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viz-happiness-2024
This example displays the raw data from the World Happiness Report 2024 as a formatted JSON preview, showing country names and their associated happiness indicators including Ladder score, social support, and GDP per capita. The visualization loads data from a CSV file using the `fetch` API and parses it with `d3.csvParse`, converting numeric fields from strings to numbers. The code renders the parsed data structure in a `<pre>` element, providing a simple text-based investigation of the dataset before any graphical mapping is applied. The implementation follows a state management pattern where data loading triggers a re-render of the view.
AI-generated descriptionDataset: World Happiness Report 2024
Source URL: World Happiness Report 2024 Data
Description
This dataset comes from the World Happiness Report 2024, which ranks countries based on their citizensโ self-reported well-being.
The primary measure is the Ladder score (happiness index), which is explained by six key factors: GDP per capita, social support, healthy life expectancy, freedom of choice, generosity, and perceptions of corruption. The dataset also includes confidence intervals and a residual factor.
The goal of using this dataset in our visualization project is to explore how different countries compare in terms of happiness and which factors contribute the most to their scores.
Attribute Types
- Country name: Categorical
- Ladder score: Quantitative
- upperwhisker / lowerwhisker: Quantitative
- Log GDP per capita: Quantitative
- Social support: Quantitative
- Healthy life expectancy: Quantitative
- Freedom to make life choices: Quantitative
- Generosity: Quantitative
- Perceptions of corruption: Quantitative
- Dystopia + residual: Quantitative