Fonda Irena Santoso

@fistz24·34 public vizzes

Polished Combined map and histogram with Brushing

The visualization pairs a natural Earth projection map with a time-series histogram, showing the total number of dead and missing migrants by reported date and location. A brushing interaction on the histogram filters the map’s circular markers, which are sized with d3.scaleSqrt based on migrant counts. The map uses d3.geoNaturalEarth1 and d3.geoPath with TopoJSON world-atlas data, while the histogram is built with d3.bin, d3.brushX, and React.

Apr 25, 2021

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Combined map and histogram with Brushing

This interactive visualization combines a world bubble map with a time-series histogram to explore missing migrant incidents. The map uses a natural Earth projection via `d3.geoNaturalEarth1` and `d3.geoPath` to render country boundaries and graticules, with circle radii scaled by `d3.scaleSqrt` based on the total dead and missing. Below, a histogram bins incidents by reported date using `d3.bin` and `d3.timeMonths`, with bar heights summed per bin. Users can brush over the histogram with `d3.brushX` to filter the map’s displayed circles. Data comes from a CSV of missing migrants, rendered in SVG with React.

Apr 25, 2021

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Combined map and histogram

A combined map and histogram visualizes the geographic distribution and temporal trends of missing migrants. The map projects city locations using a natural Earth projection via `d3.geoNaturalEarth1` and `d3.geoPath`, with bubble sizes scaled by the square root of the total dead and missing count. Below, a time-series histogram bins reported dates monthly using `d3.histogram`, with bar heights summing the casualties per month. The visualization uses React for component structure and SVG for rendering, with data from the Missing Migrants CSV and world atlas TopoJSON.

Apr 25, 2021

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Migrant Map

A world map visualizes missing migrant incidents, with semi-transparent purple circles sized by the total number of dead and missing at each location. The visualization uses a natural Earth projection via `d3.geoNaturalEarth1` and `d3.geoPath` to render landmasses, country interiors, and graticules from TopoJSON world atlas data. Circle radii are scaled with `d3.scaleSqrt` based on data from a CSV of missing migrants. The React-based implementation loads the data and map topology asynchronously using hooks before rendering SVG circles over the geographic base.

Apr 25, 2021

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Migrant Deaths

This visualization displays the total number of migrant deaths and missing persons over time, using a bar chart where each bar aggregates data by month. Built with React and D3 v5, it renders as an SVG and leverages the `histogram` and `timeMonths` functions from D3 to bin data, with axes created via custom `AxisBottom` and `AxisLeft` components. The data is loaded from a CSV file of the Data Canvas Sense Your City dataset.

Apr 25, 2021

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Points on A Map 2

A world map displays the distribution of cities across the globe, with each city represented by a circle sized according to its population. The visualization uses d3.geoNaturalEarth1 and d3.geoPath to render landmasses and graticules from World Atlas TopoJSON data. City locations are projected via the same projection, with circle radii scaled using d3.scaleSqrt and d3.max. The SVG rendering includes a subtle sphere outline, semi-transparent land fills, and yellow country borders, while population data comes from a CSV file of world cities.

Apr 23, 2021

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Points on a Map

A world map displays cities with populations over 50,000 as semi-transparent points. The visualization uses D3’s `geoNaturalEarth1` projection and `geoPath` to render landmasses, country interiors, and graticules from TopoJSON data. City coordinates are projected and drawn as SVG circles, with data loaded from a CSV via `d3.csv` in React hooks. The map prioritizes geographic context over point density, using a light gray palette for boundaries and teal circles for city locations.

Apr 23, 2021

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Interactive Color Legend

This interactive scatter plot visualizes the Iris dataset, allowing users to explore relationships between different flower measurements. Hovering over a species in the color legend highlights all corresponding data points, while fading the opacity of others for focused comparison. The visualization uses d3.v5 for scales and axes, rendered as SVG within a React framework, with dropdown menus to switch the x and y attributes. Data is loaded from a CSV via d3.csv.

Apr 23, 2021

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Polished Scatter Plot with Menus with more colors

This interactive scatter plot visualizes the classic Iris flower dataset, allowing users to explore relationships between sepal and petal measurements across three species. Two dropdown menus enable selection of the x and y axes from four numerical attributes and the species label, with colors distinguishing species via a legend. Built with React and D3 v5, the visualization uses `scaleLinear` and `scaleOrdinal` scales to map data to SVG circles, with axes rendered by custom components and data loaded via the `csv` function.

Apr 23, 2021

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Polished Scatter Plot with Menus

This interactive scatter plot visualizes the classic Iris flower dataset, allowing users to explore relationships between sepal and petal measurements across species. The chart uses SVG rendering within a React framework, with D3 v5's `scaleLinear` and `extent` APIs to map data to coordinates. Interactive dropdown menus, created with the `react-dropdown` library, let users select which attributes to plot on the x and y axes. The visualization includes custom axes with formatted tick labels and circular marks with tooltips for each data point.

Apr 23, 2021

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Scatter Plot with Menus

This interactive scatter plot visualizes the classic Iris flower dataset, which contains measurements of sepal and petal dimensions across three species. Users can dynamically select which numerical attribute appears on the x-axis and y-axis using dropdown menus built with React hooks (useState). The visualization uses d3.scaleLinear for both axes, computes domains via d3.extent, and renders the plot as an SVG with custom React components for the axes and circular marks. D3’s csv loader fetches the data from a remote GitHub gist, and axis ticks are formatted with d3.format.

Apr 23, 2021

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Untitled

A stylized smiley face rendered as a collection of overlapping SVG petal and leaf paths, generated with React and D3 v4. The example defines several petal path shapes and a leaf shape, then uses D3’s `arc` API and React to compose them into a decorative face pattern. The code imports `arc` from `d3` and uses JSX to output the SVG elements, with paths transformed and positioned to create the smiley appearance.

Apr 22, 2021

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Dropdown Menu

This example demonstrates a dropdown menu for selecting a pet, with the chosen value displayed in the browser’s console. The interface is built using React hooks (`useState`) to manage the selected option and a custom `Dropdown` component that renders a native `<select>` element. Styling is applied via CSS, while the page loads React, ReactDOM, and D3 v5 from CDNs, though D3 itself is not used in the visible logic. The rendering is SVG-based, as indicated by commented-out circles in the HTML markup.

Apr 22, 2021

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Fork of Polished World Map

This visualization presents a polished world map using a Natural Earth projection, displaying country borders, land masses, graticules, and a sphere outline. The land is filled with a teal color, while country boundaries appear as yellow mesh lines against a light gray sphere. The code loads geoJSON data from the World Atlas TopoJSON dataset via `d3.json`, converts it using `topojson.feature` and `topojson.mesh`, and renders the map with `d3.geoPath` and `d3.geoNaturalEarth1`. The React-based SVG rendering in a 960×500 viewport applies CSS styling for the distinct color palette.

Apr 22, 2021

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Polished World Map

This example displays a polished world map rendered with a Natural Earth projection, using a teal color scheme for landmasses and muted grays for the sphere and graticules. It shows country borders as interior mesh lines, with the map filling a 960 by 500 pixel SVG canvas. The visualization is built with React and D3 v5, employing `d3.geoNaturalEarth1` and `d3.geoPath` to project and draw geographic features, while `d3.geoGraticule` generates the latitude/longitude grid. TopoJSON data from the world-atlas package is fetched to create the land and border geometries.

Apr 22, 2021

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Stylized Line Chart

This example displays a line chart tracking daily average temperatures in San Francisco over time, using the `d3.csv` loader to read the week_temperature_sf dataset. The chart uses `scaleTime` for the x-axis, `scaleLinear` for the y-axis, and renders the line and points as SVG elements via React components. It employs `d3.line()` to draw the path connecting the data points, with colored axis labels, ticks, and grid lines customized through CSS. Hovering over each circle shows a tooltip with the date.

Apr 22, 2021

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Stylized Scatter Plot II

This stylized scatter plot visualizes the classic Iris dataset, mapping petal length on the x-axis and sepal width on the y-axis with purple axis lines and lime-green circular marks. Built with React and D3 v5, it uses d3.scaleLinear and d3.extent to compute scales, while d3.csv loads the data remotely. The SVG rendering includes custom axis components, a rotated y-axis label, formatted tick values via d3.format, and a title tooltip on each circle.

Apr 22, 2021

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Stylized Bar Chart

This stylized bar chart displays the population of the top ten countries, ranked by their 2020 estimates. The chart is rendered as a horizontal bar chart with a distinctive green color scheme for axes and teal bars. Built using React and D3 v5, the code loads a CSV file of UN population data and uses `scaleBand` and `scaleLinear` to map countries and populations. SVG elements are generated via React components, with custom tick labels formatting values in billions.

Apr 22, 2021

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Stylized Scatter Plot

This stylized scatter plot visualizes the Iris flower dataset, mapping petal length against sepal width for individual specimens. The visualization uses D3.js utilities—including `csv` for data loading, `scaleLinear` for axes, and `extent` for domain calculation—combined with React to render SVG circles and axis ticks. Each data point is drawn as a small teal circle with a tooltip displaying its value. The chart includes labeled axes with customized tick formatting and a responsive layout, all styled with the Poppins font family.

Apr 22, 2021

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Refactored Bar Chart

This visualization shows the population of the top 10 countries in 2020 as a horizontal bar chart, with all bars aligned to the left and sorted by country name. The application loads the data from a CSV file using D3’s `csv` utility, then renders the SVG with React components for the axes and marks. The y-axis uses `scaleBand` to position categorical labels, while the x-axis uses `scaleLinear` for the numeric population values. Each bar is drawn as a `<rect>` via the `Marks` component, with size determined by the population value.

Apr 21, 2021

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Axes with react and d3

A bar chart displays the 10 most populous countries for 2020, sourced from a UN Population dataset loaded via D3’s `csv`. React renders SVG elements, while D3’s `scaleBand` and `scaleLinear` compute axes and bar positions. Custom tick marks and labels are generated from `xScale.ticks()`, with the chart wrapped in a margin-based `<g>` group.

Apr 21, 2021

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The Margin Conventiion

This example visualizes the top 10 countries by 2020 population as a horizontal bar chart, with each bar’s length proportional to its population value. The chart applies a margin convention to position the bars within an SVG, using a band scale for country categories and a linear scale for population magnitudes. Data is loaded from a CSV file using `d3.csv`, while React manages the component state and rendering. Bars are drawn as SVG `<rect>` elements, with axes omitted, and the chart updates after the data asynchronously loads.

Apr 21, 2021

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Bar Chart I

This bar chart displays the top 10 countries by population in 2020, sorted alphabetically, with bars extending horizontally to represent each country’s population value. The data is loaded asynchronously from a CSV file using D3’s `csv` function, and the visualization is built with React components rendering SVG `rect` elements. D3’s `scaleBand` and `scaleLinear` map country names to vertical positions and population counts to bar widths, respectively. The chart updates its layout only after the data finishes loading, showing a loading message beforehand.

Apr 21, 2021

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Loading Data with React and d3

This example shows a React application that loads a CSV dataset of CSS named colors and renders it as a color wheel using d3.pie and d3.arc. The data is fetched asynchronously with the d3.csv function, and React’s useState and useEffect hooks manage the loading state. Each path in the SVG is filled with the corresponding RGB hex value from the dataset, creating a full-circle pie chart where every color occupies an equal slice.

Apr 21, 2021

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Fork of Loading and Parsing CSV Data

This example demonstrates loading and parsing CSS Named Colors data from a CSV file using D3 v5’s `d3.csv` method, which returns a promise. The visualization displays three summary statistics as large text: the file size in kilobytes, the number of data rows, and the number of columns. The code also includes commented alternatives using `fetch`, `async`/`await`, and `d3.csvParse`, plus a “pyramid of doom” nested fetch example, illustrating different approaches to asynchronous data loading. The computed values are shown in a `<pre>` element without any graphical chart.

Apr 21, 2021

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Fork of Vega-Lite API Template

This example visualizes weekly temperature readings for multiple locations, plotting each measurement as a point in a scatterplot with time on the x-axis and temperature on the y-axis. Hovering over a point reveals the location name via a tooltip. The visualization is built using the Vega-Lite API, which registers Vega and Vega-Lite libraries to produce an SVG rendering. Data is loaded asynchronously from a CSV file using D3’s `csv` function, and the chart is configured to fit the full browser window with custom axis styling.

Apr 21, 2021

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Fork of Vega-Lite API Template

This visualization uses the Vega-Lite API to render interactive scatter plots of the Auto MPG dataset from the UCI Machine Learning Repository. The code applies the Vega-Lite API’s `markCircle` and `encode` methods to map vehicle weight to the x-axis, horsepower to the y-axis, and miles per gallon to point size and color, with tooltips showing car names. It retrieves data via d3’s `csv` loader, registers the Vega and Vega-Lite libraries, and customizes axis styling through a separate config object. The visualization is built to fill the browser window entirely.

Apr 21, 2021

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Fork of Vega-Lite API Template I

This interactive scatter plot visualizes the relationship between fuel efficiency (mpg), horsepower, weight, and country of origin for automobiles from the UCI Auto MPG dataset. The visualization uses the Vega-Lite API to declaratively encode data fields onto circular marks, with color distinguishing origin and size mapping to weight. It reads CSV data via `d3.csv`, renders with Vega’s SVG renderer, and includes Vega-Tooltip for interactive hover details. The chart resizes to fit the browser window and applies custom axis and label styling for readability.

Apr 21, 2021

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Loading and Parsing CSV Data

This example shows a minimal web page that fetches a CSV file of CSS named colors and reports its size and structure. The page displays the file size in kilobytes, the number of data rows, and the number of columns in large, preformatted text. The code uses the native `fetch` API to retrieve the CSV text, then parses it with `d3.csvParse` from D3 v6. It counts rows via `data.length` and columns via `data.columns.length`, updating the DOM through `textContent`. The data comes from a GitHub Gist hosted CSV file.

Apr 20, 2021

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Mouse Follower

A black circle on a white background follows the mouse cursor across an SVG canvas. The example illustrates React state management and event handling by updating the circle's center coordinates in response to every mouse move. The `App` component uses React's `useState` hook to track the cursor position and `useCallback` to define the move handler. React renders the SVG element, updating the `cx` and `cy` attributes reactively.

Apr 20, 2021

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Mouse Follower

A smiling face rendered as a React SVG component follows the mouse cursor across the full browser window. The face consists of concentric circles for the head, eyes, and mouth, all defined with static SVG properties. Using React 16.9 and ReactDOM, the component updates its position on every mousemove event, tracking clientX and clientY coordinates to re-render the graphic dynamically.

Apr 20, 2021

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Loading and Parsing CSV Data

This example demonstrates how to fetch and parse a CSV file using the browser’s native Fetch API, then logs the raw text content to the console. It defines an async `fetchText` function that retrieves data from a GitHub-hosted CSS Named Colors CSV file and prints it. The code relies solely on vanilla JavaScript and the `fetch` and `response.text()` methods, without any external libraries or visualization frameworks. It serves as a minimal starter for loading tabular data before any parsing or charting can occur.

Apr 20, 2021

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Fork of Smiley Face Part III

A React and D3.js v5 example renders a yellow smiley face using SVG primitives. The face is drawn with circles for the head, eyes, and a path created with the `d3.arc()` function for the mouth. All coordinates are computed relative to the center of the viewport, with React managing the SVG elements and D3 handling the arc path generation.

Apr 20, 2021

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Main Smiley

A stylized smiley face rendered as an SVG using React components, with D3’s `arc` generator drawing the mouth. The face is composed of reusable pieces—a yellow background circle, two dark eyes with white highlights, and an arc-based smile—all positioned relative to a central coordinate system. React props control geometry like eye offset, radius, and stroke width, while the D3 arc’s `innerRadius`, `outerRadius`, and start/end angles define the mouth’s curvature. The final shape is assembled through nested React components and mounted into the DOM via ReactDOM.

Apr 20, 2021