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@alanponce·6 public vizzes
The visualization is a stacked horizontal bar chart titled "Resume keywords by category," showing the distribution of matched skill keywords across multiple candidates. Each bar represents a candidate, with segments colored by skill category—Statistics, NLP, Machine Learning, Deep Learning, R, Python, and Data Engineering—and labeled with category counts. The code extracts text from PDF resumes using PyPDF2, matches phrases via spaCy's PhraseMatcher against a keyword CSV, and aggregates counts with pandas. It then renders the chart using Matplotlib's `barh` with stacked bars and inline text annotations, displaying each candidate’s keyword profile.
This visualization displays a movie dataset from a JSON file, plotting each film as a circle positioned by a force-directed simulation. The circles are colored by genre and sized according to rating, with a legend mapping colors to categories. The code uses `d3.forceSimulation` to compute layout, with `d3.scaleOrdinal` for genre colors and `d3.scaleLinear` for circle radii. Titles appear as labels near each node, and the chart updates smoothly as forces settle the positions.
This radial visualization displays the dependency structure of the Flare visualization toolkit, using data from a JSON file of class imports. The D3.js layout.cluster algorithm positions classes in concentric rings around a central point, with hierarchical relationships encoded by radius. Curved, bundled links (using `d3.svg.line.radial` with bundle interpolation) connect classes to their imports, revealing dependency patterns in the software architecture. Leaf nodes are labeled with class names, and the radial arrangement organizes the hierarchy from the center outward. The visualization is rendered as an SVG, with steel-blue links and plain-text node labels, providing an overview of the dependency network.
This example demonstrates how to create high-quality scatterplots using base R graphics, covering both basic and intermediate plotting techniques. It uses the built-in `trees` and `iris` datasets to show relationships between variables, including girth versus volume for black cherry trees and sepal length versus petal length for iris species. The code illustrates core R functions like `plot()`, `rep()`, `par()`, and `axis()`, with features such as custom point characters, colors, legends, best-fit lines, confidence intervals, and multipanel layouts.
This example embeds a Google Map iframe that displays a KML layer from the University of Southampton’s data viewer, showing geographic points at a specific latitude and longitude with zoom level 16. The HTML page contains a styled iframe with fixed dimensions and a border, alongside links to view the map larger or in full screen. The code relies on Google Maps’ query parameters and the external KML URL, with a fallback message for browsers lacking iframe support.
This example uses R’s Hmisc::bezier and ggplot2 to recreate an economics-style supply-and-demand graph. It shows how a rightward shift in the demand curve (from D1 to D2) changes the market equilibrium, with the new intersection point highlighted and dotted reference lines drawn to the axes. The code generates smooth curves from control points via bezier(), approximates curve intersections by brute force using the proxy distance matrix, and annotates the plot with labels, an arrow, and custom axis breaks. It employs theme_classic() and coord_equal() for a clean, fixed-aspect visualization.