gallery_scatter_bubble_chart.cpp

This gallery example demonstrates creating a sophisticated bubble chart using Plotly.cpp to visualize world development indicators across multiple dimensions. It combines scatter plot positioning with bubble size and color to represent four different metrics simultaneously.

Features demonstrated:

• Multi-dimensional data visualization (GDP, Life Expectancy, Population, Happiness)
• Bubble size mapping to represent population data
• Color scale mapping to represent happiness scores
• Regional grouping with separate traces for easy filtering
• Logarithmic scale for GDP data to handle wide value ranges
• Interactive hover templates with detailed country information
• Viridis color scale with custom colorbar configuration
• Synthetic data generation with realistic correlations

Data dimensions visualized:

• X-axis: GDP per Capita (logarithmic scale)
• Y-axis: Life Expectancy in years
• Bubble size: Population in millions
• Bubble color: Happiness Score (0-10 scale)
• Regional grouping: North America, Asia, Europe, South America, Oceania

The visualization enables exploration of complex relationships between economic, social, and demographic indicators across different world regions.

World Development Indicators Bubble

Chart"

 
#include "plotly/plotly.hpp"
#include "utils/arg_parser.hpp"
#include <algorithm>
#include <cstddef>
#include <random>
#include <string>
#include <vector>
 
auto main(int argc, char *argv[]) -> int {
  // Parse command line arguments
  auto args = parseGalleryArgs(argc, argv);
 
  plotly::Figure fig;
  fig.openBrowser(args.headless);
 
  // Generate synthetic multi-dimensional data representing countries
  std::vector<std::string> countries = {
      "USA",       "China",       "Japan",        "Germany", "India",
      "UK",        "France",      "Italy",        "Brazil",  "Canada",
      "Russia",    "South Korea", "Australia",    "Spain",   "Mexico",
      "Indonesia", "Netherlands", "Saudi Arabia", "Turkey",  "Taiwan"};
 
  const size_t n = countries.size();
  std::vector<double> gdpPerCapita, lifeExpectancy, population, happiness;
  std::vector<std::string> regions;
 
  // Random generators
  std::random_device rd;
  std::mt19937 gen(rd());
  std::uniform_real_distribution<double> gdpDist(20000, 80000);
  std::uniform_real_distribution<double> lifeDist(70, 85);
  std::uniform_real_distribution<double> popDist(10, 1400);
  std::uniform_real_distribution<double> happinessDist(4.5, 8.0);
 
  std::vector<std::string> regionList = {"North America", "Asia", "Europe",
                                         "South America", "Oceania"};
 
  // Generate data with some correlation patterns
  for (size_t i = 0; i < n; i++) {
    double gdp = gdpDist(gen);
    double life =
        65 + (gdp - 20000) / 2000 + std::normal_distribution<double>(0, 3)(gen);
    double pop = popDist(gen);
    double happy =
        3 + life / 15 + std::normal_distribution<double>(0, 0.8)(gen);
 
    // Clamp values to reasonable ranges
    life = std::max(65.0, std::min(85.0, life));
    happy = std::max(3.0, std::min(8.5, happy));
 
    gdpPerCapita.push_back(gdp);
    lifeExpectancy.push_back(life);
    population.push_back(pop);
    happiness.push_back(happy);
    regions.push_back(regionList[i % regionList.size()]);
  }
 
  // Create separate traces for each region
  std::vector<plotly::Object> traces;
  std::vector<std::string> colors = {"red", "blue", "green", "orange",
                                     "purple"};
 
  for (size_t r = 0; r < regionList.size(); r++) {
    std::vector<double> regionGDP, regionLife, regionPop, regionHappy;
    std::vector<std::string> regionCountries;
 
    // Filter data by region
    for (size_t i = 0; i < n; i++) {
      if (regions[i] == regionList[r]) {
        regionGDP.push_back(gdpPerCapita[i]);
        regionLife.push_back(lifeExpectancy[i]);
        regionPop.push_back(population[i]);
        regionHappy.push_back(happiness[i]);
        regionCountries.push_back(countries[i]);
      }
    }
 
    if (!regionGDP.empty()) {
      plotly::Object trace = {
          {"type", "scatter"},
          {"mode", "markers"},
          {"x", regionGDP},
          {"y", regionLife},
          {"text", regionCountries},
          {"name", regionList[r]},
          {"marker",
           {{"size", regionPop},
            {"sizemode", "diameter"},
            {"sizeref", 3.0},
            {"sizemin", 4},
            {"color", regionHappy},
            {"colorscale", "Viridis"},
            {"showscale", r == 0}, // Show colorbar only for first trace
            {"colorbar",
             {{"title", "Happiness Score"}, {"titleside", "right"}}},
            {"line", {{"color", colors[r]}, {"width", 2}}}}},
          {"hovertemplate",
           "%{text}<br>GDP per Capita: $%{x:,.0f}<br>" +
               std::string("Life Expectancy: %{y:.1f} years<br>") +
               "Population: %{marker.size:.0f}M<br>" +
               "Happiness: %{marker.color:.1f}<extra></extra>"}};
      traces.push_back(trace);
    }
  }
 
  // Create layout
  plotly::Object layout = {
      {"title",
       {{"text", "World Development Indicators<br>" +
                     std::string("<sub>Bubble size = Population (millions), ") +
                     "Color = Happiness Score</sub>"},
        {"font", {{"size", 16}}}}},
      {"xaxis",
       {{"title", "GDP per Capita (USD)"},
        {"type", "log"},
        {"showgrid", true}}},
      {"yaxis", {{"title", "Life Expectancy (years)"}, {"showgrid", true}}},
      {"width", 1000},
      {"height", 700},
      {"showlegend", true},
      {"hovermode", "closest"}};
 
  // Create the plot
  fig.newPlot(traces, layout);
 
  if (!args.headless) {
    fig.waitClose();
  } else {
    // Save image instead of opening browser
    plotly::Object imageOpts = {{"format", "png"},
                                {"width", 1000},
                                {"height", 700},
                                {"filename", "scatter_bubble_chart"}};
    fig.downloadImage(imageOpts);
  }
 
  return 0;
}