scatterplot-lines-SAINTS

NFL Scatter Plot

This scatter plot visualizes NFL player performance by plotting fantasy points against targets for the 2021 season, with player positions (RB, WR, QB) color-coded. The visualization uses D3.js v7 APIs such as `scaleLinear`, `axisLeft`, and `axisBottom` to create the axes and render SVG circles. Data is loaded asynchronously from a CSV hosted on GitHub using the `csv` method, and the drop-down menus for axis selection are present but not yet wired to update the chart.

AArturo Guevara

75% match

S18 - HW3 - Scatterplot

This interactive scatterplot visualizes 2014 NCAA passing statistics, comparing rushing touchdowns on the x-axis against passing touchdowns on the y-axis for individual quarterbacks. Each point represents a player, with circle size encoding total touchdowns and color mapping to the player’s athletic conference. Built with D3 v4, the visualization uses SVG rendering and includes hover tooltips. The data originates from sports-reference.com and is loaded via `d3.csv`, with axes and labels generated using `d3.axisBottom` and `d3.axisLeft`.

LLecy277

72% match

NFL Passing Yards Visualization 11/22/2024 Revision

This interactive scatter plot animates NFL quarterback statistics from 2013–2023, visualizing relationships between numeric attributes like passing yards and touchdowns. Using D3.js, it displays each player as a colored circle representing award status, with dropdown menus for selecting axes, filters for year or award type, and a text input for player search. Clicking a data point reveals a tooltip with player details, while hovering over the legend highlights matching points. The visualization supports dynamic data exploration through state management and uses D3’s selection, scale, and axis APIs for rendering.

72% match

NFL Passing Yards Visualization

This example visualizes NFL passing statistics as a scatter plot, with each circle representing a player’s performance across two metrics. The chart uses D3’s `scaleLinear` and `axisLeft` to map data points, while the `xAxis` and `yAxis` functions from the `@curran/axis-labels` library add labeled axes. The visualization reads from a dataset, rendering circles with configurable radius and opacity. The code demonstrates a reusable `viz` function that accepts data accessors and margin settings, allowing the same plotting logic to be applied to any dataset, such as the Penguins Dataset referenced in the README.

NFL Passing Yards Visualization Final Revision

An interactive scatter plot visualizes NFL quarterback passing statistics from 2013 to 2023, comparing two user-selected numeric attributes such as yards and touchdowns. The animation shows each player as a colored circle, with colors representing awards like MVP, All-Pro, or Pro Bowl. Users can filter by year, award, and player name. Built with D3.js, the visualization uses `scaleLinear`, `axisLeft`, and dynamic axes to update the scatter plot based on selections. Data from CSV files is preprocessed to assign awards and colors, and clicking a point displays a tooltip with player details.

NFL Passing Yards Visualization 11/15/2024 Revision

This interactive scatter plot visualizes NFL quarterback passing statistics from 2013 to 2023, allowing users to compare any two numeric attributes by selecting them from dropdown menus. The visualization also filters data by year and award status, with points colored to distinguish MVP, All-Pro, Pro Bowl, and no-award players. The D3.js implementation uses `scaleLinear` for axes, `axisLeft` for the y-axis, and data-driven circles that reveal tooltips on click, displaying player name, year, and axis values. Data preprocessing converts CSV values to numbers and categorizes awards before rendering.

Ppraveennayak1702@gmail.com

Fork of Fork of Random Squares and circles

This scatter plot visualizes the relationship between Year 1 and Year 2 wins for different teams, with each point representing a team’s performance across two consecutive years. The chart uses D3 v7 to load CSV data from a remote gist and render circles with steelblue fill at reduced opacity, allowing overlapping points to be distinguished. Axes are generated using D3’s `axisBottom` and `axisLeft`, with labels added via SVG text elements. The plot highlights outliers and patterns in team consistency over time, as described in the accompanying analysis text.

Pseudo-Visualization NFL Passing

This pseudo-visualization maps 100 randomly generated data points across two statistical axes, colored by a categorical legend for Pro Bowl, No Award, and All Pro. Built with D3 v7, it uses `scaleLinear` for position scaling, `scaleOrdinal` for legend colors, and the `@curran/axis-labels` library for labeled axes. The `plot` function draws an inner rectangle, axes, and a legend, while a nested `mark` function renders scatterplot circles. The code demonstrates composable D3 modules and scales, though the data itself is synthetic random values rather than actual NFL passing statistics.