jyothirmai318@gmail.com

Project_3

This dashboard visualizes COVID-19 metrics, displaying time-series line charts for new cases, new deaths, and population density. Users select multiple countries and specify a date range to compare trends across nations. The interface uses D3.js v6 to parse and filter the data from a CSV file, with `d3.scaleTime` and `d3.scaleLinear` mapping dates and values. The line charts are rendered as SVG paths, with axes and labels generated using `d3.axisBottom` and `d3.axisLeft`.

Project_2

A choropleth-free, year-wise grouped bar chart visualizes COVID-19 metrics—total cases, deaths per million, and population density—for a user-selected country. The interactive dropdown filters data loaded from `cleaned_covid_data.csv`, using `d3.csv` with time parsing. Rendering in SVG, the chart employs `d3.scaleBand` for grouped bars, `d3.axisBottom` and `d3.axisLeft` for axes, and tooltips that display values on hover. Population density, often dwarfing case/death counts, shares the y-axis, revealing scale discrepancies across years.

Project_1

Task9_Q2

Task9_Tutorial

This interactive scatter plot animates the Iris dataset, cycling through four different feature combinations on the x-axis (petal width, sepal width, petal length, sepal length) while keeping petal width on the y-axis. It uses D3.js (v6.7.0) with the `csv`, `scaleLinear`, `axisLeft`, and `axisBottom` APIs to render SVG circles colored by species. The animation, driven by `setInterval`, updates the plot and redraws axis labels, legends, and a title. The visualization is rendered as an SVG with custom styling for species-based colors and legend markers.

Task9_Q1

This pie chart displays the sales distribution for ten product types, ranging from Plushies with 5000 units to Watches with only 30 units. The chart is rendered as an SVG using the D3.js v4 library, with each slice colored via a custom ordinal palette. The code defines a fixed dataset inline, uses `d3.pie()` to compute the arcs, and `d3.arc()` to draw them. A clickable legend is positioned to the right, although the click handler does not implement filtering logic.

Activity8_Q4

This bubble chart maps GDP per capita on the x-axis and life expectancy on the y-axis, with circle size encoding population. Using D3 v4’s `d3.csv`, `d3.scaleLinear`, `d3.axisBottom`, and `d3.axisLeft`, it scales population from 200 to 2.11 billion to radii between 1 and 40 pixels. Circles are rendered via SVG with `stroke` and opacity styling, reading data from a gist-hosted CSV. The chart visually explores relationships between economic output, health outcomes, and country population.

Task8_Task4

This animated bubble chart explores the correlation between average horsepower, torque, and highway MPG across numerous car makes. Each circle’s horizontal position maps to horsepower, vertical position to torque, and its radius encodes highway MPG, with color distinguishing manufacturers. Built with D3 v7, the SVG visualization includes interactive tooltips that appear on mouseover using event-driven handlers, plus a legend for MPG scaling. Data is loaded from a CSV file, and the chart uses d3.scaleLinear and d3.scaleOrdinal for axis and color mapping.

Activity8_Q3

This scatter plot visualizes monthly sales figures, showing a notable sales spike in October and December. Built with D3 v7, the SVG chart uses `d3.scaleLinear` for both the x and y axes, with `d3.axisBottom` and `d3.axisLeft` generating the tick marks. Each data point is rendered as a circle whose fill color alternates between black and red based on its index, with points positioned via the scales' `cx` and `cy` attributes. The data comes from a CSV file containing month names and sales values in USD.

Task8_Task2Q1

A grouped, stacked bar chart displays the population distribution across four age brackets for six US states, using data from a local CSV file. The visualization encodes each state as a cluster of colored bars, with segments representing age ranges from "Under 5 Years" to "18 to 24 Years." The chart employs D3's `scaleBand` for categorical axes and `scaleLinear` for the population count axis, with an ordinal color scale (`scaleOrdinal`) distinguishing categories. Rendering as an SVG, the code leverages D3 v5's functional API, including axis generators, data binding via `.enter()`, and a custom legend, while relying on jQuery helpers for array operations.

Fork of Task8_Task3.2

This example shows a scatter plot of Iris flower measurements, mapping sepal length on the x-axis and petal length on the y-axis, with species distinguished by different symbol shapes. The visualization includes a custom shape legend positioned within the plot. It uses D3 v7’s `scaleLinear` for axes, `scaleOrdinal` with `symbolsFill` for shape encoding, and the `symbol` generator to render marks. Data loads asynchronously from a CSV file via `fetch` and `csvParse`, with axes and legend constructed through reusable functions that accept configurations.

Task8_Task3

This scatter plot visualizes the Iris dataset, showing petal length against petal width for three species, each represented by a distinct color (skyblue for setosa, lightgreen for versicolor, orange for virginica). Built with D3 v6, the visualization uses `d3.select`, `d3.csv`, `d3.scaleLinear`, `d3.extent`, `d3.axisLeft`, and `d3.axisBottom` to load data from a remote CSV, map the quantitative fields, and render SVG circles with fixed radius. The chart includes rotated and bottom axis labels, with styling applied through CSS. The code reads petal and sepal measurements directly from the parsed rows, though only the petal dimensions are plotted.

Task8_Task(2)

A stacked horizontal bar chart displays the distribution of IMDb movie ratings across six genres, broken into five rating brackets from below 5.0 to above 8.5. Each bar is segmented proportionally, with colors assigned per bracket via `d3.scaleOrdinal`, and hover interactions reveal exact percentages using a custom tooltip. The chart is rendered as SVG, using `d3.stack`, `d3.scaleLinear`, and `d3.scaleBand` for layout, with axes and labels styled via CSS. The data comes from a CSV file, processed directly in D3 v7 within the VizHub environment.

Fork of Task8_Tutorial(1.2)

Task8_Tutorial(1.1)

This example presents a horizontal bar chart ranking the population of ten countries in the year 2000, sourced from a CSV file and rendered with React and SVG. The visualization uses D3 v5 scales and axes, with `scaleBand` for the categorical country labels and `scaleLinear` for the population values. The code defines custom `AxisBottom`, `AxisLeft`, and `Marks` components to draw the bars and tick marks, and formats tick values using D3’s `format`. Data is fetched with `d3.csv` and loaded asynchronously through a custom `useData` hook, displaying a loading state until ready.

Task8_Task(1)

This horizontal bar chart displays 2020 population estimates for 30 countries, sorted alphabetically by country name with bars extending left-to-right proportional to population values. The visualization uses React for component structure and D3 v5 for scales, axes, and data parsing, with SVG rectangles representing each country’s population in millions. Data loads from a GitHub CSV file, formatting large tick labels with D3’s `format('.2s')` to abbreviate billions as “B”. Tooltips show precise population values via SVG `<title>` elements, while blue axis lines and light-green bars provide visual clarity.

5 Buddhas

This visualization presents "5 Buddhas," a vibrant and overlapping composition built with React and rendered as SVG. The artwork layers five distinct Buddha silhouettes, each constructed from a dense arrangement of semitransparent, randomly colored polygons that create a mosaic-like texture. The polygons are generated procedurally using nested array maps, with their positions, colors, and rotations determined by pseudo-random calculations based on array indices. The background is black, and each Buddha shape is rendered with rotated, semi-opaque polygons in bright, varied hues, producing a glowing, kaleidoscopic effect. The piece merges computational randomness with the iconographic stability of the Buddha form, creating a hypnotic, meditative visual. The code demonstrates a creative use of React and SVG for generative art, where the deterministic random seed ensures the intricate, layered patterns remain consistent across renders.