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Activtiy6-Question3

This scatter plot visualizes product dimensions from a Superstore dataset, displaying product length on the x-axis and product width on the y-axis. Each circle represents a product, with colors distinguishing categories: skyblue for electronics, lightgreen for furniture, orange for kitchen, and gray for others. The visualization uses D3.js v7 with SVG rendering, employing `d3.csv` to load data, `scaleLinear` for axis scaling, and `axisLeft`/`axisBottom` to generate axes. Data is fetched from a raw GitHub CSV URL, with circles positioned using the `cx` and `cy` attributes and styled via a color mapping function.

Activtiy6-Question4

A bubble chart visualizes sales data for 19 employees, plotting cost of sales against profit, while bubble size encodes quantity sold and color distinguishes individual employees. The chart includes a legend for bubble size and a color legend. Built with D3.js v7, it uses `d3.scaleLinear` for axes and bubble size, `d3.interpolateTurbo` for colors, and SVG circles. Interactive tooltips display employee names on mouseover. Data is loaded from a local CSV file.

Activity6-Question4

This bubble chart shows the global distribution of language speakers, plotting native speakers on the x-axis and total speakers on the y-axis. Circle size encodes total speakers via the square root, and labels identify each language. The visualization uses d3.scaleLinear for axis scaling and d3.select to manage the SVG rendering, with data loaded from a CSV file. Circle styling and text labels are applied via CSS classes.

Activity6-Task4

This bubble chart visualizes the correlation between average horsepower, torque, and highway MPG across multiple car makes. Each circle’s position encodes horsepower (x-axis) and torque (y-axis), while its radius scales with highway MPG and its color distinguishes the manufacturer. Built with D3 v7, the SVG visualization uses `scaleLinear` for axes and bubble size, `scaleOrdinal` with `interpolateTurbo` for color mapping, and `axisBottom`/`axisLeft` for labels. Interactive tooltips appear on mouseover via D3 event handlers, showing the car make. The legend displays MPG values as circle sizes and includes a color-coded list of all makes.

Activity6-Tutorial-3.2

This visualization displays the classic Iris dataset as a scatter plot, mapping sepal length on the x-axis against petal length on the y-axis, with points colored by species via a categorical ordinal scale. The chart uses custom D3 v7 modules for axes and a shape legend, employing `scaleLinear` for coordinate scaling, `scaleOrdinal` with `symbolsFill` to assign distinct marker shapes, and a `symbol` generator for drawing them. Data is loaded asynchronously from a CSV file using `csvParse` and rendered within an SVG that resizes to the full window.

Activity6-Question1-1

This example shows a horizontal bar chart comparing the populations of ten countries in the year 2000, using data loaded from a CSV file. The visualization renders as an SVG within a React app, using D3 v5 APIs like `scaleBand` and `scaleLinear` to position bars and axes. Custom React components—`AxisBottom`, `AxisLeft`, and `Marks`—build the chart, with axis tick lines styled blue and bars filled light green. A native SVG `<title>` element provides tooltips on each bar, and population labels are formatted with `format('.2s')` to display abbreviations like "B" for billions.

Activity6-Task3

Activity-6-Question2-1

Activity6_Task2

This stacked bar chart shows the percentage distribution of five rating categories across six movie genres, revealing how genre influences audience reception. The chart uses stacked rectangles to represent proportions, with a color-coded legend and tooltips displaying precise percentages on hover. Built with D3.js v7, the visualization uses `d3.stack()` for data aggregation, `d3.scaleLinear` and `d3.scaleBand` for axes, and SVG rendering. The data comes from a CSV file with genre-based rating percentages, styled with custom CSS for interactive elements.

Activity 6_Question1-2

This bar chart visualizes the population of 20 countries in 2010, sorted by country name. Each bar’s length corresponds to population, with hover tooltips showing formatted values in billions. The visualization is built with React and D3 v5, using `scaleBand` for the y-axis, `scaleLinear` for the x-axis, and custom `AxisBottom`, `AxisLeft`, and `Marks` components to render SVG rectangles and tick marks. Data is loaded from a CSV file via `d3.csv`.