Transforming data of Historical NBA Performance

Historical NBA Performance

This example visualizes historical win-loss records for every NBA team, showing winning rates over time. The data is fetched as CSV and parsed with `d3.csvParse`. While loading, the state flag triggers a re-render; the code currently outputs the parsed data as raw JSON text in a `pre` element using `d3.select` and `.text()`. The visualization logic is scaffolded through a `viz` function that manages state transitions via a `setState` callback, with the fetch initiated when `state.data` is undefined.

84% match

Final Project

This interactive NBA visualization compares team winning percentages from 2010 to 2022 using two switchable chart types. A line chart displays seasonal win rates for all teams, highlighting a selected team in black, while a scatter plot shows the relationship between a team’s historical average win rate and its performance in a selected season using team logos as points. Users toggle between charts via buttons, select teams or seasons from dropdown menus, and hover over scatter plot logos for detailed tooltips. The visualization is built with D3 v7, using CSV data parsed and aggregated with `csvParse` and `flatRollup`.

77% match

Gist 1e62506e7bca2ddfbb82

This visualization maps every NBA game since 1946 onto a grid where the x-axis represents combined losses and the y-axis represents combined wins. Each cell’s color and size indicate the number of games played between teams with that specific combined win-loss record. The chart uses d3.csv, d3.nest, and d3.scale.quantile to process the game data and render the colored rectangles. The visualization also highlights the best and worst historical records with step-after lines, making it easy to compare team performance over time.

11wheel

Fork of Line graph

This interactive line graph visualizes the historical win rates of NBA teams from 2010-11 through 2021-22, with the x-axis representing the season year and the y-axis showing each team’s winning percentage. A dropdown menu lets the viewer highlight one of 30 teams, whose line is drawn in red while the others remain black. Built with D3 v7, the visualization uses `scaleTime` and `scaleLinear` for axes, `line` for path generation, and a `join` pattern for updating SVG elements on state changes. The data is loaded from a CSV file parsed with `csvParse`.

Transforming Data_NH

This visualization demonstrates binned aggregation and data transformation techniques using D3.js (v7). It loads solar energy data from a CSV file and computes the average maximum wattage per site address using `flatRollup` and `sum` functions. The resulting summary is displayed as formatted JSON text within a `<pre>` element, showcasing how raw tabular data can be programmatically summarized. The example uses D3's `csvParse` for data parsing and `select` for DOM manipulation.

NNavak94

Transforming Data

This example demonstrates data transformation from a CSV file using D3.js. The visualization loads fruit pricing data and calculates the average sepal length for each species using `flatRollup` and `sum`. The transformed data is then rendered as a JSON string in a `<pre>` element. The code utilizes `csvParse` for data ingestion, `select` for DOM manipulation, and the `flatRollup` aggregation API to summarize the dataset.

Rrushithareddyy

week 8 final project

This interactive dashboard presents the historical win rate of the Boston Celtics NBA team across seasons. The visualization begins with a static logo, and clicking it triggers a line chart that plots the winning percentage (W/L%) against the year, using D3's `scaleTime` and `scaleLinear` to map the data. The chart is rendered with `d3.line` and custom axes created via `axisLeft` and `axisBottom`. Data is loaded from a provided CSV file using `d3.csvParse`.

72% match