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Activity7_question2

This example shows monthly sales data as a blue area chart with labeled axes for month and sales. It uses D3.js v7 with an SVG rendering approach. The code loads comma-separated values from a GitHub Gist, then applies `d3.scalePoint` for the categorical x-axis and `d3.scaleLinear` for the numeric y-axis. A `d3.area` generator produces the filled region, while `d3.axisBottom` and `d3.axisLeft` create the tick marks and labels. Axis titles are added as SVG text elements.

Activity 7_question1

This example shows a donut chart of categorical sales data, with each slice labeled by category and a legend on the left. Hovering over a slice displays a tooltip with the percentage share of the total sales. The code uses D3 v7 with `d3.pie` and `d3.arc` to generate the pie geometry, and `d3.scaleOrdinal` with `schemeCategory10` for colors. Data is loaded as CSV from a GitHub gist, and the SVG is rendered with a centered group and a 200-pixel right margin for the legend.

Activity6_question3

A scatter plot displays global cities by population on the x-axis and GDP in billion USD on the y-axis, with each city’s points colored via a categorical scale using `d3.scaleOrdinal` and a combined `schemeCategory10` and `schemeSet3` palette. The visualization loads a CSV from a GitHub raw URL using `d3.csv`, parses numeric fields, and renders circles with `d3.selectAll('circle')` and `.enter()`. Axes are generated with `d3.axisBottom` and `d3.axisLeft`, formatted using `d3.format('.2s')`, and a legend lists city names with matching color swatches. The SVG output uses D3 v6 with a light-gray background and Arial font.

Activity6_tutorial3.2

This scatter plot uses D3 v7 to visualize the Iris dataset, plotting sepal length against petal length. Each flower is rendered as a shape-coded path marker, with the species determining the symbol type via a d3.symbol generator and ordinal scale. A custom axes module adds labeled x and y axes using d3.axisLeft and axisBottom, while a shape legend provides a key for species, positioned on the right. Data is fetched as CSV and parsed with d3.csvParse.

Activity6_question4

This bubble chart compares major tech companies by research and development spending (x-axis) against revenue (y-axis), with bubble size encoding employee count and color distinguishing each company. It uses D3.js v7 to render an interactive SVG visualization, loading data from a CSV file. Hovering over a bubble triggers a custom tooltip showing the company's R&D spending, revenue, and total employees. A color legend on the right identifies each company, and the chart includes labeled axes with linear scales and a square-root scale for bubble radii.

Activity6_tutorial4

This bubble chart shows the correlation between average horsepower, torque, and highway MPG across 31 car makes, with circle position encoding the first two variables, size encoding MPG, and color distinguishing each manufacturer. The visualization uses d3.csv to load data and d3.scaleLinear for x, y, and radius mappings, with d3.axisBottom and d3.axisLeft generating axes. Interactive tooltips appear on mouseover using D3 event handling, while a legend displays MPG circle sizes and color-coded make labels. Rendering uses D3 v7 with SVG, including a custom color scale via d3.scaleOrdinal and d3.interpolateTurbo.

Activity6_tutorial3.2

This example shows a scatter plot of the Iris dataset, mapping sepal length on the x-axis and petal length on the y-axis. Each point is rendered as a different shape (circle, square, triangle, etc.) based on the iris species, with a legend explaining the shape-to-species mapping. The visualization uses D3 v7’s `scaleLinear`, `scaleOrdinal`, and `symbol` APIs to position marks and generate custom path shapes. Data is loaded as CSV from a GitHub repository and parsed with `csvParse`, while axis and legend components are built using `axisLeft`, `axisBottom`, and reusable selection patterns.

Activity6_tutorial4

This visualization presents a bubble chart exploring the relationship between average horsepower, torque, and highway MPG across various car makes. Each bubble’s position maps to horsepower and torque, while its size encodes highway MPG and color distinguishes the manufacturer. The chart reads data from a CSV file using d3.csv, employs d3.scaleLinear for axes and bubble sizing, and d3.scaleOrdinal with d3.interpolateTurbo for color mapping. Interactive tooltips appear on mouseover, and legends detail both MPG scale and make colors.

Activity6_tutorial4

This visualization presents a bubble chart exploring the relationship between average horsepower, torque, and highway MPG across various car makes. Each bubble’s position maps to horsepower and torque, while its size encodes highway MPG and color distinguishes the manufacturer. The chart reads data from a CSV file using d3.csv, employs d3.scaleLinear for axes and bubble sizing, and d3.scaleOrdinal with d3.interpolateTurbo for color mapping. Interactive tooltips appear on mouseover, and legends detail both MPG scale and make colors.

Activity6_tutorial4

This visualization presents a bubble chart exploring the relationship between average horsepower, torque, and highway MPG across various car makes. Each bubble’s position maps to horsepower and torque, while its size encodes highway MPG and color distinguishes the manufacturer. The chart reads data from a CSV file using d3.csv, employs d3.scaleLinear for axes and bubble sizing, and d3.scaleOrdinal with d3.interpolateTurbo for color mapping. Interactive tooltips appear on mouseover, and legends detail both MPG scale and make colors.

Fork of Fork of Scatter Plot with Shape

This scatter plot visualizes the Iris dataset, mapping sepal length against petal length for three species. Each species is represented by a distinct symbol shape, with a legend showing the mapping between the categorical species variable and the corresponding shape. The visualization uses D3 v7, employing `scaleLinear` for the axes, `scaleOrdinal` with `symbolsFill` for shape encoding, and custom modules for axes and legend rendering. Data is loaded from a remote CSV file and parsed using `csvParse`.

Activity6_tutorial3.2

This interactive scatter plot visualizes the Iris dataset, showing sepal length against petal length with each species represented by a distinct symbol. The visualization uses D3.js v7 APIs including scaleLinear, scaleOrdinal, and symbol to create the plot, with axes imported from the d3-axis module. A custom shape legend displays the species mapping using D3's symbol generator. The data is loaded asynchronously from a remote CSV file and parsed using d3.csvParse, with careful number coercion for numeric columns.

Activity_tutorial3.2

Activity6_tutorial3.1

This scatter plot visualizes the Iris dataset, displaying petal length on the x-axis and petal width on the y-axis. Each circle is colored by species—sky blue for setosa, light green for versicolor, and orange for virginica—with all points rendered as fixed-size circles. The visualization uses the `d3.csv` loader to fetch data from a GitHub repository, `d3.scaleLinear` for axis scaling, and `d3.axisLeft` and `d3.axisBottom` for chart axes. The SVG canvas resizes to the browser window, and the code employs D3's data-join operator (`.join`) to efficiently bind data to circle elements.

Activity_quetion2

This visualization is a stacked horizontal bar chart that shows the average daily time, in minutes, that different age groups (Kids, Teens, Adults, Middle-aged, Seniors) spend on five activities: Screen Time, Physical Activity, Reading, Socializing, and Household Chores. The bars are segmented by activity and colored according to a categorical scale, with a tooltip revealing the exact minutes for each activity on hover. The chart is built with D3.js v7, using its `stack()` API to create the stacked segments and `scaleBand()` for the categorical y-axis. It renders to an SVG canvas and parses data from a local CSV file. The legend, axis labels, and styling are defined in an external CSS file.

Activity6_tutorial2

This stacked bar chart visualizes the distribution of IMDb ratings across six movie genres, showing the proportion of films falling into five rating brackets from "below 5.0" to "above 8.5." Each bar represents a genre, segmented by rating category with distinct colors. The visualization uses d3.stack() to compute the stacked segments, d3.scaleBand for the categorical y-axis, and d3.scaleLinear for the percentage x-axis. Hover interactions highlight individual segments and display a tooltip with the exact percentage, computed from the pre-aggregated proportions in the CSV data.

Activity6_question1.2

A horizontal bar chart displays the population of 20 countries in 2010, using data from a GitHub-hosted CSV file. The bars are rendered as lightgreen rectangles using SVG, with a linear x-scale for population values and a band y-scale for country names. D3 v7 handles the axis generation and formatting, converting population figures to abbreviated labels like "B" for billions. The chart includes labeled axes for both population and countries, and the code is organized into separate modules for data loading, axes, and marks.

Activity6_question1.1

This horizontal bar chart displays the top 10 most populous countries in 2000, with light green bars sorted by population and labeled along a formatted x-axis that converts values to billions. The visualization is built with D3 v7, using a linear scale for population (x-axis) and a band scale for country names (y-axis). The code imports separate modules for the axes and marks, applies a `.2s` tick format that replaces "M" with "B", and loads CSV data from a GitHub repository via `d3.csv`, rendering the final output as SVG.

Activity6_task1

This horizontal bar chart displays the 2020 populations of 30 countries, with bars sorted by country name along the y-axis and population values along the x-axis. The visualization uses D3 v7 to load CSV data from a GitHub repository, then renders SVG rectangles with a light green fill. The x-axis uses a linear scale with custom tick formatting to abbreviate large numbers (e.g., 200M, 1.2B), while the y-axis employs a band scale to position the country labels. Axis labels are added via the AxisBottom and AxisLeft components.