Fork of BARCHART-SUBHASH

Fork of ICE5_SUBHASH

This scatter plot visualizes YouTube channel analytics, showing the relationship between total views and watch time in hours across multiple weeks. Built with D3.js v7, it converts CSV data into a clean SVG chart with animated bubbles that grow into place. Hover interactions reveal a detailed tooltip containing weekly metrics like likes and subscriber gains. The visualization uses D3's scale functions for axis mapping, autoType for parsing, and transition methods to animate the dots.

84% match

Fork of HTML Starter-ic6

This scatter plot visualizes YouTube channel analytics, mapping total views on the x-axis against watch time in hours on the y-axis. Each circle represents a week of data, with hover interactions revealing a tooltip containing additional metrics like likes, comments, and subscriber gains. The visualization uses d3.scaleLinear for axes and d3.axisBottom/axisLeft for tick marks, while circles animate into place via d3.transition. Data is loaded from a remote CSV file using d3.csv, and the SVG rendering incorporates a dark theme with responsive sizing.

83% match

piechart-subhash

This example visualizes YouTube channel analytics as an animated, reusable pie chart that cycles through five metrics—Views, Likes, Comments, Watch Time, and Subscribers Gained—every two seconds. Built with D3 v6, it uses `d3.pie` and `d3.arc` to generate slices, `scaleOrdinal` with `schemeTableau10` for colors, and includes labeled leader lines plus a legend. The chart reads data from a CSV gist, parsing numeric fields, and renders as SVG with a `setInterval` driving the metric rotation.

subhash-areachart

This area chart visualizes YouTube channel analytics, plotting watch time in hours across weekly data points. The visualization combines a filled area path with a steelblue outline line to emphasize trends in the time-series data. Built with D3 v7, the reusable `areaChart` component uses `scaleLinear` for both axes, `d3Area` and `d3Line` for path generation, and `axisBottom`/`axisLeft` for tick rendering. Data is loaded from a CSV hosted on GitHub Gist and parsed with `csvParse`. The chart renders responsively to the full window size using SVG.

ICE5_SUBHASH

This example visualizes YouTube channel analytics across weekly data, combining bar charts for total views with an overlaid line graph for watch time in hours, using dual y-axes to compare the metrics. The SVG chart is rendered with D3 v7, and bars animate their height upward via a transition, while the line draws itself using stroke-dasharray and the curveMonotoneX interpolator. Tooltips provide detailed per-week values, and the dataset is loaded from a remote CSV file.

Fork of Refactored Bar Chartasd

This example renders a scatterplot of YouTube channel data, plotting channels along the x-axis and their actual subscriber counts along the y-axis. The visualization uses React to structure SVG elements, with D3 v5 providing the linear scales and tick generation through `scaleLinear` and `extent`. The data is loaded asynchronously from a CSV file using `d3.csv` in a custom `useData` hook. Axes are built manually from scale ticks, while the marks are ten-pixel-radius circles positioned via the scales. The source data comes from a GitHub gist containing channel statistics.

AAhmeTyAtakan

subhash-linechart

This line chart visualizes weekly YouTube channel analytics, specifically tracking watch time in hours across 52 weeks. The chart uses D3 v7 scaleLinear, line, axisBottom, and axisLeft APIs to render the time-series data. Raw CSV data is fetched from a GitHub gist, parsed, and normalized before being bound to a steelblue line path. The SVG output includes labeled axes for weeks and watch time, with the x-axis formatted to display week numbers.

79% match

subhash_PieChart-1

This interactive donut chart visualizes YouTube channel analytics, showing the distribution of a selected metric (Views, Likes, Comments, Watch Time, or Subscribers) across the top N weeks, with remaining weeks aggregated into an "Other" slice. Users adjust the metric and slice count via dropdown and number input controls. The visualization uses D3’s `pie`, `arc`, `scaleOrdinal` with `schemeTableau10`, and `csvParse` to load and process data from a GitHub-hosted CSV file, rendering slices, leader lines, labels, and a legend as SVG.