Skip to main content
SimilarExport ZIP
100%

Cyber Attacks by Year v2 w/ Responsive Axes

✓ Published1🌍 Public
Ccoreyfnolan@gmail.com
Last edited Feb 19, 2024
Created on Feb 12, 2024

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.

AI-generated description

A simple line viz showing annual cyber attacks dataset from below: CISSM Cyber Attacks CISSM-EDA

Data was parsed/cleaned using Python before importing into VizHub. This is the same dataset used for simple scatter plot.

v2 - Added reponsive axis

Would have liked to include a separate line for each industry type that was attacked.

Challenge

Add responsive axis to an existing viz

MIT Licensed

Similar vizzes

Loading thumbnail…

Cyber Attacks by Year

This line chart displays the number of cyber attacks per year from the CISSM dataset. The visualization uses a simple black line against a light gray plot area, with labeled axes for year and attack count. Built with D3.js, the chart employs `scaleBand` for the x-axis and `scaleLinear` for the y-axis to position data points, which are connected using the `line` generator. The code also integrates the `corey-responsive-axis` library for axis labels and utilizes a reactive layout for resizing.

Ccoreyfnolan@gmail.com
90% match
Loading thumbnail…

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.

Ccoreyfnolan@gmail.com
87% match
Loading thumbnail…

Corey's Simple Line Chart

This visualization displays the number of cyber attacks per year across different industry types, using a simple line chart. The chart renders a single line showing total attacks over time, with a gray background rectangle highlighting the plotting area. The code imports d3 modules like `scaleBand`, `scaleLinear`, and `line` to construct the scales and line generator, while `xAxis` and `yAxis` from `@curran/axis-labels` handle the axes. Data originates from the CISSM Cyber Attacks database, parsed with Python, and the visualization is built using d3 version 7.8.5.

Ccoreyfnolan@gmail.com
84% match
Loading thumbnail…

CyberAttacks LineChart - Final Project Draft v2

This interactive line chart displays annual cyber attack counts across industry sectors, using data from the CISSM Cyber Attacks dataset. Three drop-down menus allow viewers to select up to three industry codes, and the chart renders a multi-line visualization with a color legend that updates dynamically. Built with D3.js, the visualization employs `flatGroup`, `scaleOrdinal`, and `schemeCategory10` to group and color lines, while custom axis and color legend modules handle responsive rendering. The chart includes an inner rectangle background and labeled axes showing year and attack counts for the filtered industries.

Ccoreyfnolan@gmail.com
84% match
Loading thumbnail…

Yearly Cyber Attack Data

This visualization presents yearly cyber attack counts from the CISSM Cyber Attacks dataset, showing fluctuations from 2014 to 2023 with a notable spike in 2022. The code parses the CSV data, converting year strings to Date objects and attack counts to numbers using `Date.UTC` before displaying the first ten records as JSON. It relies on the d3 library version 7.8.5 for data handling and rendering, though the current implementation only outputs the parsed data structure rather than a graphical chart.

Ccoreyfnolan@gmail.com
84% match
Loading thumbnail…

CyberAttacks LineChart - Final Project Draft v1

This interactive line chart visualizes annual cyber attack counts across selected industry sectors from 2018 onward. Users can select up to three industry codes via dropdown menus to compare attack trends over time, with each industry represented by a distinct colored line. The visualization is built with D3.js and uses the CISSM Cyber Attacks dataset, encoding years on the x-axis and attack counts on the y-axis. The chart includes a color legend and hover interactions to highlight specific data points.

Ccoreyfnolan@gmail.com
83% match
Loading thumbnail…

CISSM_CyberAttacks - Iterated

This example uses an iterated scatter plot to visualize cyber attack events from the CISSM Cyber Attacks dataset, plotting event date along the x-axis against the responsible actor on the y-axis. Each circle is colored by the industry targeted, using the `scaleOrdinal` and `schemeCategory10` schemes. The visualization leverages D3's `scalePoint` for both axes and renders data from the `@python-monty/cyber_attacksv2` package, with axis labels and a background rect for clarity. The code filters the data through custom `viz` and `axis_labels` modules built on D3 7.8.5.

Ccoreyfnolan@gmail.com
79% match
Loading thumbnail…

CISSM_CyberAttacks (Fork of Simple Scatter Plot)

This scatter plot visualizes CISSM cyber attack records, plotting event dates on the x-axis against attacker actors on the y-axis. Each circle is color-coded by the affected industry using D3’s `scaleOrdinal` and `schemeCategory10`. The visualization uses D3’s `scalePoint` for both axes and renders axis labels via the `@python-monty/axis_labels` library. The underlying data comes from the CISSM-EDA dataset, and the code forks an existing example to replace the sample data with cyber attack records.

Ccoreyfnolan@gmail.com
78% match