S

shivakumarganji@my.unt.edu

@ShivaKGanji·20 public vizzes

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Fork of Question 1.2

This example displays a horizontal bar chart of population data for 20 countries in 2010. The visualization uses React with hooks (useState, useEffect) to load and parse CSV data via the d3.csv method, then renders SVG rectangles scaled with d3.scaleLinear and d3.scaleBand. Custom React components (AxisBottom, AxisLeft, Marks) generate the axes and bars, with D3’s format helper providing abbreviated tick labels like “1.5B” and native SVG <title> elements for tooltips.

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Question 1.2

This visualization shows a horizontal bar chart comparing the populations of 20 countries for the year 2010. Rectangular marks are drawn using React and SVG, with their widths proportional to population values from a CSV file loaded via `d3.csv`. D3's `scaleBand` and `scaleLinear` position the bars and axes, while `d3.format` with the SI prefix `'.2s'` converts large numbers to abbreviated labels like "1.2B". Custom `AxisBottom` and `AxisLeft` components render the tick marks and labels.

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This horizontal stacked bar chart shows the proportion of ratings (from "Below 4" to "Above 8") for six food categories (Fruit, Vegetables, etc.), with each bar’s segments representing the percentage of ratings in that range. The chart uses `d3.stack()` to create the stacked segments, `d3.scaleBand` for the categorical y-axis, and `d3.scaleLinear` for the x-axis. Hovering over a segment reveals a tooltip showing the exact percentage for that rating, calculated from the provided data. The visualization is rendered as SVG using D3 v7, with colors assigned via `d3.scaleOrdinal`.

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Fork of Activity6_Task2

This stacked horizontal bar chart displays the proportion of six food categories (Fruit, Vegetables, Meat, Dairy, Grains, Snacks) across rating bands from "Below 4" to "Above 8." Each category is a row of stacked colored segments, where segment length represents the percentage of ratings in that band. The visualization uses d3.stack() to compute cumulative values and d3.scaleOrdinal() for categorical colors. It renders via SVG with d3.axisBottom and d3.axisLeft, and includes interactive tooltips on mouseover showing exact percentage values. Data comes from a CSV file with pre-computed proportions.

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Fork of Question 1

This example shows a horizontal bar chart of population data for ten countries in the year 2000, with each bar’s length encoded by population value and country names on the y-axis. It uses React components to render SVG marks and axes, with d3.scaleBand for the categorical y-axis and d3.scaleLinear for the quantitative x-axis. The code fetches CSV data via d3.csv, formats tick labels with d3.format, and displays hover tooltips via SVG `<title>` elements. The visualization demonstrates a clean, reusable component-based architecture for building D3 charts within React.

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Question 1

A horizontal bar chart displays the population of ten countries in the year 2000, sorted alphabetically by country name. Built with React and D3 v5, the visualization uses `scaleBand` for the y-axis and `scaleLinear` for the x-axis, rendering bars as SVG rectangles. Data is loaded via `d3.csv` from a GitHub gist, with tick labels formatted using `d3.format`.

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Fork of Activity 6_Task 1

A horizontal bar chart displays the 2020 population of thirty countries, with bars arranged alphabetically by country name. The chart loads data from a CSV file and renders it as an SVG using React and D3 v5, employing `scaleBand` for the categorical y-axis and `scaleLinear` for the quantitative x-axis. The code defines custom axis components and a `Marks` component for the rectangles, which include tooltips formatted using D3's `format` function.

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Activity 6_Task 1

This example displays a horizontal bar chart of 2020 population data for thirty countries, sourced from a CSV file hosted on GitHub. The visualization is rendered using React and SVG, with D3 v5 utilities managing the layout. Specifically, code imports `scaleBand`, `scaleLinear`, `max`, and `format` from D3 to position bars and axes. The chart uses a linear x-scale for population values and a band scale for countries, with tick labels and axis text custom-styled. React's `useData` hook asynchronously fetches and processes the data before rendering.

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Fork of Question 4

A bubble chart compares beverage manufacturers by glucose level, rating, and calorie count. Each circle represents a product, with x-position mapping glucose, y-position mapping rating, and radius encoding calories. Color distinguishes brand names. The visualization uses d3.csv to load data, d3.scaleLinear for axes and bubble size, d3.scaleOrdinal for color, and SVG circles with tooltip mouseover interactions. A legend displays calorie size and brand colors, all rendered as static SVG.

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Question 4

This bubble chart compares beverage manufacturers based on their glucose levels, ratings, and caloric content, with each circle’s x-axis position representing glucose, y-axis representing rating, and size mapped to calories. Color encodes the brand, as identified through the `Make` column in the loaded CSV data. Built with D3.js v7, the visualization uses `d3.scaleLinear` for axes and bubble sizing, `d3.scaleOrdinal` for categorical coloring, and SVG circles with interactive tooltips that appear on mouseover. A legend for calorie sizes and color-coded brand markers are included directly in the SVG canvas.

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Fork of Activity6_Task4

This bubble chart uses D3 v7 (SVG and animation) to visualize the correlation between average horsepower, torque, and highway MPG for 32 car makes. Each circle’s position encodes horsepower and torque, while its radius scales with highway MPG, and color distinguishes car brands, as shown in the legend. The `d3.scaleLinear` and `d3.scaleOrdinal` functions map data to sizes and colors, with tooltips revealing the car make on hover.

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Activity6_Task4

This bubble chart visualizes the correlation between average horsepower, torque, and highway MPG across different car makes. Each circle's horizontal position represents horsepower, vertical position represents torque, and its size encodes highway MPG, with color distinguishing car manufacturers. The visualization uses D3.js v7 with an SVG rendering and includes interactive tooltips that appear on hover, displaying the car make. The chart also provides a legend for bubble size and color, using d3.scaleLinear for axis and bubble scaling, and d3.scaleOrdinal with the interpolateTurbo color scheme for categorical color mapping. The data is loaded from an external CSV file.

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Question 3

This scatter plot displays the Iris dataset, plotting weight against height for individual data points. The visualization uses colored circles, with categories A, B, and other points rendered in skyblue, lightgreen, and orange respectively. The chart is built with D3 v7, using `scaleLinear`, `axisLeft`, and `axisBottom` for axes, while data is loaded via `csv` from a remote GitHub source. Axis labels for Weight and Height are styled with green Times New Roman font.

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Question 3

This scatter plot displays the Iris dataset, plotting weight against height for individual data points. The visualization uses colored circles, with categories A, B, and other points rendered in skyblue, lightgreen, and orange respectively. The chart is built with D3 v7, using `scaleLinear`, `axisLeft`, and `axisBottom` for axes, while data is loaded via `csv` from a remote GitHub source. Axis labels for Weight and Height are styled with green Times New Roman font.

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Fork of Task-3.2

A scatter plot visualizes the Iris dataset, plotting sepal length against petal length and encoding the three flower species with distinct symbol shapes. The chart includes labeled axes and a shape legend that identifies each species. Built with D3 v7, it uses `scaleLinear`, `scaleOrdinal`, and `symbol` generators to position and render marks, with data fetched from a remote CSV and parsed via `csvParse`. Axes and legends are composed through reusable D3 modules.

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Task-3.2

This scatter plot displays the relationship between sepal length and petal length for the Iris dataset, with species differentiated by shape. The visualization uses D3 v7's `scaleLinear`, `scaleOrdinal`, `symbolsFill`, and `symbol` APIs to map data to positions and shapes, while `axisLeft` and `axisBottom` render the axes. A custom `shapeLegend` built with D3's `symbol` generator and tick marks shows the species mapping. Data is loaded via `csvParse` from a remote GitHub-hosted CSV file, and the chart fills the browser window using `window.innerWidth` and `window.innerHeight`.

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This scatter plot visualizes the Iris dataset, mapping petal length and width for individual data points, with color coding by species (setosa, versicolor, virginica). The visualization uses D3.js v6 APIs including `csv`, `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` to load and parse the CSV data from a GitHub gist, then render SVG circles. Axes are labeled for petal length and sepal length, though the data mapping uses petal dimensions for positioning. The code also references additional properties like sepal measurements, which are not directly used in the final point placement.

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Fork of Activity6_Task3

This scatter plot visualizes the Iris dataset, mapping petal length and width for each sample while using color to distinguish the three iris species. The D3.js v6 code loads the data from a remote CSV file, applies linear scales to position the circles, and renders the chart as an SVG. Custom axes, tick labels, and green axis labels annotate the plot, with the styling specified through inline CSS within an HTML page.

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Activity6_Task3

This example demonstrates how to assemble a minimal VizHub project using D3.js and local ES6 modules. It renders a static message, “Hello VizHub!”, into a centered HTML div using the D3 `select` API. The code imports the `select` function from the D3 library and a custom string from the `myMessage` module, illustrating both external and internal import patterns. Styling is applied via an external CSS file, and D3 is loaded from a CDN via UNPKG.

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Activity6_Task2

This example displays a stacked bar chart of movie ratings by genre, with each bar broken into colored segments for action, comedy, drama, thriller, and horror categories, based on data from `data.csv`. Using D3 v7, it applies `d3.stack()` to create the layered segments, `d3.scaleBand()` and `d3.scaleLinear()` for axes, and `d3.axisLeft()` and `d3.axisBottom()` for tick marks. Interactive tooltips appear on mouseover, showing the percentage for each segment.