C

csabavath@wpi.edu

@Charan12-prog·32 public vizzes

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Sleep Health- Interactive Visualization Dashboard

This interactive dashboard visualizes sleep disorder prevalence across low, medium, and high stress levels, using 100% stacked bars with animated transitions. Users filter by gender and toggle between all categories or disorders only, with hover tooltips and clickable legend items that isolate healthy versus disordered sleep groups. Built with React and D3 v7 (d3.scaleBand, d3.stack), the SVG chart renders from a CSV dataset and updates bins and counts dynamically. The animation and ordinal color scale highlight how higher stress correlates with increased Insomnia and Sleep Apnea rates.

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fork of Finishing Touches

This visualization shows a 100% stacked bar chart of sleep disorder prevalence across stress levels, using data from a sleep health and lifestyle dataset. The chart displays the proportion of people with no disorder, insomnia, or sleep apnea, with a gray and two blue color scheme to distinguish healthy from disordered sleep. Users can filter by gender and view only disorder categories through dropdown menus, while hovering reveals exact percentages via a tooltip. The implementation uses D3 v7 with d3.stack, d3.scaleOrdinal, and d3.axisBottom/Left to render the SVG with interactive animation.

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project-momentum–start-polishing

This 100% stacked bar chart explores how stress levels relate to sleep disorders, allowing viewers to filter by gender and toggle categories through an interactive legend. Built with D3 v7 and React, it bins stress scores into low, medium, and high groups, displaying the proportion of people with no disorder, insomnia, or sleep apnea. Hovering reveals exact percentages and counts, while the chart uses a sequential color palette with SVG animation for smooth transitions, sourced from the Sleep Health & Lifestyle dataset.

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Fork of project-momentum-Hover

This visualization shows the percentage of people with different sleep disorders across three stress-level bins, filtered by gender via a dropdown. The chart uses stacked bar segments colored by disorder category, with a legend that supports highlighting and filtering on hover. Built with D3 v7, it uses `d3.stack`, `d3.scaleOrdinal`, and `d3.scaleBand` with transitions on rect joins to animate updates. Data comes from a CSV of sleep health records, summarized into percentages per stress bin.

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project-momentum-color

This interactive 100% stacked bar chart visualizes the distribution of sleep disorders (None, Insomnia, Sleep Apnea) across binned stress levels for a synthetic "Sleep Health & Lifestyle" dataset, with a gender filter and clickable legend. Built with D3 v7 and React, the visualization computes proportional segments per stress bin, using an ordinal color scale and animated transitions via `transition().duration()` to update bars when the gender select changes. Hovering a segment reveals a tooltip with exact percentages and category counts, while the legend filters which disorder categories are displayed.

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Fork of Week 7 – Project Momentum: Color assignment.

This visualization displays the prevalence of sleep disorders (None, Insomnia, Sleep Apnea) as a 100% stacked bar chart, segmented by binned stress levels (Low, Medium, High) and filterable by gender. A legend allows users to toggle categories on or off, with hover and click interactions updating the display. The chart uses D3 v7’s `d3.stack()`, `d3.scaleBand()`, and `d3.scaleOrdinal()` to compute and color the proportional segments, with smooth transitions applied to bar heights via `transition().duration()`. Data comes from a CSV file on sleep health and lifestyle.

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Week 7 – Project Momentum: Color assignment.

This visualization presents a 100% stacked bar chart showing the distribution of sleep disorders (None, Insomnia, Sleep Apnea) across three stress-level bins (Low, Medium, High), with a dropdown filter for gender. Built with D3 v7, it uses `d3.scaleOrdinal` for color assignment, `d3.stack` for stacking, and SVG rendering with animated transitions via `transition().duration(400)`. The chart dynamically updates its title to reflect the selected gender and sample count, drawing from the Sleep Health & Lifestyle dataset.

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Sleep vs Stress × Gender (Filters + Error Bars)

This interactive visualization displays the percentage distribution of sleep disorders—None, Insomnia, and Sleep Apnea—across three stress-level bins (Low, Medium, High). A dropdown filter restricts the visual to All, Male, or Female respondents, dynamically updating the stacked bar chart and title with the selected gender and sample size. Built with d3.v7 and React, the chart uses D3’s `scaleBand`, `stack`, and `transition` APIs to animate bar segments, with tooltips showing exact counts and percentages. Data comes from the bundled `data.csv` file.

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“Sleep disorders % by stress — 100% stacked bar”

This chart displays the percentage distribution of sleep disorders (None, Insomnia, Sleep Apnea) across low, medium, and high stress categories, with each bar representing a 100% stacked composition. The visualization uses D3 v7’s `d3.rollup` for data aggregation, `d3.stack` for layer generation, and SVG for rendering. It reads from a CSV file containing lifestyle and health metrics, binarizes stress levels into three groups, and employs an ordinal color scale with a custom tooltip for interactive detail inspection.

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sleep-vs-stress-gender-tooltips

A scatter plot displays sleep duration against stress level from a Kaggle sleep health dataset, with points colored by gender and a legend. D3.js v7 renders the SVG, applying linear scales, axes, gridlines, and per-gender dashed regression lines computed via least squares. Hovering a point triggers a custom tooltip showing person ID, gender, sleep, stress, and sleep disorder. React manages the component lifecycle, initializing the chart after asynchronously loading and parsing the CSV.

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Fork of Assignment 5 - Prototype 2

This interactive scatter plot visualizes the relationship between sleep duration and stress level, with individual data points colored by gender. The visualization, built with React and D3 v7, loads data from a Kaggle CSV file and renders an SVG chart. It includes axes with labels, a legend, and per-gender dashed trend lines computed via least-squares regression. Tooltips display detailed information about each person’s ID, gender, sleep duration, stress level, and sleep disorder. The chart employs D3’s `scaleLinear`, `scaleOrdinal`, and `axisBottom`/`axisLeft` functions for scaling and axes, with `schemeTableau10` providing the color palette.

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First Project Prototype – Sleep & Stress Bar Chart

This grouped bar chart shows how average sleep duration varies across three stress-level bins (low, medium, high) for females and males, revealing that higher stress corresponds to fewer sleep hours. Built with D3 v7 and React, the visualization loads CSV data via `d3.csv`, aggregates with `d3.rollups` and `d3.mean`, and renders SVG bars through `d3.scaleBand` and `d3.scaleLinear`. A Tableau10 color scale encodes gender, while value labels and a small legend clarify the comparison.

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First Project Prototype – Sleep & Stress Bar Chart

This scatter plot visualizes the relationship between sleep duration and stress level, with points colored by gender and dashed trend lines showing the per-group linear regression. It uses D3 v7 functions like `d3.scaleLinear`, `d3.axisBottom`, and `d3.scaleOrdinal` on an SVG canvas, loading survey data from a Kaggle CSV. A legend and hover tooltips reveal additional details, while the chart highlights how higher stress correlates with reduced sleep, particularly for females.

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Fork of “Sleep Hours vs Stress Level – Scatter Plot” with axes

This scatter plot visualizes the relationship between sleep duration (hours per night) and stress level (scale of 1–10) using data from a Kaggle sleep health and lifestyle dataset. Each circle represents an individual, colored by gender, with dashed trend lines showing the linear regression per gender. The React application renders an SVG visualization using D3 v7 APIs including `scaleLinear`, `extent`, `axisBottom`, `axisLeft`, and `scaleOrdinal` with the Tableau 10 color scheme. Tooltips display detailed information on hover, while axis labels and a legend clarify the variables and categories.

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Fork of “Sleep Hours vs Stress Level – Scatter Plot”

This scatter plot visualizes the relationship between sleep duration and stress levels from a Kaggle dataset, with points colored by gender. Built with React and rendered as an SVG, the visualization uses D3 v7 APIs including `scaleLinear`, `scaleOrdinal`, `schemeTableau10`, and `axisBottom`/`axisLeft` for axes, plus a legend and tooltips. The chart includes bold labels, a title, and interactive hover details showing person ID and sleep disorder.

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“Sleep Hours vs Stress Level – Scatter Plot”

This scatter plot visualizes the relationship between sleep duration and stress levels from the Sleep Health and Lifestyle dataset. Each circle represents an individual, positioned by their nightly sleep hours and stress rating, with color encoding gender and a legend explaining the categories. Built with React, the D3.js code uses `scaleLinear` and `scaleOrdinal` for positioning and color, while `axisBottom` and `axisLeft` create the axes. Tooltips reveal additional details per point, and the plot is rendered as an SVG with bold labels for readability.

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Sleep Health and Lifestyle Dataset

This visualization displays a raw preview of the Sleep Health and Lifestyle Dataset, showing all 400 records with their 13 attributes including sleep duration, stress levels, and sleep disorder classifications. The example uses d3.csvParse to load and parse the CSV data, converting numeric fields to numbers before displaying the JSON-formatted data in a pre element. The code demonstrates a state management pattern where data transitions from undefined to 'LOADING' to the final parsed array, with each state change triggering a re-render of the visualization.

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restaurant-orders-data

This visualization displays raw restaurant order data from a Kaggle dataset, presenting each record as a JSON object. The example loads the CSV file asynchronously and parses it using D3's `csvParse` function, mapping string attributes like "Order ID" and "Payment Method" to cleaner property names while converting numeric fields such as "Quantity" and "Price" to numbers. The `viz.js` module uses D3's `select` and `join` operations to render the parsed data as a formatted `<pre>` element, with the initial state showing "LOADING" until the fetch completes. The interaction pattern relies on a simple state management system where `setState` triggers a re-render of the visualization.

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covid19-data

This example displays the raw COVID-19 dataset as a formatted JSON preview, loading the CSV file via `fetch` and parsing it with `d3.csvParse`. The visualization shows the first rows of data, including date, region, confirmed cases, deaths, and recoveries, placing each field on a separate line for inspection. The code uses `d3.select` to manage a container element and `d3.selectAll` with a `join` operation to update the displayed content whenever the state changes, initially setting the data to a `'LOADING'` placeholder before fetching the file.

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Housing Prices Dataset

This example loads the Housing Prices Dataset from a CSV file and displays the raw data as JSON in a pre element. The code uses D3 v7 functions such as `d3.select`, `d3.csvParse`, and the `join` operation to manage the data loading and DOM rendering. It demonstrates a straightforward approach to fetching, parsing, and visualizing tabular data, with the fetched CSV transformed into an array of objects for display.

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Netflix Movies & TV Shows

This visualization displays the raw contents of the Netflix Movies & TV Shows dataset as a JSON-formatted text dump. It initially shows a loading message before fetching the CSV file and parsing it with D3’s `csvParse` method, then renders the resulting array of records as pretty-printed JSON using D3’s `select` and `join` APIs. The code does not yet produce a graphical chart, only a textual representation of the data’s attributes like title, type, and release year.

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Netflix Movies & TV Shows

This example renders a raw, tabular view of global happiness rankings by loading a CSV dataset and parsing it with `d3.csvParse`. The visualization initially shows a loading state, then fetches the CSV file and displays the raw parsed data as JSON, including calculated fields like rank, country, score, GDP per capita, and social support. Using D3’s `select` and `.text`, it binds the data to a `<pre>` element, demonstrating a minimal data-loading and inspection workflow without any graphical chart or complex rendering.

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world-happiness-data

This gallery example displays the World Happiness Report 2019 data, presenting each country’s happiness score alongside contributing factors such as GDP per capita, social support, and healthy life expectancy. The visualization loads the data from a CSV file and displays the raw parsed JSON, demonstrating the initial data-loading phase. It uses d3.v7’s `select`, `csvParse`, and `fetch` API to retrieve and parse the dataset, updating the view through a state management system.

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world-happiness-data

This example visualizes the World Happiness Report data, showing how each country’s overall happiness score and rank relate to factors like GDP per capita, social support, and healthy life expectancy. The code loads a CSV file using D3’s `csvParse` (though the import initially references `tsvParse`) and displays the parsed data as a raw JSON object inside a `<pre>` element. It uses D3 v7’s `select` and `join` to manage the DOM, with a `setState` pattern to handle asynchronous data loading. The visualization currently only logs the first five rows to the console and prints the full dataset, not yet rendering any charts.

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world-happiness-data

This example demonstrates the process of loading and parsing a CSV dataset containing measurements of iris flowers. The visualization initially shows a JSON representation of the parsed data, displaying all 150 records with their sepal and petal dimensions. The code uses D3's `csvParse` function to convert the raw CSV text into structured data, then converts the numeric string values to numbers. The `select` function and `.join('pre')` method render the data as text in the container, while the `fetch` API retrieves the data.csv file from the server.

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world-happiness-data

This example loads the Iris dataset from a CSV file and displays it as raw JSON text in a `<pre>` element. The visualization uses d3.csvParse to convert the CSV string into an array of objects, then coerces numeric columns to numbers. It demonstrates a state management pattern with d3-selection’s select and join methods, where data is asynchronously fetched and rendered after a loading state. The code shows only text output, not a graphical chart.

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My Fork of Circles with D3

This example generates an animated spiral galaxy of 420 circles in SVG using D3 v7. Each circle is positioned at a radius proportional to the square root of its index and an angle based on the golden angle, with colors assigned via `d3.interpolateRainbow`. The circles transition from radius zero to their final size with staggered delays, followed by an infinite sinusoidal pulse animation using `transition.attr`. The visualization is centered in a fixed container with a light green background, rendering purely through D3’s data join and transition APIs in an ES module.

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Creative Circles by Charan

Four hundred twenty rainbow-colored circles spiral outward from the center of an SVG canvas, positioned using the golden angle and square-root scaling to form a galaxy-like pattern. Each circle expands from a radius of zero in a staggered transition, then gently pulses in size through a recursive `pulse()` function using `d3.easeSinInOut`. The visualization uses D3 v7’s selection, data join, and transition APIs, with colors generated via `d3.interpolateRainbow` on a light green background.

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Creative Circles by Charan

Four hundred twenty rainbow-colored circles spiral outward from the center of an SVG canvas, positioned using the golden angle and square-root scaling to form a galaxy-like pattern. Each circle expands from a radius of zero in a staggered transition, then gently pulses in size through a recursive `pulse()` function using `d3.easeSinInOut`. The visualization uses D3 v7’s selection, data join, and transition APIs, with colors generated via `d3.interpolateRainbow` on a light green background.

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Creative Circles by Charan

This visualization displays a spiral galaxy of 420 circles that bloom outward from the center, colored with the rainbow spectrum. The circles are positioned using the golden angle and square-root scaling, creating a phyllotaxis-like pattern. Built with D3 v7 and SVG, each circle transitions from radius 0 to its full size with staggered delays, then continuously pulses via chained `transition()` calls with `easeSinInOut`, demonstrating D3's animation capabilities with `interpolateRainbow` for color mapping.

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Fork of Creative Cricles by Charan

This fork of Creative Circles generates an animated, interactive network of colored nodes and connecting lines. The visualization uses `d3.forceSimulation` with charge and link forces to position the elements, while `d3.forceCenter` keeps the cluster centered. Each node is rendered as a circle whose radius and color are derived from the simulation’s data, and the links are drawn as simple lines. The animation runs continuously as the forces settle, creating an organic, ever-shifting circular composition. The code relies entirely on D3 v7’s modular API within a single container div.

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Creative Cricles by Charan

This example shows a static composition of seven semi-transparent circles scattered across a light green background, each with a distinct neon color and varying radius. The visualization uses D3 v7 to bind an array of circle data to SVG elements, setting their position, size, and fill via the `attr` method. The code leverages the `selectAll` and `join` pattern to create the circles within the SVG container, which is sized to the full viewport. A fixed opacity of 0.7 gives the circles a subtle layered appearance.