An Jingyi

@ajy2000·8 public vizzes

Temperature in San Francisco Line Chart

This line chart displays one week of temperature data in San Francisco, using a smooth curved line to show temperature fluctuations over time. The visualization uses D3 v5's `scaleTime` and `scaleLinear` for the axes, `line` with `curveBasis` for the path, and `axisBottom` and `axisLeft` for labeled ticks. Data comes from the Data Canvas - Sense Your City project, loaded via `csv` and parsed into `Date` and numeric values. The SVG render includes a maroon stroke path, grid lines, and axis titles for time and temperature.

May 8, 2020

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Temperature in San Francisco Line Chart

This line chart visualizes a week of temperature data in San Francisco, with time on the x-axis and degrees Celsius on the y-axis. The data originates from the Data Canvas - Sense Your City project and is loaded via a CSV file. Built with D3 v5, the visualization uses `scaleTime`, `scaleLinear`, `axisBottom`, `axisLeft`, and a `line` generator with `curveBasis` for smooth interpolation. The SVG rendering includes styled grid lines, axis labels, and a title, with the chart drawn as a maroon path.

May 8, 2020

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Temperature in San Francisco Line Chart

This line chart visualizes one week of temperature data in San Francisco, sourced from Data Canvas: Sense Your City. It shows how temperatures fluctuate over time, with a smooth curved line connecting hourly readings. The visualization uses D3 v5 APIs including `scaleTime`, `scaleLinear`, `axisBottom`, `axisLeft`, and `line` with `curveBasis` to render the path. The chart is drawn as an SVG, with styled axis labels, tick marks, and a title, using data parsed from a CSV file.

May 8, 2020

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Making a Bar Chart

This bar chart displays the top 10 most populous countries based on 2018 estimates from the United Nations World Population Prospects 2017. The visualization uses D3 v5’s `scaleLinear`, `scaleBand`, `axisLeft`, and `axisBottom` to render the SVG, with a custom tick formatter converting values to billions. Grid lines are drawn via the x-axis, while the y-axis domain and tick lines are removed for a cleaner look. Population data is loaded from a local CSV file using `d3.csv` and scaled by 1,000.

Apr 29, 2020

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D3 Page Template

This example uses D3 v5 to render a simple bar chart. It creates an SVG canvas of 300 by 300 pixels, then defines a dataset of six numeric values. The code selects the SVG element, binds the data to it, and appends rectangles with widths proportional to the data values. Each rectangle’s height is fixed at 30 pixels minus a small gap, and its y-position is offset by index. The bars are uniformly colored steel blue. The rendering is purely SVG-based, using `d3.select`, `.append`, `.data`, and `.enter` to construct and position the bars.

Apr 29, 2020

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React Starter

This example shows a minimal React application that displays the text “Hello React!” centered on the page. It renders a single component, `App`, which returns a `<div>` with the CSS class `message`, styled with a large font size. The code imports React and ReactDOM, then calls `ReactDOM.render` to mount the component into the `#root` element of the HTML document. The page loads React and ReactDOM from the unpkg CDN, with the application logic in a separate `bundle.js` file.

Apr 29, 2020

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React Starter

This example shows a minimal React application that renders a static text message, “Hello React!” in large, centered type. It displays the core pattern of a React component returning HTML through JSX syntax, with the component composed of a simple `div` element carrying a CSS class for styling. The app loads React 16.10.0 and ReactDOM from unpkg CDN, then uses `ReactDOM.render` to mount the component into a root `<div>` element defined in `index.html`. The accompanying `bundle.js` would contain the compiled JavaScript, though the visible code alone does not demonstrate any data processing or interaction.

Apr 29, 2020

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Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data using D3 v5, displaying the file’s size, row count, and column count as text. It fetches the CSS Named Colors dataset from a GitHub gist via `d3.csv`, then formats the result with `d3.csvFormat` to compute the kilobyte size. The visualization also includes commented alternatives using `fetch` with `async`/`await` and `d3.csvParse`, highlighting different parsing approaches. The output appears in a `<pre>` element with large font styling, showing only textual metadata rather than a graphical chart.

Apr 29, 2020