chamarthisreenivasvarma@gmail.com

Sreenivas Varma_ICE8_Networkgraph

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Sreenivas Varma ICE-8

Actvity6_question1

Tree Chart

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Tree graph

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Tree graph

Tree graph

Tree graph

Tree graph

Fork of Reusable D3 Scatter Plot

This fork of a reusable D3 scatter plot animates through different iris flower measurements, cycling the x-axis between petal width, sepal width, petal length, and sepal length every two seconds using `setInterval`. The visualization uses D3 scales (`scaleLinear`, `extent`), axes (`axisLeft`, `axisBottom`), and SVG circle elements to render the data, which comes from a CSV file loaded via `d3.csv`. The plot is built as a configurable closure function that updates in place with each data change.

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This example demonstrates loading and parsing a CSV dataset of CSS named colors and rendering it as an interactive bar chart using Chart.js within a React application. The visualization aggregates the frequency of each team's colors, displaying the counts as vertical bars colored by the team’s primary color. The chart is rendered on an HTML canvas, with a clean, sky-blue background. The data, sourced from a CSV of NCAA team colors, is parsed and processed in JavaScript, with team names on the x-axis and color counts on the y-axis. The visualization highlights how color usage varies across teams, with bars colored to match each team's primary color.

Fork of Reusable scatterplot

This example uses Chart.js to render a scatter plot of the official colors associated with various college sports teams. The x-axis positions each team in sequence, the y-axis shows the rank of each color within that team’s palette, and each point is filled with its corresponding hex value from the CSS Named Colors CSV data. The visualization reveals the color sequences and frequencies across different universities.

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This example shows a scatter plot of team colors, where each point represents a color associated with a college sports team, plotted by index on the x-axis and a count value on the y-axis. The visualization uses Chart.js to render the scatter plot on an HTML canvas element, with each point colored according to the team’s hex color value. The data is embedded directly in the code rather than loaded from an external CSV file, and it draws from a dataset of CSS named colors and team color schemes.

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This example visualizes the CSS Named Colors dataset, which maps college sports teams to their official colors and the order in which each color appears. The visualization renders as a scatter plot using the Chart.js library on an HTML canvas, with each point representing a color entry positioned by its index in the dataset. Points are colored using the actual hex values from the data, while the SVG file provides a simple film-strip graphic. The code demonstrates loading and parsing CSV data to drive the chart, with a React-based approach for structuring the application.

Fork of Fork of Fork of Fork of Fork of Fork of Fork of Loading and Parsing CSV Data

This example demonstrates loading and parsing CSV data containing CSS named colors, then visualizing the results as a bar chart using Chart.js within a React application. The data is parsed to aggregate color counts per sports team, with each bar colored according to the team's primary color. The visualization renders as a static bar chart on an HTML canvas, with the chart positioned centrally on a sky-blue background for contrast. The code includes a custom aggregation function that processes the raw CSV-derived data and maps team names to their corresponding color counts and hex values for the bars.

Fork of Fork of Fork of Fork of Fork of Loading and Parsing CSV Data

This example demonstrates loading and parsing a CSV dataset of CSS named colors and rendering it as an interactive bar chart using Chart.js within a React application. The visualization aggregates the frequency of each team's colors, displaying the counts as vertical bars colored by the team’s primary color. The chart is rendered on an HTML canvas, with a clean, sky-blue background. The data, sourced from a CSV of NCAA team colors, is parsed and processed in JavaScript, with team names on the x-axis and color counts on the y-axis. The visualization highlights how color usage varies across teams, with bars colored to match each team's primary color.

Fork of Fork of Fork of Fork of Loading and Parsing CSV Data

This example demonstrates the data loading and parsing workflow for a CSV dataset of CSS named colors and college sports team colors. It uses a React frontend with Chart.js to render an SVG and canvas-based bar chart showing the aggregated color counts for each team. The code employs the `Chart` API from Chart.js, along with `getContext('2d')` for canvas rendering. The dataset is manually embedded within the JavaScript, bypassing external file fetching, and displays team names alongside their corresponding color hex values.

Fork of ICE6

This example shows a bar chart of the number of colors associated with each college sports team, aggregating the data from a flat list of team-color-count entries. It uses the Chart.js library to render the chart onto a canvas element, with team names on the x-axis and the aggregated color counts on the y-axis. The data is processed by first grouping entries by team and summing their counts, then feeding the resulting arrays into a bar chart configuration with a sky-blue background color.

Fork of Bar chart

This example visualizes the top 15 coffee-exporting countries using a horizontal bar chart, with Brazil leading in export volume. The data is loaded from a CSV file and sorted in descending order, with each bar’s length proportional to metric tons exported. D3 v7 renders the chart as an SVG, using a linear scale for the x-axis and a band scale for the y-axis, and includes rotated tick labels for readability. The visualization is titled "Top 15 Coffee Exporters" and includes axis labels for clarity.

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This bar chart displays movie budgets, comparing international films in the primary dataset with a secondary Indian film dataset. The visualization uses SVG rendering with D3 v6, employing `scaleLinear` for the budget axis and `scaleBand` for categorical movie names. Bars are colored with a green-to-orange linear gradient, while axis labels appear in red. Data is loaded from CSV files using D3’s `csv` function, with budgets parsed as numbers. The chart includes a rotated y-axis label and tick marks with formatted budget values using D3’s `format` function.

Fork of Circles with D3

This example builds a cheerful figure of a snowman from basic geometric shapes, using D3.js to bind data arrays to SVG circles, lines, rectangles, and polygons. The code explicitly sets coordinates, radii, and colors for each element, including the snowman’s body, eyes, and a striped hat, while also drawing a ground line and a decorative arc via d3.arc. A separate CSV file is loaded with d3.csv, and its row and column counts are displayed in a message container. The visualization relies on D3’s data joins and static SVG attributes rather than transitions or scales.

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This visualization displays the job placement status of students from a CSV dataset, comparing the percentages of placed and unplaced individuals through a pie chart. The page also presents a text summary of the dataset's size, row count, and column count. Built with React and D3 v7, it loads data via `d3.csv` and uses `d3.pie` to compute arc segments, with `d3.scaleOrdinal` for color mapping. The chart is rendered as SVG, with labels showing each category's name and percentage.

Fork of Fork of Fork of Fork of Loading and Parsing CSV Data

This visualization is a web page that loads and parses a CSV file containing historical movie metadata, displaying a summary of the file's size, row count, and column count. The page features a filmstrip-themed SVG graphic and uses the D3.js library's `d3.csv` function to fetch and parse the remote data. The interface is built with HTML, CSS, and React, with a bright pink background and white headings, all rendered as an SVG file.

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This example visualizes the metadata of a historical movie collection, starting with a stylized SVG film strip graphic. The page's primary function is to load and parse a CSV file of movie data using the D3.js library. The code uses `d3.csv` to fetch the remote CSV file and then calculates and displays summary information about the dataset, including file size and row and column counts, in a message container. The page is built with HTML, CSS, and SVG, styled with a bold color scheme.

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This example demonstrates the loading and parsing of a CSV dataset containing job placement information, showing a summary of the file size, row count, and column count. It uses `d3.csv` to fetch and parse the data from a GitHub Gist URL, then computes the number of placed versus unplaced students and visualizes these proportions as a pie chart rendered with SVG paths using `d3.pie` and `d3.arc`. The visualization uses a color scale with `d3.scaleOrdinal` to distinguish the two categories.

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This example demonstrates loading and parsing CSV data using D3’s `d3.csv` method, displaying a summary of the dataset’s size, row count, and column count in a preformatted text block. The page also includes a static SVG rendering of a quantum circuit with qubits (Q0–Q2) and gates like H and CNOT, drawn using basic SVG shapes. The visualization is built with React and D3 v7, using `d3.csvFormat` to compute the data’s byte length for the summary. The underlying data source is a CSV file containing CSS named colors.

Fork of Fork of Fork of Loading and Parsing CSV Data

This example demonstrates loading and parsing CSV data using D3’s `d3.csv` method, displaying a summary of the dataset’s size, row count, and column count in a preformatted text block. The page also includes a static SVG rendering of a quantum circuit with qubits (Q0–Q2) and gates like H and CNOT, drawn using basic SVG shapes. The visualization is built with React and D3 v7, using `d3.csvFormat` to compute the data’s byte length for the summary. The underlying data source is a CSV file containing CSS named colors.

Fork of Fork of Fork of Loading and Parsing CSV Data

This example demonstrates loading and parsing CSV data using D3’s `d3.csv` method, displaying a summary of the dataset’s size, row count, and column count in a preformatted text block. The page also includes a static SVG rendering of a quantum circuit with qubits (Q0–Q2) and gates like H and CNOT, drawn using basic SVG shapes. The visualization is built with React and D3 v7, using `d3.csvFormat` to compute the data’s byte length for the summary. The underlying data source is a CSV file containing CSS named colors.

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This example loads and parses a CSV dataset containing movie metadata using D3’s `d3.csv` method, then displays a summary of the file’s size, row count, and column count in a message container. The page, styled with a pink background and white text, is built with HTML, CSS, and SVG, featuring a simple film-strip graphic with sprocket holes and movie frames. It also incorporates a YouTube video link and is implemented within a React framework using D3 v7.

Fork of Fork of Fork of Fork of Fork of Loading and Parsing CSV Data

This example demonstrates loading and parsing a CSV file containing movie metadata using the D3.js library (v7) within a React framework. The page displays a summary of the data, including file size in bytes and kilobytes, the number of rows, and the number of columns, which is rendered into an HTML `<pre>` element. The visualization includes a decorative SVG film strip illustration created with basic shapes like rectangles, circles, and text. The code uses `d3.csv()` to fetch and parse the remote CSV data, and `d3.csvFormat()` to measure the file size.

Fork of Fork of Fork of Fork of Fork of Loading and Parsing CSV Data

This example demonstrates loading and parsing a CSV file of movie metadata, presenting a text summary of the dataset's size, row count, and column count within an HTML page. The visualization uses D3.js's `d3.csv` function to fetch and parse the data from a remote URL, then displays the summary in a `<pre>` element. The page, styled with CSS and featuring an SVG film-strip icon, is built with React and renders entirely through SVG, providing a straightforward illustration of CSV data ingestion and basic statistical reporting.

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This example loads a job placement CSV dataset using `d3.csv` and displays a summary of the data’s size, row count, and column count. It then calculates the number of placed versus unplaced students and renders a simple SVG pie chart with `d3.pie` and `d3.arc`, showing each category’s label and percentage. The chart uses an ordinal color scale and is styled with custom CSS in a React-compatible HTML page, though the interaction logic itself is plain JavaScript with D3 v7.

Fork of Fork of Fork of Fork of Loading and Parsing CSV Data

This example loads a job placement CSV dataset using `d3.csv` and displays a summary of the data’s size, row count, and column count. It then calculates the number of placed versus unplaced students and renders a simple SVG pie chart with `d3.pie` and `d3.arc`, showing each category’s label and percentage. The chart uses an ordinal color scale and is styled with custom CSS in a React-compatible HTML page, though the interaction logic itself is plain JavaScript with D3 v7.