Skip to main content
SimilarExport ZIP
100%

Data on CO2 and Greenhouse

✓ Published0🌍 Public
Oomniaabouhassan@gmail.com
Last edited Sep 10, 2024
Created on Sep 4, 2024

This example displays the first row of a CSV dataset on CO2 and greenhouse gas emissions by Our World in Data. The code loads the data, parses it, and formats it as a JSON object, which is then rendered in a `<pre>` element using `JSON.stringify`. The main visualization shows how the data is structured and highlights the various metrics tracked, including emissions by source, per-capita values, and cumulative totals.

AI-generated description

The Data on CO2 and Greenhouse Gas Emissions by Our World in Data, loaded and parsed as CSV.

This dataset includes data on CO2 and other greenhouse gas emissions, energy mix, and other relevant metrics, maintained by Our World in Data.

Tasks

  1. Analyze CO2 Emissions by Country:

    • Visualize and compare CO2 emissions for each country to identify the largest and smallest emitters. This can include creating bar charts or maps.

  2. Examine Trends in CO2 Emissions Over Time:

    • Track changes in CO2 emissions for different countries over the years to identify trends, such as increasing or decreasing emissions.

  3. Investigate the Relationship Between CO2 Emissions and Economic Indicators:

    • Explore how CO2 emissions correlate with economic indicators like GDP or population growth to understand the impact of economic activity on emissions.

  4. Compare CO2 Emissions from Fossil Fuels and Cement Production:

    • Analyze and compare the contributions of different sources of CO2 emissions, such as fossil fuels and cement production, to identify major contributors.

  5. Identify Regions with High CO2 Emissions:

    • Group countries by region (e.g., continents) and compare CO2 emissions to identify regions with the highest emissions.
MIT Licensed

Similar vizzes

Loading thumbnail…

Global Data

This example displays a single country’s complete record from a global dataset as a formatted JSON object. It uses D3.js and a custom CSV parser to load and convert numeric fields, then renders the first data entry in a `<pre>` block. The visualization provides a direct, code-level view of the dataset structure, showing all variables like GDP, fertility, and CO2 emissions for a given nation.

Nnitinramagiri1@gmail.com
74% match
Loading thumbnail…

Fork of Loading Data - CO2 Emissions

This example shows raw historical CO2 emissions data from a Kaggle dataset, displaying each row as a JSON-formatted object in a `<pre>` element. The visualization begins with a loading state, then fetches and parses the CSV file using the Fetch API and D3’s `csvParse`. It converts the `year` and `value` columns to numbers and stores the array of country-level records in the app’s state, which is rendered via D3’s `select` and `join` methods.

BBrian Hulbert
73% match
Loading thumbnail…

CO2

This visualization displays global CO2 emissions from 2000 to 2014, showing a steady decline in values over time. The data is loaded from a CSV file using the D3 v4 and v5 libraries, with each value converted to a numeric format. The page, structured around an HTML container, fetches the dataset and logs it to the console, though the chart itself is not yet rendered. The example relies on D3’s `csv` method for data parsing and demonstrates a foundational step in building a time-series bar or line chart.

Llamiyaa
73% match
Loading thumbnail…

Ingesting-Data

This example fetches a CSV of greenhouse gas emissions by life-cycle stage from a GitHub-hosted Our World in Data upload, then uses `d3.csvParse` to convert the raw text into a structured array. It displays a summary of the data—approximate file size in kilobytes, row count, and column count—in a pre-formatted message area. The code also demonstrates chaining `d3.select`, `append`, and `attr` calls to construct an SVG circle and group, though no data-driven geometry is rendered. The page uses React and ReactDOM loaded via UMD, but the visible output relies on plain DOM text updates.

RRoland
71% match
Loading thumbnail…

Data Summary

This example displays a text-based summary of a greenhouse gas emissions dataset, which includes the number of rows, columns, and file size, followed by a per-column statistical breakdown. The data is loaded from a CSV file using D3.js’s `d3.csv` method, and the summary is generated with the datalib library’s `dl.format.summary` function. The resulting text is rendered on the page using `d3.select('body').append('pre')` and also logged to the console.

RRobertDelgado
70% match
Loading thumbnail…

Global Tons of CO2 Emissions V2

This visualization shows global CO2 emissions on a world map from 1850 to 2023, using color intensity to represent emission levels. A slider enables year selection, while a rectangular legend maps the green-to-red gradient to millions of tons. The map is rendered with React and D3, using `d3.geoNaturalEarth1` and `d3.geoPath` for geographic projection. Emission data comes from a CSV file, and the color scale applies a fifth-root transformation to visually differentiate lower values.

BBrian Hulbert
69% match
Loading thumbnail…

Fork of Green house gas bar chart

This bar chart displays greenhouse gas emissions (in MtCO₂e) for several countries, using horizontal bars ordered by country name. Built with D3 v7, it maps population values to a linear x-scale and country names to a band y-scale, rendering SVG rectangles with axes. A custom tooltip appears on mouseover, showing the emission value, while the bar color darkens slightly on hover. Data is loaded from a CSV file and parsed as numbers before rendering.

VVitor Leonel
69% match
Loading thumbnail…

CO2 Emissions

This interactive world map highlights the ten countries with the highest CO2 emissions, coloring them green and labeling each with its name. Hovering over a highlighted country displays its emission value in million metric tons. The visualization uses a Natural Earth projection via `d3.geoNaturalEarth1` and renders country boundaries with `d3.geoPath`, pulling geographic data from the visionscarto-world-atlas TopoJSON file, which is converted with `topojson-client`. Country names and values are positioned using `d3.geoCentroid`.

Nnitinramagiri1@gmail.com
68% match