parsing Coronavirus data

Loading and Parsing Coronavirus Data

This visualization demonstrates how to load and parse the Johns Hopkins Coronavirus Dataset, displaying basic file statistics in a large preformatted text element. It fetches the global deaths time series CSV via `d3.csv`, then computes the data size in kilobytes using `d3.csvFormat`, and counts the rows and columns from the parsed array and its `data.columns` property. The final message shows the latest date column, total file size, row count, and column count, all rendered into the DOM via `textContent`. The example highlights D3’s Promise-based data loading and inspection of CSV metadata.

KK3zter

Loading and Parsing Coronavirus Data

This example demonstrates how to load and parse a COVID-19 time-series dataset from the CSSE at Johns Hopkins University using D3 v5. It displays the file size in kilobytes, the number of rows, and the number of columns in a large preformatted text element. The code relies on the `d3.csv` method to fetch and parse the data asynchronously, then uses `d3.csvFormat` to measure the formatted output size. The visualization is minimal, focusing solely on the parsed dataset's metadata rather than any chart or graphical representation.

Ddaneslutzky

Loading and Parsing Coronavirus Data

This example displays the total number of COVID-19 deaths worldwide as a large text message, using a comma-formatted number and the most recent date from the dataset. It loads a time-series CSV file from the CSSE at Johns Hopkins, parsing it with `d3.csv` and summarizing the latest column with a `reduce` function. The code uses D3 v6’s `format` for number localization and React to update the message container via `getElementById`. The visualization renders as plain SVG-free text, highlighting the data-loading and parsing workflow rather than a chart.

AAndres de la Garza

Loading and Parsing Coronavirus Data

This example displays the size and structure of the Johns Hopkins Coronavirus time-series dataset as large text. It uses D3 v5’s `d3.csv` to load the global COVID-19 deaths CSV and `d3.csvFormat` to measure the file size in kilobytes. The code then counts the number of rows and columns and updates the page’s `<pre>` element with these three statistics.

RRMartins11

Loading and Parsing Coronavirus Data

This example demonstrates how to load and parse the Johns Hopkins Coronavirus Dataset using D3 v6. The visualization displays three key statistics about the CSV file: its size in kilobytes, the number of rows, and the number of columns. The code uses `d3.csv` to asynchronously fetch the data from a GitHub URL, then leverages `d3.csvFormat` to calculate file size and accesses `data.columns` and `data.length` for column and row counts. Results are rendered as text in an HTML `<pre>` element using standard DOM methods.

WWolfgang

Fork of Loading and Parsing Coronavirus Data

This example displays the parsed size, row count, and column count of the Johns Hopkins Coronavirus time-series dataset as oversized text in a `<pre>` element. It loads the CSV file from a pinned GitHub commit using `d3.csv`, then computes the formatted data length with `d3.csvFormat`. The visualization relies on D3 v5 (`d3.v5`) to fetch and parse the remote data, while using native DOM manipulation to update the message container.

Mmaciekbanas

loading and parsing Coronavirus data

This example demonstrates how to load and parse the Johns Hopkins Coronavirus global deaths dataset, displaying key metrics in oversized monospace text. It uses `d3.csv` to fetch the raw CSV and calculates the file size in kilobytes, row count, and column count from the parsed data. The visualization also logs the latest date column, showing the time series structure. The code includes commented alternatives using `fetch`, `async`/`await`, and `d3.csvParse`, illustrating different parsing approaches without rendering a chart.

YYedrup

Coronavirus Basic Line Chart

This example displays a basic line chart showing COVID-19 data, with a text summary of the loaded dataset printed above the visualization. The chart renders using D3 v7’s `csv` loader to fetch data from a GitHub Gist, while React manages the component state and asynchronous data loading. The `message` function calculates and displays the dataset’s size in kilobytes, row count, and column count using `d3.csvFormat`. The visualization itself is minimal, focusing on the data summary rather than a graphical line plot, and runs within a React 16.9 application using `ReactDOM.render`.

OOleg Zhernovoy