Skip to main content
100%

Gist 4cd8f6cf6b2c29372f82424c1363d08a

✓ Published0🌍 Public
VVioletVivirand
Last edited Apr 21, 2017
Created on Apr 21, 2017

This visualization demonstrates a minimal time-series dataset containing two data points, each with a Unix timestamp and a numeric value. It uses D3.js to render the data as a simple line or area chart on an SVG canvas, likely plotting time on the x-axis and value on the y-axis. The code initializes a D3 scale and axis to map the input coordinates, then draws a path connecting the two points. The sparse dataset highlights how the visualization handles gaps or immediate transitions between values, with the second point dropping to zero shortly after the first.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 74cb28602595c2a18d8c9de77ebabe78

This visualization shows a time-series dataset from `timedata.json`, where each entry pairs a Unix timestamp in milliseconds with a numeric value, here displaying two points: 1483228800000 mapping to 2 and 1483228820000 mapping to 4. The example renders these as a simple line or scatter plot, likely using D3.js with `d3.scaleTime` for the x-axis and `d3.scaleLinear` for the y-axis. The code constructs an SVG container, binds the data, and draws circles or a path to represent the values, while axes are generated via `d3.axisBottom` and `d3.axisLeft`. The visualization focuses on demonstrating a minimal, direct mapping of raw timestamped data to visual marks without additional transformations.

VVioletVivirand
89% match
Loading thumbnail…

Gist 130517

This visualization displays a sparse, grid-based dataset from a gist by DylanFM, using a fixed-size coordinate system (values such as 1056, 64, 1152, and 4608) to plot points in a two-dimensional space. The rendering relies on D3’s `d3.forceSimulation` for positioning and likely `d3.geoPath` or a similar path generator to draw shapes, though the specific API is not explicitly shown. The data, provided as a plain text file, is read and mapped directly to screen coordinates without additional transformation, creating a minimal scatter-like plot. The example emphasizes raw data plotting with minimal abstraction, focusing on the direct conversion of numeric inputs into visual marks.

DDylanFM
79% match
Loading thumbnail…

Gist b126925b336690e07481fe09b384f125

This example demonstrates a time-series line chart showing closing values over a two-month period. The visualization uses a single blue line to connect data points, with a time-scaled x-axis and a linear y-axis that starts at zero. The code employs D3 v4’s `d3.line()` generator, `d3.scaleTime()`, and `d3.scaleLinear()` to map the data, which is loaded from an external tab-separated file. Axes are rendered via `d3.axisBottom()` and `d3.axisLeft()`, with the SVG canvas sized at 960 by 500 pixels plus margins.

335degrees
77% match
Loading thumbnail…

Gist bb8fddc4a5fc3cc60c99

A line chart maps a small array of ten numbers to an SVG path, showing how the values fluctuate across a linear sequence. The visualization uses a red, unbroken polyline to emphasize the shape of the data, with the y-axis scaled from the data’s minimum to maximum and the x-axis evenly spaced by index. The code creates the chart using d3.scale.linear for both axes and d3.svg.line to generate the path, which is appended to an SVG container via d3.select and datum. The fixed width and height of 500 pixels define the plotting area, while the stroke and fill styles come from a CSS class.

CCaged
76% match
Loading thumbnail…

Gist fe5834a6c79855e23c3c7e3ae7e7f048

This example visualizes the distribution of a dataset through an interactive scatterplot, encoding three variables on the x- and y-axes and point size. The visualization uses D3.js with `d3.forceSimulation` to arrange overlapping points into a readable layout, while `d3.scaleLinear` and `d3.axisBottom` handle axis generation. Rendering is done via SVG circles, and transitions are applied using `transition.attrTween` to smoothly update positions when data changes. The underlying data originates from a static JSON array embedded in the gist, with no external data source, and the code includes hover tooltips via `d3.select().on("mouseover")` for individual point inspection.

AAlexDaGr8
75% match
Loading thumbnail…

Gist 890029

This visualization maps the timestamps of Gist 890029 commits as a radial time-series layout, showing activity across days of the week and hours. It uses d3.forceSimulation to position circular nodes on a spiral, with each point encoded by day and time. The rendering relies on d3.quadtree for collision detection and a custom path generator via d3.curveLinear, while axis labels and tick marks are drawn using d3.axisTop and d3.axisLeft. The source data comes from the gist’s file list, where each line is a timestamp parsed into a Date object. Color intensity varies by frequency, and the visualization highlights daily rhythms through a stacked circular band.

CCaged
75% match
Loading thumbnail…

DataViz_TP2

This example visualizes a simple line chart of monthly values using D3.js v4, rendering an SVG with a time-scaled x-axis and linear y-axis. It displays labeled data points as light grey circles connected by a black line, with the final value annotated at the end. Hovering over a circle highlights it in red, reveals the corresponding value label, and draws a dashed vertical guide line to the bottom of the chart. The code uses `d3.scaleTime`, `d3.scaleLinear`, `d3.line`, `d3.json` for loading `dataset.json`, and transitions via `transition().style()` for interactive feedback.

BBkilian971
75% match
Loading thumbnail…

connect the dots

This animated scatterplot visualizes a time series of 50 data points, connecting them in chronological order with a smooth line. Each dot represents a moment in time, with its vertical position encoding the "rate" value while the x-axis maps temporal progression. The visualization uses D3 v4's SVG rendering to draw a continuously-updating path that links each point to the next, creating the sense of drawing a connected line through the data. As time advances, the line extends to incorporate new points while earlier segments remain visible, producing a dynamic "connect-the-dots" effect. The animation highlights the temporal flow and changing values across the dataset, with the line's movement revealing patterns in the rate changes over time. The visualization uses a minimal, uncluttered aesthetic that focuses attention on the trajectory of the data points.

EEranM6
75% match