Fitness Data Pie Chart

Fitness Data Pie Chart

This pie chart visualizes the distribution of fitness goals from a cleaned CSV dataset, with each slice labeled directly on the chart and accompanied by a color-coded legend. The chart is rendered using D3.js v7 in an SVG container, with the data aggregated via `d3.rollup` to count occurrences per goal. The visualization employs `d3.pie` and `d3.arc` for the layout and shape generation, while `d3.scaleOrdinal` with `schemeCategory10` assigns slice colors. A gradient background and translucent chart container provide stylistic contrast, and the data is fetched asynchronously from a GitHub gist URL.

98% match

Fitness Data Pie Chart

This pie chart visualizes the distribution of fitness goals from a CSV dataset, showing the percentage share of each goal category. D3 v7 loads the data with `d3.csv`, aggregates counts using `d3.rollup`, and generates the pie with `d3.pie` and `d3.arc`. A categorical color scale from `d3.schemeSet2` fills the slices, which are labeled with percentage values. A legend on the right, built with SVG rectangles and text, maps each color to its corresponding goal, making the chart easy to read at a glance.

98% match

Fitness Data Pie Chart

This pie chart displays the distribution of fitness goals from a CSV dataset, showing the percentage share of each goal category. The visualization uses D3.js (v7) to load the data, aggregate it with `d3.rollup`, and render the chart as SVG with `d3.pie` and `d3.arc`. A slice label shows the rounded percentage for each segment, while an ordinal color scale (`d3.schemeSet2`) and a custom legend identify the goals. The chart is centered within the SVG, with a white background and stroke separation between slices.

97% match

Fitness Data Area Chart

This area chart displays the total workout duration for each fitness goal, aggregated from a cleaned CSV dataset. The visualization uses D3.js v7 to load the data, compute sums with `d3.rollup`, and render a smooth monotone area path. A point scale positions goals along the x-axis, while a linear scale maps total durations to the y-axis, with axes and labels generated via `d3.axisBottom` and `d3.axisLeft`. The SVG output features a teal filled area, styled with a white background and minimal margins.

82% match

SSathviKankanala@my.unt.edu

Pie Chart

A pie chart displays the average calories burned for each workout type—Cardio, HIIT, Strength, and Yoga—based on data from lifestyle.csv. The code uses D3 v7’s `d3.csv`, `d3.rollup`, and `d3.mean` to aggregate the data, then `d3.pie` and `d3.arc` to generate the SVG paths. A categorical color scale with `d3.schemeSet2` colors each segment, and labels are placed using `arc.centroid`. A legend is appended manually using D3 selections.

PieChart-D3

A pie chart displays the distribution of fruit categories from a CSV file, with each slice labeled and a corresponding color legend. The chart is rendered as SVG using D3 v7, with the pie layout created via `d3.pie` and `d3.arc`, and colors assigned through `d3.scaleOrdinal` with `d3.schemeCategory10`. Data is loaded asynchronously with `d3.csv`, and slice labels are positioned using `arc.centroid`. The legend is built manually by appending colored rectangles and text elements.

Kkblikitha22@gmail.com

CChaturyaAkhilaPallem@my.unt.edu

PIE CHART pca

This example shows the distribution of BMI values from a loaded CSV dataset, categorizing individuals into underweight, normal weight, overweight, and obese groups as a pie chart. It uses D3 v7 to fetch and parse the CSV, compute category counts and percentages, and render the chart as SVG with a color scale and legend. The chart includes an interactive tooltip and highlights the proportion of each category, with the pie arc drawn using `d3.arc` and data bound through `d3.pie`.

Fitness Data Reusable Scatter Plot

This reusable scatter plot visualizes fitness tracking data, plotting relationships between calorie intake, protein intake, and mental wellbeing scores from a CSV dataset. The chart uses D3 v7 to render SVG circles with configurable axes, margins, and data accessor functions. The code demonstrates a modular chart factory pattern with setters for width, height, and value fields, allowing users to easily swap which metrics appear on each axis. Axis labels update dynamically based on the selected fields, while steelblue semi-transparent points highlight distribution patterns without animation.