Corey's Parallel Coordinates with Brushing

CyberAttacks Final Draft v5

Two coordinated visualizations explore cyber attack data from 2014 to 2023. A multi-line chart displays attack counts per industry over time, with a clickable color legend; selecting an industry filters the adjacent scatter plot to show only that sector's attacks by year and attack type, colored by actor. The scatter plot supports d3.brush for highlighting points. The code uses d3.scaleOrdinal, d3.line, d3.axisLeft, and d3.axisBottom, rendering as SVG with data sourced from a local dataset.

79% match

CyberAttacks Final Draft

This visualization presents cyber attack data from 2014 to 2023 through two linked panels. The left panel uses d3’s `line` generator and `scaleTime`/`scaleLinear` to show attack counts per industry over time, with a custom color legend supporting twenty categories. The right panel displays filtered scatter plot data using `scalePoint`, showing attack types by year for a selected industry, with actor-based coloring. Interaction includes hover highlighting and click-based filtering, implemented through d3 selection joins and event handlers.

76% match

CyberAttacks ScatterPlot - Final Project Draft v1

This interactive scatter plot visualizes cyber attack event types against years, with points colored by attacking actor. Users can brush over the plot to highlight specific data points. The visualization uses D3 scales (scaleOrdinal, scalePoint), axes, a color legend, and brush behavior to enable selection. Data comes from the University of Maryland CISSM Cyber Attacks Database, filtered by industry code for display.

75% match

CyberAttacks Combined Vizs- Final Project Draft v2

This visualization presents a combined line chart and scatter plot of cybersecurity attack data, which is filtered by three industry selectors. The line chart shows trends over time with a color legend, while the scatter plot displays the distribution of attack subtypes per year and allows users to directly brush-select data points on the plot. Built with D3.js, the code uses the `scaleTime`, `scaleLinear`, and `scaleOrdinal` APIs for scales, `line` for the line generation, and `brush` for interactive selection. The underlying datasets, `full_cyber_attack_data` and `newData`, are imported from an external package.

Fork of Parallel Coordinates with Brushing

This interactive parallel coordinates visualization enables exploration of multidimensional datasets through linked brushing and filtering. Built with D3.js, it renders lines for each data point across categorical and quantitative axes, using `scalePoint` and `scaleLinear` Y-scales respectively. Users can brush along any axis to filter data across all dimensions simultaneously, with the brushed intervals managed as state. The visualization responds to container resizing and uses a custom color scale with a hue shift for enhanced category distinction. It supports both categorical and quantitative columns, making it adaptable for various datasets.

Vvigneshs1006@gmail.com

Fork of Parallel Coordinates with Brushing

This interactive parallel coordinates plot visualizes COVID-19 case data across six quantitative dimensions—total cases, deaths, recovered, active cases, tests, and population—using D3.js in React. Each line represents a country, colored by total deaths, with a cyan-on-navy color scheme and screen blending for overplotting. Users can brush along any axis to filter the data; brushed intervals persist across axes, and the polylines fade smoothly (500ms) to show only rows within all active selections. The visualization supports dynamic brushing interactions, enabling exploratory analysis of multi-dimensional relationships in the dataset.

Wwkj0713@gmail.com

Cyber Attacks by Year v2 w/ Responsive Axes

This line chart visualizes annual cyber attack counts from the CISSM Cyber Attacks dataset, with hover guides that track the mouse position. The visualization uses D3 scales and axes rendered via custom `xAxis` and `yAxis` functions from the `corey-responsive-axis` package, which adapt to container resizing through `observeResize`. The line is drawn using D3’s `line` generator with a time scale for years and a linear scale for attack counts. Data was pre-processed in Python before being imported into the VizHub environment.

Fork of Cyber Attacks by Year v3

This fork of the Cyber Attacks by Year visualization displays annual cyber attack counts from 2014 to 2023, with multiple colored lines representing different industry codes. The chart uses D3.js with a time-scaled x-axis, linear y-axis, and an ordinal color scale based on the category10 scheme. Responding to mouse position, it draws red crosshair lines and uses axis labeling methods from the `@python-monty/corey-responsive-axis` library. The data originates from the CISSM Cyber Attacks database, and the visualization is implemented using `svg`, `scaleTime`, `scaleLinear`, `line`, and `flatGroup` APIs.