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surikmeetr

@surikmeetr·53 public vizzes

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Coronavirus Deaths Over Time by Country With Voronoi Overlay and tooltips

This line chart shows cumulative coronavirus deaths over time for countries with more than 1,000 total deaths, using a log scale for the y-axis. A Voronoi overlay, built with `d3.Delaunay`, enables hover detection on the dense line chart, highlighting the active country’s path in black. The visualization uses React with `d3.scaleTime`, `d3.scaleLog`, and `d3.line`, rendering SVG elements with axes from `d3.axisBottom` and `d3.axisLeft`. Data comes from the Johns Hopkins Coronavirus Dataset and is loaded via `d3.csv`.

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Fork of Coronavirus Deaths Over Time by Country With Voronoi Overlay

This visualization shows cumulative COVID-19 deaths over time for each country, plotted on a logarithmic scale with a Voronoi overlay that highlights the nearest data point to the cursor, displaying the active country's name. Built with React and D3 v5, it uses `scaleTime` for the date axis, `scaleLog` for the death counts, `d3.line` for the polylines, and `d3.Delaunay` from the `d3-delaunay` package to compute the Voronoi cells for hover detection. Data comes from the Johns Hopkins CSSE GitHub repository.

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Coronav deaths over time by country voronoi overlay

This example shows cumulative COVID-19 death totals over time for multiple countries, plotted as small multiples on a single log-scaled chart. The visualization uses a Voronoi overlay to partition the chart area, enabling hover interactions across closely clustered lines. Built with React and D3 v6, it fetches data from the CSSEGISandData COVID-19 GitHub repository, transforms the CSV into per-country time series, and renders SVG paths with `scaleLog`, `scaleTime`, and `line`. The `VoronoiOverlay` component uses `d3.Delaunay` to generate the overlay regions.

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Coronav deaths over time by country

A multi-line chart tracks cumulative COVID-19 deaths over time for each country, using a logarithmic y-axis to accommodate wide value ranges. The visualization loads data from a CSSEGISandData GitHub CSV and renders it as SVG paths via React. D3’s `scaleTime`, `scaleLog`, and `line` generator plot the series, while custom React components handle `axisBottom` and `axisLeft` with gridlines. Each country’s line receives a randomly generated RGB stroke color. The chart includes axis labels and a title, with tick formatting showing month/year and abbreviated large numbers.

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Coronav deaths over time by country (linear)

A multi-line chart tracks cumulative COVID-19 deaths over time for each country, with one colored path per nation. The visualization uses a linear y-scale and a time-based x-axis, with gridlines and axis labels formatted via `d3.axisBottom` and `d3.axisLeft`. Data is loaded from a CSV using `d3.csv` and parsed with `d3.timeParse`. The React component builds an SVG, generating paths with the `d3.line` generator and rendering axes through custom React hooks that forward refs to D3 selection calls.

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React + d3 axes + log scale + labels

This example shows cumulative COVID-19 deaths over time as a single black line chart, with a logarithmic y-axis for deaths and a time-based x-axis formatted by month/year. The data comes from the CSSE COVID-19 time series CSV. Built with React and D3 v6, it uses `scaleTime`, `scaleLog`, and `line` generators, while `axisBottom` and `axisLeft` render labeled gridlines. The SVG includes axis titles and optional teal marker lines for highlighting specific values.

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React + d3 axes + log scale

This example shows a line chart of global COVID-19 death totals over time, using a logarithmic y-axis to accommodate the wide range of values. The chart is built with React and D3 v6, combining SVG rendering with D3’s `scaleLog`, `scaleTime`, `axisBottom`, and `axisLeft` APIs for the axes. The line is generated with `line()` and plotted from a CSV dataset fetched from the CSSE at Johns Hopkins, with data transformed via `csv` and `timeParse`.

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React + d3 axes X and Y

A line chart displays cumulative COVID-19 deaths over time using React components and D3 scales. The chart renders an SVG with a time-scaled X axis and linear Y axis, both with gridlines via `axisBottom` and `axisLeft`. A D3 `line` generator plots the data path, while styled tick lines and hidden domains create a clean aesthetic. Global death totals come from a CSV fetched with `d3.csv`, parsed with `timeParse` and aggregated by column.

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React + d3 axes

This example shows a line chart of global COVID-19 deaths over time, using a dataset fetched from the CSSE at Johns Hopkins. The visualization highlights specific y-axis milestones (1, 2, and 3 million deaths) with teal marker lines and a vertical marker at the latest date. The React component `LineChart` uses D3’s `scaleTime`, `scaleLinear`, `line`, and `axisBottom` to render axes and the line path, while separate React elements (`XMarkerLine`, `YMarkerLine`) draw the annotation lines. The SVG output is styled with CSS, hiding the axis domain and using light gray ticks for gridlines.

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Coronav deaths line chart 2 margin convention

A line chart tracks cumulative global COVID-19 deaths over time, with horizontal reference lines at one, two, and three million deaths and a vertical "now" marker at the latest date. The chart uses React with D3 v6, employing `scaleTime`, `scaleLinear`, and `line` to map dates and death totals, with margins applied via an SVG `<g>` transform. Data comes from the CSSE COVID-19 time series CSV, processed with `csv` and `timeParse` to aggregate daily global totals.

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Rapporto pcr/positivi per regione - line chart

A line chart displays the percentage of positive PCR tests per day for a selected Italian region, using a dropdown menu to switch between them. The data comes from the Italian government's COVID-19 regional dataset, filtered to the last 90 days. Built with React and D3 v6, the SVG visualization uses `scaleTime` for the x-axis, `scaleLinear` for the y-axis, and `line` from d3 to draw the path, with circles and tooltips for each data point.

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Tamponi sardegna: Rapporto pcr/positivi - line chart 7 mesi

This visualization shows a seven-month line chart tracking the percentage of positive PCR tests in Sardinia, Italy, with data sourced from the Italian government's regional COVID-19 dataset. The React-based chart uses d3.scaleTime and d3.scaleLinear to map dates and percentages, while d3.line with curveNatural renders the smooth path. Each data point is marked with an SVG circle that includes a hover tooltip via the title element, and axes are generated using custom React components that leverage d3's scale ticks for positioning.

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Coronav deaths line chart

This chart displays the total global COVID-19 deaths over time as a single black line rising across a white SVG canvas, with a teal horizontal marker line fixed at the one-million death threshold. The React component uses d3’s `scaleTime`, `scaleLinear`, and `line` generator to map dates and cumulative death totals from the CSSE COVID-19 time series dataset (a CSV loaded via d3.csv). The marker line’s position is computed with `yScale(1000000)`, while the path is rendered directly to SVG coordinates.

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Summarized deaths coronav

A minimal text-based visualization displays the cumulative global death count from the Johns Hopkins COVID-19 time series dataset, formatted with thousands separators and shown alongside the latest date column. The page loads the CSV via `d3.csv`, extracts the most recent column, and sums all numeric values using `Array.reduce`. A custom `d3.formatLocale` configuration formats the result with European decimal and grouping conventions, and the final message is injected into the DOM via `document.getElementById` and `textContent`. The visualization offers no charts or interactivity, focusing solely on the computed total.

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Parsing CSV data with D3

This example demonstrates how to parse CSV data with D3 by loading a remote file containing CSS color names and displaying basic summary statistics. It fetches the dataset from a GitHub gist using `d3.csv`, then computes the formatted CSV size in kilobytes, the number of rows, and the number of columns. The results are rendered as plain text in a `<pre>` element using standard DOM manipulation. The code relies on D3 v6 APIs `d3.csv`, `d3.csvFormat`, and `Math.round` to process and present the data metrics.

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HIV/AIDS choropleth map

This choropleth map visualizes the global prevalence of HIV/AIDS among adults aged 15-49 for the year 2017, with countries shaded on a sequential yellow-to-red color scale based on infection rates. Regions lacking data appear in dark gray. The map uses D3’s `geoNaturalEarth1` projection and `geoPath` to render country boundaries and graticules from World Atlas TopoJSON data. React hooks load the CSV dataset and ISO country codes, while `scaleSequential` with `interpolateYlOrRd` maps prevalence values to fill colors.

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Line chart temp

A line chart displays the trend of total hospitalized patients over time in Sardinia, using a dataset from Data Canvas Sense Your City. The visualization maps dates on a time scale and patient counts on a linear scale, with a smooth curve rendered via d3.line and curveNatural. Data points are marked with circles that have hover tooltips, while custom React components, AxisBottom and AxisLeft, render the axes using d3 scaleTime and d3 scaleLinear. The SVG is styled with Poppins font and subtle gray ticks.

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Missing migrants log scale

This scatter plot visualizes the Missing Migrants Dataset, with each point representing a single migration event. The x-axis displays the reported date of the event in time series format, while the y-axis shows the total number of dead and missing individuals using a logarithmic scale. The visualization uses D3.js v5, including `scaleLog` and `scaleTime` for axis transformations, and is rendered as an SVG using React 16. The plot employs `d3.csv` for data loading and custom SVG marks with tooltips showing the reported date.

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Missing migrants with brushing

A choropleth-free world map displays the total number of missing migrants per incident as semi-transparent blue circles, sized with a square-root scale. A linked time-series histogram below shows monthly death tolls, and a D3 brushX selection on the histogram filters the map circles to the selected date range. The visualization uses d3.geoPath and geoNaturalEarth1 for the map projection, d3.bin with timeMonths for the histogram, and loads world geometry from World Atlas topojson and incident data from a CSV. React manages the state for the brush extent.

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Missing migrants multiple view

A map of the world uses D3's geoNaturalEarth1 projection and geoPath to draw landmasses and graticules, while circles sized with a square-root scale encode totals of dead and missing migrants per location. A time-series histogram beneath the map bins the same reported data by month using d3.bin and scaleTime, with bar heights scaled by monthly fatality counts. React manages the state and rendering of both views, loading world geometry from TopoJSON and CSV data from a gist.

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Missing migrants p1

A histogram displays the monthly total of dead and missing migrants, with rectangular bars representing cumulative counts over time. The chart uses React with D3 v6, employing d3.bin, d3.scaleTime, d3.scaleLinear, and d3.timeMonths to aggregate data from the Missing Migrants Project subset. Hovering over a bar reveals a tooltip showing the specific count and month/year. The SVG rendering includes custom axes for time and count, with a Poppins font and teal-colored marks.

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Missing migrants on map

This map visualizes the locations of missing migrants, with each circle sized proportionally to the total number of dead and missing individuals at that coordinate. The visualization uses a Natural Earth projection via `d3.geoNaturalEarth1` and `d3.geoPath` to render world landmasses, graticules, and country borders from TopoJSON data loaded through the World Atlas. Circle radii are scaled using `d3.scaleSqrt`, and the React components fetch data from a CSV of migrant incident coordinates. The semi-transparent teal circles overlay the map, highlighting geographic clusters of migrant disappearances.

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Fork of loading and parsing csv data part 2 await/async - ep 13

This example demonstrates asynchronous loading of a CSV file from a GitHub Gist using JavaScript’s `async/await` syntax within a minimal HTML page. The page displays no visual output; instead, the fetched text is logged to the browser console. The code uses the native `fetch` API to retrieve the remote `css_colors.csv` file, then `response.text()` to extract its contents. A large `13em` body font size is set via CSS, but no rendering or charting library, such as D3, is employed.

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Point size

A world map displays city populations as circles sized by a square-root scale. The visualization uses D3’s `geoNaturalEarth1` projection and `geoPath` for geographic features, with `scaleSqrt` mapping population values to circle radii. City coordinates come from a CSV, while land and country boundaries load from World Atlas TopoJSON. React handles component rendering, and D3’s `csv` and `json` fetch the data.

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Points on a map

This example displays a natural Earth projection world map with city locations plotted as semi-transparent teal circles. It loads world geography from the World Atlas TopoJSON and city coordinates from a CSV file. Using React hooks, it fetches both datasets and renders them as SVG elements. The map includes graticules, country boundaries, and interior borders, with cities positioned via the `d3.geoNaturalEarth1` projection and `d3.geoPath`. The visualization is built with D3 v6, React, and uses `d3.geoGraticule` for the background grid.

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interactive color legend

This interactive scatter plot of the Iris dataset allows users to explore relationships between sepal and petal measurements using dropdown menus to select variables for the x and y axes. The color legend for species is interactive: hovering over a legend item fades all other points to 20% opacity, highlighting the selected species in SVG circles rendered with React. The visualization uses d3-scale for linear and ordinal scales, d3-format to tick labels, and react-dropdown for the axis selectors, with CSV data fetched from a GitHub gist.

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Scatterplot with color and legend

This interactive scatterplot displays the Iris dataset, with dropdown menus letting users choose which sepal or petal measurements appear on the x- and y-axes. Points are colored by species via a categorical color scale, with a legend showing the three iris varieties. Built with React and D3 v6, it uses `scaleLinear` for axes, `scaleOrdinal` for colors, and `csv` to load data from a GitHub gist. SVG rendering handles marks, axes, and legend, while the react-dropdown library provides the selection controls.

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Scatterplot with color

This interactive scatterplot visualizes the Iris dataset, letting users explore relationships between sepal and petal measurements across three flower species. Each point is colored by species using d3.scaleOrdinal, with dropdown menus powered by ReactDropdown to switch which quantitative attributes appear on the x and y axes. The chart renders as SVG with React components, using d3.scaleLinear for axes and d3.extent to compute domains dynamically. Data loads asynchronously via d3.csv, and the interface includes labeled axes and hover tooltips showing species names.

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Polished Scatterplot with menus

This interactive scatterplot displays the Iris dataset, allowing users to select which measured attribute (sepal length, sepal width, petal length, or petal width) appears on the X and Y axes via dropdown menus. The visualization updates the linear scales, axes, and positioned circles in response to menu changes. Built with React and D3 v6, it uses `scaleLinear` for axis scaling, `csv` for data loading, and SVG for rendering. The polished styling includes custom menus from `react-dropdown` and axis formatting with `d3.format`.

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Scatterplot with menus

This interactive scatterplot displays the Iris flower dataset, using two dropdown menus to select which attributes are plotted on the x- and y-axes. Built with React and D3 v6, the visualization renders data points as SVG circles with tooltips showing exact values. The code uses D3's `scaleLinear` for axis scaling and `csv` for data loading, while React's `useState` and `useEffect` hooks manage menu selections and asynchronous data fetching.

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React starter - menu

This example demonstrates a simple pet-selection menu built with React, using a reusable Dropdown component that renders a native HTML `<select>` element. It shows how React state, managed via the `useState` hook, updates the chosen option when a user changes the selection. The `Dropdown.js` file defines the component, mapping over an array of pet options (goldfish, dog, cat, hamster, parrot, spider) and passing the selected value through props. The app is rendered into the DOM using `ReactDOM.render` from React 16.9, with inline styling adjusting the font sizes of the label and select controls.

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React starter

This example demonstrates a minimal React application that renders a single text element, "React to this," onto a web page. It shows the basic structure of a React component using JSX syntax within a functional component. The code imports the React and ReactDOM libraries and uses `ReactDOM.render` to mount the component into a DOM node with the id "root". The page is configured with a hidden overflow and uses a production build of React from a CDN, with the application bundled separately.

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World map less. 29

A world map rendered in SVG displays landmasses in teal, with interiors highlighted in cyan and graticules in gray over a light blue sphere. The visualization loads GeoJSON from the World Atlas TopoJSON dataset, converting it via `topojson.feature` and `topojson.mesh`. Using React hooks and `d3.geoPath` with a `geoNaturalEarth1` projection, the map draws countries and boundary lines through the `Marks` component, with styling applied via CSS classes.

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Temperature line chart

This line chart visualizes temperature fluctuations over time, using a dataset from the "Sense Your City" collection. The chart’s smooth, curved line connects individual data points, each marked by a small circle that reveals the exact temperature value via a tooltip on hover. Built with React and D3 v6, it employs `scaleTime` and `scaleLinear` for axes, while `line` with `curveNatural` renders the smoothed path. The SVG visualization includes custom-styled axes and labels, with interactive hover effects on the data points.

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Temperature scatterplot chart

A temperature scatterplot shows hourly temperature readings over one week, with time on the x-axis and degrees on the y-axis. Each data point is an SVG circle colored teal, with a native `<title>` tooltip displaying the exact temperature value. The chart uses React components for the axes and marks, while D3 v6 APIs (`scaleTime`, `scaleLinear`, `extent`, `csv`) handle data loading, scaling, and tick generation. The dataset comes from the Sense Your City weather CSV, and labels are formatted with `timeFormat`.

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React start practice 1

This example shows a minimal React application that renders the text “Hello jsx” on a webpage. The app uses React and ReactDOM, loaded via external CDN scripts, with a single functional component defined in index.js. ReactDOM.render targets the root div element in index.html, displaying the styled heading at a large font size. React 16.7.0 and ReactDOM 17.0.1 are the concrete libraries used, with JSX syntax in the component.

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Scatter plot iris dataset

A scatter plot displays the Iris dataset, plotting sepal length against sepal width for individual flowers. Each point is rendered as a circle using React and SVG, with axes generated from d3-scale linear scales. The code uses d3.csv for data loading, d3.extent and d3.max for domain calculations, and custom React components for axis ticks and marks. Hovering reveals a tooltip with the sepal length value via an SVG title element. The visualization uses a clean grid layout with labeled axes and a teal color for data points.

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Stylized bar chart

A stylized horizontal bar chart displays the ten most populous countries by estimated 2020 population, using a teal fill and the Poppins font. Population values are formatted in billions with a custom tick formatter, and each bar includes a native SVG tooltip. The chart is built with React and D3 v6, using `scaleBand` for the categorical y-axis, `scaleLinear` for the numeric x-axis, and SVG `rect` elements generated by the `Marks` component. Data originates from a UN population CSV loaded via D3’s `csv` method.

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Refactor bar chart

This example displays a horizontal bar chart of population data for the top 10 countries from a UN 2019 dataset, rendered as SVG elements within a React component. The code uses d3-scale's `scaleBand` and `scaleLinear` to map country names and population values to screen coordinates, while `AxisBottom` and `AxisLeft` generate the tick marks and labels using D3's tick system. Bars are drawn as `rect` elements via the `Marks` component, with width proportional to each country's population. Data is fetched asynchronously using the `csv` function from D3 v6.

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Axes with React & D3

This example shows a horizontal bar chart of the top 10 most populous countries in 2020, using data from a CSV file loaded via the `csv` function from D3. The React `useState` and `useEffect` hooks fetch the data and manage the chart state. Axes are drawn manually with SVG elements, using `scaleBand` for the y-axis countries and `scaleLinear` for the x-axis population values, with tick marks generated from `xScale.ticks()`. Bars are rendered as SVG rectangles with widths mapped to population counts.

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Margin convention

This example demonstrates a bar chart of the top 10 most populous countries in 2020, using a margin convention to offset the chart area within the SVG canvas. The visualization is built with React and D3 v6, where D3 utilities load the UN population data from a CSV and compute the band scale and linear scale for the axes. The SVG rendering positions the grouped `<rect>` elements inside a translated `<g>` element, with margins applied to keep the chart clear of the canvas edges.

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Pathetic bar chart fork

This example renders a horizontal bar chart of the top 10 most populated countries for 2020, using data loaded from a CSV file via D3’s `csv()` utility. React state and the `useEffect` hook manage the asynchronous data fetch, while D3’s `scaleBand` and `scaleLinear` define the y- and x-axes. Each country is represented as an SVG `<rect>` element, with its width proportional to population, and the chart updates reactively once the data arrives.

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Display css colors with react using D3 pie

A circular pie chart displays every CSS color name as a slice, with each slice filled by its corresponding hexadecimal RGB value. The visualization loads color data from a CSV file using D3’s `csv` utility and React’s `useEffect` hook. D3’s `pie` and `arc` generators compute the slice angles and SVG path geometry, while React renders each `<path>` element within an SVG canvas. The chart uses an inner radius of zero and an outer radius equal to the viewport width, producing a full disk of color categories.

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Display css colors with react

This example displays the full CSS color palette as a circular pie chart, with each color occupying an equal slice. It clearly shows the distribution and visual relationships among all named CSS colors. The code loads color data from a GitHub-hosted CSV file using `d3.csv`, then computes each slice’s angles with a `d3.arc` generator. React handles rendering by mapping over the data to create SVG `<path>` elements, with the fill set to each color’s hex value. The visualization updates once the asynchronous data fetch completes.

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test Vega-Lite API 2

This example displays a bar chart of satellite launch purposes by country, with bar heights encoded via a symmetric log scale and bars colored by purpose, using a dataset loaded from a CSV file. It is built with the Vega-Lite API, which registers Vega and Vega-Lite libraries, and renders the visualization as SVG using `vl.markBar`, `vl.x`, `vl.y`, and `vl.color` encodings. The chart fills the browser window, and a Vega Tooltip handler is initialized to show value details on hover.

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Fork of Vega-Lite API Template

This visualization explores the relationship between a car’s country of origin, its horsepower, weight, and miles per gallon, using a bubble chart built with the vega-lite-api. Each circle’s x-axis position encodes origin, y-axis horsepower, color encodes weight, and size encodes mpg, with tooltips revealing the car’s name. The code loads auto-mpg data via d3.csv, applies custom axis and label styling, and renders the SVG using vega-lite, vega, and a vega-tooltip handler.

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Load data with D3 & React

This example demonstrates the composition of a data-loading pipeline using D3 and React hooks, displaying a summary of CSS color data fetched from a GitHub gist. It renders text output showing the dataset’s file size in kilobytes, row count, and column count. The code uses `useState` and `useEffect` to asynchronously load data via `d3.csv`, then formats the result with `d3.csvFormat`. React 16.9 manages the component lifecycle, while D3 v6 handles CSV parsing and formatting.

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Mouse follower

A smiling face follows the mouse cursor across an SVG canvas. The visualization uses React state to track pointer position via the `onMouseMove` event handler, updating a single circle's `cx` and `cy` coordinates. Rendered with React DOM and SVG elements on a 960×500 viewport, the circle moves in real time as the user moves the mouse, demonstrating reactive state-driven updates. The code uses React's `useState` and `useCallback` hooks to manage and optimize the position updates.

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loading and parsing csv data - ep 13

This example demonstrates the initial step of loading and parsing CSV data in a web page, using the browser’s built-in `fetch` API to retrieve a remote file from a GitHub gist containing CSS color names. The code logs the raw text response to the console, illustrating a nested promise chain—often called a “pyramid of doom”—where `response.text()` is called inside the first `.then()` callback. No parsing or visualization occurs yet; the focus is on the asynchronous data-fetching pattern. Styled with inline CSS, the page itself is minimal, with a large empty body.

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Smiley face with react comp part 6

A grid of 18 randomly generated smiley faces is rendered as SVG using React components. Each face reuses the same modular structure—BackgroundCircle, Eyes, Mouth, and FaceContainer—to assemble a yellow circle with eyes and an arc-based mouth, while randomized stroke widths, eye radii, mouth widths, and mouth radii create subtle variation across the grid. The faces are positioned with the d3.range function to generate the array count, and the mouth path is drawn using the d3.arc API with a half-circle angular range.

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Smiley face with react comp

A React component renders a smiley face as an SVG graphic, composed of a yellow circle with a black outline, two eyes, and an arc-shaped mouth. The face is centered on a 960 by 500 canvas using a group translation. The mouth is generated with d3.arc, which defines its inner and outer radii and angular extent. The code defines reusable React components for the background, eyes, and mouth, passing positioning and sizing values as props. The illustration uses React 17 and d3 v6, displaying the rendering approach of combining React’s component model with D3’s geometry API.

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Tweaked smiley face

This example shows a stylized smiley face composed of layered SVG shapes, including a large circular head, angled elliptical ears, gradient-filled eyes, and a toothy rectangular mouth. The face is built declaratively with React components, while D3’s `arc` generator creates the curved smile path. SVG radial gradients, transforms, and stroke styling define the visual details, and the entire scene is rendered client-side without external data.

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Smiley face pt. 1

A simple smiley face rendered as SVG using React components and D3's arc generator. The code creates a yellow circle for the head, two black circles for eyes, and a curved mouth path via the d3.arc() function with specified inner and outer radii. Coordinates are computed relative to the SVG center, with dimensions fixed at 960 by 500 pixels. React handles the component structure while D3 computes the arc path geometry.