JavaScript-Based Scatter Plot with Curved Zones in React

Building a Dynamic Scatter Plot with Temperature and Humidity Data

Visualizing data points effectively is critical when you're dealing with multiple variables. In this scenario, plotting temperature and humidity on a scatter plot can provide valuable insights. A scatter plot helps represent correlations and patterns between these variables, especially over time.

Beyond just a simple plot, you might want to create specialized zones that separate regions of the graph based on a curve. This adds a layer of complexity to your diagram, providing more detailed analysis, like defining areas of different humidity levels across temperature ranges. This can be challenging, especially if the zones are based on curves.

Thankfully, there are many libraries available for React and plain JavaScript that can help plot these data points and add curved zones. By using the right tools, you can easily generate a scatter plot with custom zones for better data analysis. These tools allow for flexibility and precision in mapping non-linear relationships.

In this article, we’ll explore how to collect data points and use popular JavaScript and React libraries to draw scatter plots with curved zones. With the right setup, you'll be able to map your temperature and humidity data to a diagram effectively.

Understanding the Implementation of Scatter Plot with Curved Zones in React

The first example in the scripts utilizes React in combination with the powerful D3.js library to create a scatter plot with curved zones. The scatter plot maps temperature to the x-axis and humidity to the y-axis, helping to visualize the relationship between these two variables. The data points are represented by circles, plotted using the D3 `enter()` and `append()` methods, which ensure each data point is added to the DOM. A crucial aspect of the implementation is the use of linear scales with `scaleLinear()`, which maps temperature and humidity values to pixel positions within the SVG, allowing the points to be positioned correctly on the chart.

In addition to plotting data points, the script draws curved zones using a line generator (`d3.line()`). This command creates paths that represent curves between specified points, allowing for non-linear zones to be drawn over the scatter plot. In this case, `curve(d3.curveNatural)` is applied to create smooth, natural-looking curves between temperature and humidity values. These curves are critical for defining different zones in the scatter plot, which can represent specific regions or ranges of interest, such as a comfortable or hazardous range of humidity based on the temperature.

The second example leverages Chart.js in React, a simpler but effective library for chart rendering. The Chart.js `Scatter` component is used to plot temperature and humidity data points. While Chart.js is not as flexible as D3.js for creating custom visualizations, it offers an intuitive setup for scatter plots. A key feature here is the `annotation` plugin, which allows drawing shapes, lines, or regions on the chart. This plugin is used to approximate the curved zones by drawing straight lines across sections of the scatter plot, creating visual divisions between the areas of interest. Though the curved zones are approximated with straight lines, this method is straightforward and provides a quick way to visualize zones in a scatter plot.

Both methods incorporate important practices, such as scaling data using `scaleLinear()` in D3.js and utilizing built-in options for customizing chart scales in Chart.js. These approaches are designed for flexibility, allowing developers to modify and extend them for different use cases. While D3.js offers more control and precision for drawing curves and zones, Chart.js provides a faster setup for basic scatter plots with some level of customization through plugins like `annotation`. Both scripts are modular and reusable, offering flexibility in building interactive scatter plots with curved zones in React applications.

import React, { useEffect, useRef } from 'react';
import * as d3 from 'd3';
const ScatterPlotWithCurves = ({ data }) => {
  const svgRef = useRef();
  useEffect(() => {
    const svg = d3.select(svgRef.current)
      .attr('width', 500)
      .attr('height', 500);
    const xScale = d3.scaleLinear()
      .domain([d3.min(data, d => d.temperatureC), d3.max(data, d => d.temperatureC)])
      .range([0, 500]);
    const yScale = d3.scaleLinear()
      .domain([d3.min(data, d => d.humidity), d3.max(data, d => d.humidity)])
      .range([500, 0]);
    svg.selectAll('.dot')
      .data(data)
      .enter().append('circle')
      .attr('cx', d => xScale(d.temperatureC))
      .attr('cy', d => yScale(d.humidity))
      .attr('r', 5);
    // Add zones using curved paths
    const lineGenerator = d3.line()
      .x(d => xScale(d[0]))
      .y(d => yScale(d[1]))
      .curve(d3.curveNatural);
    svg.append('path')
      .datum([[30, 60], [40, 70], [50, 80]])
      .attr('d', lineGenerator)
      .attr('stroke', 'red')
      .attr('fill', 'none');
  }, [data]);
  return <svg ref={svgRef}></svg>;
};
export default ScatterPlotWithCurves;

import React from 'react';
import { Scatter } from 'react-chartjs-2';
const ScatterPlot = ({ data }) => {
  const chartData = {
    datasets: [{
      label: 'Temperature vs Humidity',
      data: data.map(d => ({ x: d.temperatureC, y: d.humidity })),
      borderColor: 'blue',
      pointBackgroundColor: 'blue',
    }],
  };
  const options = {
    scales: {
      x: { type: 'linear', position: 'bottom', title: { display: true, text: 'Temperature (°C)' } },
      y: { title: { display: true, text: 'Humidity (%)' } },
    },
    plugins: {
      annotation: { // Plugin to draw curved zones
        annotations: [{
          type: 'line',
          xMin: 30, xMax: 50, yMin: 60, yMax: 80,
          borderColor: 'red', borderWidth: 2,
        }],
      },
    },
  };
  return <Scatter data={chartData} options={options} />;
};
export default ScatterPlot;

Exploring Alternative Libraries for Scatter Plot Creation in React

In addition to D3.js and Chart.js, there are other robust libraries that can handle scatter plot creation in React. One such option is Plotly, a charting library that offers both flexibility and ease of use. Plotly allows for interactive plots, including scatter plots, where you can not only plot data but also add curved zones using annotations or shape-drawing capabilities. Plotly comes with built-in support for responsive design, making it suitable for web applications that need to adjust for different screen sizes.

Another alternative is the use of Recharts, a library specifically designed for React applications. Recharts provides a simpler API compared to D3.js and is a great choice for developers who want quick results with minimal configuration. It supports scatter plots and custom shapes, making it possible to approximate curved zones. Though Recharts doesn’t have the extensive customization of D3.js, it is still a strong option for handling basic scatter plots, especially when ease of use and readability are key considerations.

Finally, for those who want maximum performance and rendering speed, CanvasJS is a good option. CanvasJS is lightweight and focuses on using the HTML5 canvas for drawing. It can handle large datasets effectively and supports real-time updates, making it a good fit for applications that require high performance. While it might lack some of the flexibility found in D3.js, CanvasJS is perfect for applications that require quick rendering and responsiveness, such as monitoring dashboards.