Presentation_task3

Presentation_Task2

This visualization shows a red line chart tracking product prices from 2019 to 2023, with each year plotted as a point connected by a path. The data, loaded from a CSV file containing shopping mall transaction records, is aggregated by year. The code uses D3 v7 to create an SVG graphic, employing `d3.scaleBand` for the x-axis years, `d3.scaleLinear` for the price range, and `d3.line` to generate the path. Axes are added with `d3.axisBottom` and `d3.axisLeft`, and text labels identify the year and price dimensions.

77% match

Presentation_Task1

This chart displays the relationship between product quantity and price for five product categories, with each category represented by a distinct color: Clothing, Food & Beverage, Technology, Cosmetics, and Shoes. The data comes from a remote CSV file loaded via the `csv` function, and the visualization is rendered as an SVG using D3.js v5. The code uses `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` to construct the axes, and `select` appends the chart to the body. Each data point is drawn as a circle with a fixed radius, and the color is determined by the category using a custom switch function.

76% match

Presentation_Task6

This stacked area chart visualizes the total products available across five categories—Cosmetics, Clothing, Food/Beverages, Technology, and Books/Toys—from 2010 to 2023. Using d3.stack and d3.area with curveBasis, the SVG rendering displays each category as a filled area, colored via d3.scaleOrdinal with d3.schemeDark2. Labels are positioned through d3-area-label, while axes use d3.axisBottom and d3.axisLeft with a time scale for years. The code reads data from a CSV file and includes hover tooltips via SVG title elements.

76% match

Presentation_Task4

This bar chart displays the average customer spending on each product category, with horizontal bars representing the price values from the dataset. The visualization uses D3 v5 to load CSV data and render an SVG, employing `scaleLinear` for the x-axis and `scaleBand` for the categorical y-axis. Axis labels are generated with `axisLeft` and `axisBottom`, while each rectangle’s height is set using the y-scale’s bandwidth, and hover effects reduce opacity via CSS.

76% match

Pie Chart

This pie chart displays the sales figures of various companies, with each slice sized proportionally and labeled with its value in thousands. It also includes a color-coded legend listing company names and corresponding sales amounts. The visualization uses D3 v5 APIs including `pie`, `arc`, `scaleOrdinal`, and `format` to generate the SVG segments and text, with data loaded via `csv` from a GitHub gist. Colors are assigned through a custom ordinal scale.

TTejaswi@3271

pie chart

This pie chart visualizes the prices of the top 11 products from a CSV dataset, sorted in descending order and drawn as SVG arcs. Built with D3.js v5, it uses the `pie` and `arc` functions to generate segments, `scaleOrdinal` with `schemeCategory10` for color coding, and includes an interactive legend showing each product name and its price in dollars. Data is loaded asynchronously via `csv` from a GitHub gist, and the chart is rendered on mouseover logging.

Aadapavaishnavi12

Fork of Assignment ICE 7

This pie chart displays the number of units purchased for each item, with values labeled on each slice and a color-coded legend listing items and their corresponding counts. The visualization is built using D3 v5, loading CSV data from a GitHub gist and rendering with SVG. It uses `d3.pie`, `d3.arc`, and `scaleOrdinal` to generate the chart, while the legend is constructed manually with appended rectangles and text elements.

VVaishnavi6199

RRohithNaiduDevareddy@my.unt.edu

pie chart

This pie chart visualizes the top 10 store areas by sales, using D3 v7 to load CSV data, sort it, and render an SVG pie chart with colored slices, labels, and a legend. The code uses `d3.pie()` to compute arc angles, `d3.arc()` to draw paths, and `d3.scaleOrdinal()` for color encoding. Store area names and corresponding sales values appear both on the slices and in a legend, with data sourced from a GitHub-hosted CSV file.