Rakesh Yadav

Fork of Q 2 task 9 & 10

This area chart visualizes scores associated with different names from a CSV file loaded via d3.csv. The chart uses an SVG rendering with d3.area and a monotone curve to create the filled path, while scaleBand and scaleLinear map the data to x and y axes. The code includes rotated axis labels, steelblue styling, and gridlines generated with axisBottom and axisLeft.

task 9 pie chart Q1

This pie chart displays the percentage of climate-related spending allocated across different government departments. The visualization uses D3.js v7 to load CSV data from a GitHub gist, then encodes the spending values with a categorical color scheme and labels each slice with its department name and percentage. The chart includes a legend on the right side of the SVG canvas, and it uses d3.pie() for slice calculations, d3.arc() for constructing the pie geometry, and d3.scaleOrdinal for coloring.

1 q -pie chart task 9

Fork of task 4 Bubble chart

Activity 6 task2 Q 2.2

Fork of Fork oftutorial 3.2,question

This example shows a scatter plot of age versus BMI from an insurance dataset, with the smoker status of each individual encoded by the shape of the marker. The visualization uses d3.scaleOrdinal with d3.symbolsFill to assign distinct symbol types, and d3.axisLeft with d3.axisBottom to create the axes. A custom legend, built with d3.symbol, displays the smoker-status shapes alongside their labels. Data is loaded asynchronously from a CSV file using d3.csvParse, and the chart updates once the dataset arrives.

Fork oftutorial 3.2, Scatter Plot with Shape

This example visualizes the Iris dataset as a scatter plot of sepal length against petal length, with each of the three species distinguished by a different symbol shape. The chart includes labeled axes and a separate legend mapping species names to their corresponding shapes. The visualization is built with D3 v7, using scaleLinear for axes, scaleOrdinal with symbolsFill for shapes, and d3.symbol to generate the marked points. Data is loaded asynchronously from a CSV file and parsed with d3.csvParse.

Fork of Task -3 question6

buble chart Q4

This bubble chart visualizes the relationship between stress level, average heart rate, and vessel count, with color indicating the presence of heart disease. It uses a D3.js v7 SVG rendering, with a linear scale for both axes and a separate scale for bubble size. The chart includes interactive tooltips on mouseover that display the heart disease status and vessel count, alongside a legend for the color categories. Data is parsed from a CSV file, with stress levels converted from text to a numerical scale.

task 3 question 6

activity 6 2.1

This example demonstrates how to display a message using D3.js version 5. The code imports the `select` function from D3 and a local ES6 module, then sets the text of a `<div>` element to "Hello VizHub!". It also includes a CSS stylesheet that styles the message with a large font size. The page loads D3 from a CDN via UNPKG and uses the `import` syntax to organize dependencies. Data from a CSV file is not visualized in this example, focusing instead on basic DOM manipulation and module loading.

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This visualization presents the classic Iris dataset as a scatter plot, mapping petal length on the x-axis and petal width on the y-axis, with points colored by species (setosa, versicolor, and virginica) in skyblue, lightgreen, and orange. The code also reads sepal measurements into the data marks, though these are not directly used for positioning. Built with D3.js v6, it loads the CSV data via `d3.csv`, scales the axes with `scaleLinear`, and renders circles using SVG attributes, with `axisLeft` and `axisBottom` for labeled axes.

activity 6 question 2

activity 6 q 2

This visualization shows daily temperature variations in Delhi using a stacked bar chart, breaking down mean, minimum, and maximum temperatures across the dataset. The chart uses color-coded segments to represent the three temperature metrics, with the primary data source being the DailyDelhiClimateTest.csv file. Built with D3 v6, the code employs `d3.stack()` to compute the stacked series, `d3.scaleBand()` for the time-based x-axis, and `d3.scaleLinear()` for the y-axis, rendering the final output as SVG elements with semi-transparent bars.

Activity 6 task2

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activity 6_Task 1

5 budha

The visualization depicts an abstract, algorithmic composition of randomly positioned, semi-transparent diamond shapes layered across a black canvas. Rendered as an SVG via React, it uses nested loops over an array of numbers to generate overlapping polygons, each with randomized coordinates, colors from a broad RGB spectrum, and a 45-degree rotation. A large orange circular emblem dominates the center, providing a stark, high-contrast focal point against the dark background. The overall effect is a chaotic yet structured generative art piece, where mathematical repetition and stochastic variation combine to create a visually dense, almost psychedelic mosaic.

5 budha

This visualization, titled "5 budha," renders a grid of overlapping, rotated diamond shapes in vibrant, randomly generated colors on a black background. Built with React and rendered as SVG, the design uses nested map functions to generate a dense pattern of polygons, each with randomized positions and hues. The overall effect is a colorful, chaotic mosaic that evokes a sense of digital psychedelia. The piece includes a small, detailed path element depicting a buddha figure, contrasting with the abstract geometric field. The code is available under the MIT license and was authored by Rakesh-aviraboina.