K

Karnam Abinay Goud

@abinaykarnam9·22 public vizzes

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question 2

This example demonstrates how to create a stacked area chart using D3.js, visualizing fruit quantities across multiple years. The chart renders three overlapping areas representing apples, oranges, and bananas, with color-coded fills from the `d3.schemeCategory10` palette. The code imports data from a remote CSV file, uses `d3.stack()` to compute stacked series, and applies `d3.area()` with `d3.scaleBand()` and `d3.scaleLinear()` to generate the SVG paths. Axes and labels are added using `d3.axisBottom()` and `d3.axisLeft()`, with hover-free static rendering.

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Question 1

This pie chart visualizes the top 10 countries by deaths from air pollution in 2020. It uses D3 v5 to fetch CSV data from a GitHub repository, process it with d3.csv, and render the chart as SVG paths generated by d3.pie and d3.arc. Each slice displays the death count in thousands using d3.format, while a corresponding legend with colored rectangles identifies each country. The chart employs an ordinal color scale with ten distinct colors.

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Activity 7

This interactive scatter plot explores the Iris dataset, cycling through four different variable pairings on the x-axis to reveal relationships between sepal and petal measurements. The visualization uses D3 (v6.7.0) with an animated `setInterval` to rotate the x-axis feature, while the y-axis remains sepal length. Each point is colored by species—setosa, versicolor, and virginica—using a custom marker function. The plot is rendered as an SVG with D3's `scaleLinear`, `axisLeft`, and `axisBottom` APIs, and includes a manual legend and axis labels.

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Fork of Activity 6_Q9

A bubble chart compares beverages by glucose levels on the x-axis, ratings on the y-axis, and calorie content through bubble size. The code loads CSV data with `d3.csv`, creates linear scales for axes and bubble radius, and maps beverage types to colors using `d3.scaleOrdinal` with `d3.schemeSet1`. SVG circles are positioned with x and y scales, with legends for size and color rendered alongside the chart.

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Fork of Activity6_Q9

This bubble chart compares beverages by glucose levels on the x-axis, ratings on the y-axis, and calorie content encoded as bubble radius. It uses d3.csv to load the data and d3.scaleLinear for axes and bubble sizing, with d3.scaleOrdinal and d3.schemeSet1 mapping beverage types to colors. The SVG rendering includes axis labels, a graph title, and separate legends for calorie sizes and beverage colors. Circles are drawn with opacity and red strokes for visibility.

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Fork of Activity 6_Task 4

This bubble chart visualizes the correlation between average horsepower and torque for various car makes, with bubble size representing highway MPG and color distinguishing makes. The D3 v7 code loads data from a CSV, uses `d3.scaleLinear` for axes and bubble sizing, and `d3.scaleOrdinal` with `d3.interpolateTurbo` for color mapping. Interactive tooltips appear on mouseover, and an SVG legend explains bubble size and color categories.

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Fork of Activity 6_Q6

This scatter plot visualizes candy data from a GitHub-hosted CSV file, plotting sugar percentage against win percentage. Circles are colored red or blue based on whether the candy contains chocolate. The visualization uses D3 v6 APIs including `scaleLinear`, `extent`, `axisLeft`, `axisBottom`, and `join` to render the SVG, with axis labels styled via CSS. The plot dynamically fits the browser window dimensions.

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Fork of Activity 6_task 3.1

This scatter plot visualizes the Iris dataset, mapping petal length and petal width for individual flowers while coloring each point by species (skyblue, lightgreen, or orange). It uses D3 v6 APIs including `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` to render an SVG with circle marks and labeled axes. The data is loaded from a remote CSV via `d3.csv`, and axis labels are rotated using SVG transforms.

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Fork of Activity 6_task 3.2

This scatter plot visualizes the Iris dataset, showing sepal length against petal length with points colored by species. The chart uses D3 v7’s `scaleLinear` for axes, `scaleOrdinal` with `symbolsFill` to assign distinct shapes to each species, and a custom legend to map shapes to species names. Data is loaded from a remote CSV via `fetch` and parsed with `d3.csvParse`, with numeric fields coerced to numbers. The visualization updates reactively through a simple state management pattern, re-rendering the SVG on data arrival.

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Fork of Activity 6_task 3

This example visualizes the Iris dataset as a scatter plot, mapping petal length and width as the primary x and y axes while also encoding sepal dimensions as circle radius properties. The plot uses colored circles—sky blue for setosa, light green for versicolor, and orange for virginica—to distinguish species. Built with D3 v6, the code loads CSV data from a GitHub repository, applies linear scales via `scaleLinear` and `extent`, and renders SVG circles with appended axis labels and tick marks. The visualization clearly shows clustering patterns among the three Iris species based on their morphological measurements.

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Fork of Activity 6_Q3

A stacked horizontal bar chart shows sales by product category for five stores, with tooltips displaying precise values on hover. The visualization uses D3 v7, loading data from a CSV file and rendering with SVG. It employs `d3.stack()` for data stacking, `d3.scaleBand()` for the y-axis, and `d3.scaleLinear()` for the x-axis, with `d3.scaleOrdinal()` for category colors.

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Fork of Activity 6_task 2

A stacked horizontal bar chart displays the distribution of IMDb movie ratings across six genres, using percentages from the bundled CSV data. The chart uses D3 v7's `d3.stack`, `d3.scaleBand`, and `d3.scaleOrdinal` to render five rating categories per genre with a custom color scale. Hovering over a bar segment triggers a tooltip via mouse events, showing the exact percentage for that rating segment. Axes are formatted with `d3.axisBottom` and `d3.axisLeft`, while styled tick text and labels come from external CSS.

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Fork of Activity 6_Q1_2

This visualization shows a horizontal bar chart comparing the 2010 population of twenty countries, sourced from a CSV file hosted on GitHub. It uses React with D3 v5’s `scaleBand` and `scaleLinear` to map country names and population values, rendering marks as SVG rectangles. Axes are customized via reusable components, and `d3.format` converts tick labels from billions to abbreviate them.

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Fork of Activity 6_task1

This bar chart visualizes the top 30 countries by population in 2020, sourced from a CSV file. Using D3 v5 within a React framework, the code employs `scaleBand` and `scaleLinear` for axis mappings and renders bars as SVG rectangles. The chart includes custom tick formatting via `format('.2s')`, converting values to billions with a 'B' suffix, and a tooltip showing the formatted population value on hover.

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Fork of Activity 6_Q1

This example displays a horizontal bar chart of the top ten countries by population in the year 2000, with bars sized proportionally to population values. It uses React components to render SVG elements, with D3 v5’s `scaleBand` and `scaleLinear` for positioning. The `csv` loader fetches data from a GitHub-hosted CSV file, and `format('.2s')` converts population numbers into abbreviated labels like “1.2B”. Native SVG `<title>` elements provide hover tooltips, while axis ticks and labels are generated through custom React components.

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Activity 6

A horizontal bar chart displays the populations of ten countries for the year 2000, with each bar's length proportional to its population value. The chart uses a green fill for the bars and blue axis tick marks. Built with React and D3 version 5, the visualization uses `scaleBand` for the categorical y-axis, `scaleLinear` for the quantitative x-axis, and renders as SVG. Population data is loaded from a remote CSV file using `d3.csv`, and D3's `format` function converts large numbers to a compact "B" (billion) notation for axis labels and tooltips.

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Fork of Fork of Activity-6

This horizontal bar chart displays the 2020 population estimates for 30 countries, with each bar's length proportional to its population value. Built with React and D3.js, the visualization uses reusable components for axes and marks. The `AxisBottom` component generates tick marks with formatted labels, while `AxisLeft` renders categorical labels. The `Marks` component maps each data row to a rectangle positioned by the categorical y-scale and sized by the linear x-scale, with tooltips showing the exact population via SVG `<title>` elements. Data is loaded asynchronously using D3's `csv` parser from a GitHub-hosted CSV file.

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Fork of Activity-6

This fork of Activity-6 presents a horizontal bar chart that ranks the top 30 countries by their 2020 population, using data from a CSV file hosted on GitHub. The bars are drawn as SVG rectangles, with their widths scaled by population values and their vertical positions determined by a categorical y-scale. The visualization uses D3 v5 for data loading (via `d3.csv`) and scale construction, while React manages the component state and rendering. Axis components render tick marks and labels, and hovering over a bar reveals its exact population value through a native SVG `<title>` tooltip.

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Fork of Activity 6_Task 1

This example demonstrates a minimal D3.js visualization that renders a text message centered on the page. The code imports the `select` function from D3 and a local ES6 module exporting a string, then uses `select('#message').text(message)` to display the text. It loads D3 v5 from a CDN via UNPKG and includes a CSS file for styling. A regular expression and numeric variable are declared but unused, showing basic JavaScript syntax alongside the core D3 selection and manipulation approach.

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Activity-6

This example demonstrates a minimal D3.js setup within VizHub, rendering the text "Hello VizHub!" in a centered, oversized style. It shows how to load D3 v5 via UNPKG and use ES6 module imports, both for the `select` function from D3 and a custom local module exporting the message string. The code also includes a declared constant and a regular expression, though they are unused in the visualization. The final output is a single styled text element in the DOM, driven by the imported message.

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Fork of Buddha in colors - 6 reflections

A colorful, flower-patterned background hosts a series of overlapping, hand-coded Buddha silhouettes, each rendered in a distinct solid color and arranged with subtle rotations to create a kaleidoscopic, mirrored effect. The React-based SVG visualization maps a single complex path through multiple `<g>` transform groups, scaling and translating it across the canvas while the background image is set directly via CSS `backgroundImage` on the `<svg>` element. The code uses React's `ReactDOM.render` to mount the component, with the path data embedded inline in the JSX.

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Buddha in colors -overlapping Buddha

A dense field of randomly generated, overlapping diamond polygons fills a black SVG canvas, each shape colored with semi-transparent RGB values and rotated by 45 degrees. A large, intricate Buddha silhouette rendered as a single SVG path is overlaid on top of this chaotic backdrop. The visualization uses React to render the SVG elements, with the polygon positions and colors generated via `Math.random()` within nested array maps, while the Buddha path is drawn using SVG `<path>` geometry.