clem2

Gist 2e92f7659a37c0aad9b95cd210fa517e

This Shiny app displays two ggplot2 scatterplots of the mtcars dataset, comparing engine cylinders and weight against miles per gallon, with point size controlled by a slider. The plot’s color scale represents weight (wt) as a continuous variable, and a shared legend is extracted and repositioned using a custom grid_arrange_shared_legend function built with grid and gridExtra. The server renders both plots inside a single output, pulling the legend from a grob via ggplotGrob. The UI uses a sidebar slider to adjust the point size interactively.

RRCura

Gist 9622037

This interactive Shiny application plots geographic data onto a web map, scaling point markers according to a selected attribute. Users choose latitude and longitude columns to position points and adjust the maximum point size via a slider. The interface is built with Shiny widgets, including selectInput and sliderInput, while the map itself is rendered using an HTML output that likely integrates a JavaScript mapping library. A secondary plot, generated with ggplot2, is also included. The code relies on Shiny's server-side plotting and reactive input handling to update both the interactive map and the static plot.

RRCura

Visualization

This interactive scatterplot visualizes a Principal Component Analysis of citation data from 2,000 philosophy articles from the 1990s, with points colored by K-Means clusters. Users can zoom and pan the plot using d3.behavior.zoom and hover over points to see tooltips generated by d3.tip. The visualization is built with D3 v3, rendering SVG circles, and includes a button that switches the x-axis to display carbohydrate values, triggering a transition that updates the axis and dot positions.

Visualization

A zoomable scatterplot displays a Principal Component Analysis of citation data from 2,000 philosophy articles, with points colored by K-Means clusters. The visualization uses D3 v3’s linear scales, axes, and zoom behavior to explore the bidimensional projection. Points are rendered as SVG circles with tooltips via d3-tip, and a button switches the x-axis variable, triggering a transition on the dot transforms. Data is loaded from a CSV file, and the plot includes a categorical color legend.

Visualization

This visualization displays a principal component analysis of citation data from 2,000 philosophy articles, projecting each article into a 2D scatterplot where points are colored by K-Means cluster assignment. The chart, built with D3 v3 and rendered as SVG, supports zooming and panning through the d3.behavior.zoom API. Each dot represents an article, with mouseover tooltips provided by the d3-tip library. The visualization includes an interactive button that switches the x-axis to display carbohydrate values, demonstrating a data-viewing alternative, though the primary dataset remains the citation components.

Gist c5e80c4ef3f4d3ac5fc33ece65d1490a

This animated scatterplot uses a simulation to show how countries move through a 2D space over time, encoding data from a comprehensive country-level dataset. Each country is represented as a point, with attributes like population, area, fertility, and GDP driving the positions or sizes of the marks. The visualization leverages D3.js v4 for smooth transitions and animated updates, making the shifting distribution of countries across quantitative dimensions easy to follow. The SVG rendering and motion clarify changes in rankings or relationships between variables, turning a dense multivariate table into an explorable temporal narrative.

11wheel

Visualization

This interactive scatterplot visualizes a principal component analysis of citation data from 2,000 philosophy articles, with k-means clustering applied to reveal groupings. The D3.js (v3) visualization uses SVG circles positioned via a zoomable, panning coordinate system, with color encoding cluster membership and tooltips showing exact coordinates. Users can click a button to switch the x-axis variable, triggering animated transitions of the data points. The code leverages `d3.scale.linear`, `d3.svg.axis`, `d3.behavior.zoom`, and the `d3.tip` library for interactivity, with data loaded from a CSV file.

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