clojure

Data visualization in Clojure leverages the language’s functional programming paradigm, allowing for concise and expressive code. As an experienced Java developer, you’ll appreciate the seamless integration of these libraries with Clojure’s immutable data structures and higher-order functions.

Why Use Clojure for Data Visualization?

• Functional Paradigm: Clojure’s functional nature makes it easy to transform and manipulate data before visualization.
• Interoperability: Clojure runs on the JVM, allowing you to leverage existing Java libraries and tools.
• Expressiveness: Clojure’s syntax is concise, making it easier to express complex data transformations and visualizations.

Incanter: Statistical Computing and Graphics

Incanter is a Clojure-based library for statistical computing and graphics. It provides a rich set of functions for data manipulation, statistical analysis, and visualization.

Getting Started with Incanter

To use Incanter, you’ll need to add it to your project dependencies. Here’s how you can do it using Leiningen:

(defproject my-project "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.10.3"]
                 [incanter "1.9.3"]])

Once added, you can start using Incanter to create visualizations.

Creating a Simple Line Chart

Let’s create a simple line chart to visualize a dataset. We’ll use Incanter’s charts namespace to generate the chart.

(ns my-project.core
  (:require [incanter.core :as ic]
            [incanter.charts :as charts]))

(defn line-chart-example []
  (let [x (range 0 10)
        y (map #(* % %) x)] ; y = x^2
    (charts/view (charts/line-chart x y
                                    :title "Line Chart Example"
                                    :x-label "X Axis"
                                    :y-label "Y Axis"))))

(line-chart-example)

Explanation:

• We define a range of x-values from 0 to 9.
• We compute the y-values as the square of each x-value.
• We create a line chart using charts/line-chart and display it with charts/view.

Try It Yourself

Modify the y computation to visualize different mathematical functions, such as y = x^3 or y = sin(x).

Vega/Vega-Lite with Oz: Declarative Visualization

Vega and Vega-Lite are declarative languages for creating, sharing, and exploring interactive visualization designs. The oz library provides a Clojure interface to these tools, allowing you to create complex visualizations with minimal code.

Setting Up Oz

Add the oz library to your project dependencies:

(defproject my-project "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.10.3"]
                 [metasoarous/oz "1.6.0"]])

Creating a Bar Chart with Vega-Lite

Let’s create a bar chart using Vega-Lite through the oz library.

(ns my-project.core
  (:require [oz.core :as oz]))

(defn bar-chart-example []
  (oz/view!
   {:data {:values [{:category "A" :value 30}
                    {:category "B" :value 80}
                    {:category "C" :value 45}]}
    :mark "bar"
    :encoding {:x {:field "category" :type "nominal"}
               :y {:field "value" :type "quantitative"}}}))

(bar-chart-example)

Explanation:

• We define a dataset with categories and values.
• We specify the chart type as bar and map the data fields to the x and y axes.
• The oz/view! function renders the chart in a browser.

Try It Yourself

Experiment with different chart types, such as line, point, or area, by changing the :mark value.

Hanami: Declarative Data Visualization

Hanami is a declarative data visualization library for Clojure that builds on top of Vega-Lite. It provides a more idiomatic Clojure interface for creating visualizations.

Setting Up Hanami

Add Hanami to your project dependencies:

(defproject my-project "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.10.3"]
                 [scicloj/hanami "0.2.6"]])

Creating a Scatter Plot with Hanami

Let’s create a scatter plot using Hanami.

(ns my-project.core
  (:require [hanami.core :as hanami]))

(defn scatter-plot-example []
  (hanami/view!
   {:data {:values [{:x 1 :y 2}
                    {:x 2 :y 3}
                    {:x 3 :y 5}
                    {:x 4 :y 7}]}
    :mark "point"
    :encoding {:x {:field "x" :type "quantitative"}
               :y {:field "y" :type "quantitative"}}}))

(scatter-plot-example)

Explanation:

• We define a dataset with x and y values.
• We specify the chart type as point for a scatter plot.
• The hanami/view! function renders the chart.

Try It Yourself

Add more data points to the dataset and observe how the scatter plot changes.

Comparing Clojure Visualization Libraries

Diagram: Data Flow in Clojure Visualization

    flowchart TD
	    A[Data Source] --> B[Data Transformation]
	    B --> C[Visualization Library]
	    C --> D[Rendered Chart]

This diagram illustrates the flow of data from the source through transformation and into the visualization library, resulting in a rendered chart.

Best Practices for Data Visualization in Clojure

• Choose the Right Tool: Select a visualization library that fits your project’s needs in terms of complexity, interactivity, and ease of use.
• Keep It Simple: Avoid cluttering your visualizations with too much information. Focus on conveying the key insights.
• Leverage Clojure’s Strengths: Use Clojure’s functional programming capabilities to preprocess and transform data before visualization.
• Iterate and Experiment: Visualization is an iterative process. Experiment with different chart types and styles to find the most effective representation.

Exercises

1. Create a Multi-Series Line Chart: Use Incanter to create a line chart with multiple data series. Experiment with different colors and line styles.
2. Interactive Dashboard: Use Vega-Lite with Oz to create an interactive dashboard that includes multiple chart types and allows user interaction.
3. Custom Visualization with Hanami: Design a custom visualization using Hanami that combines multiple data sources and chart types.

Key Takeaways

• Clojure offers powerful tools for data visualization, each with unique strengths and use cases.
• Incanter is great for statistical analysis and traditional charts, while Vega-Lite and Hanami excel in creating interactive and declarative visualizations.
• Leveraging Clojure’s functional programming paradigm can enhance your data visualization capabilities.

For further reading, explore the Official Clojure Documentation and the Incanter GitHub Repository.

Quiz: Mastering Data Visualization in Clojure