Gist demonstrating an R error

ggvis issue - multiple layers on different dataset

This example shows a correspondence analysis of three random normal variables, with points colored by cluster and overlaid with 70% confidence ellipses. It first builds the analysis in R using `ade4`'s `dudi.coa`, then computes clusters via `hclust` and ellipses with the `ellipse` package. The visualization attempts to combine both layers in `ggvis`, using `layer_points` for the observations and `layer_paths` for the ellipses, but reveals a known issue where `group_by` on the path layer is not respected when mixing data frames, leaving the ellipses ungrouped in the final plot.

60% match

Gist 61957648a96af6b4167e

This example visualizes traffic crash records from a Gist file, mapping each participant’s location and attributes onto a canvas. It uses a force-directed layout with `d3.forceSimulation` to spatially arrange points, while `d3.geoPath` renders geographic features from the included geometry. The code reads the raw text data, parses fields like crash severity and participant details, and draws small circles colored by injury severity. It also employs Canvas `getImageData` for pixel-level rendering, and transitions with `transition.attrTween` to animate point movement. The result is an interactive scatterplot-style map of incident density and participant distribution.

CCaged

57% match

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

56% match

Gist 05f4cffac50217bd0c33a538cb013563

This example visualizes geographic routing and geocoding workflows in R, showing how spatial data can be processed and mapped. It installs and loads packages for text mining, word clouds, and interactive map viewing, including `ggplot2`, `tm`, `wordcloud`, and `RColorBrewer` for static and corpus-based visualizations. The code also integrates the `photon` and `osrm` packages via `devtools` to access geocoding and routing APIs, enabling point-and-route calculations. The visualization pipeline combines spatial operations with text analytics, demonstrating a multi-step data enrichment and mapping process.

56% match

Gist a7dcca17dd6f9275b5d6435007f6b9f0

A small scatterplot displays five manually defined points with x and y coordinates from a fixed data array. The visualization uses d3.forceSimulation to position the circles, applying a simple force layout that settles the nodes into place. Each point is rendered as a circle with a fill color and stroke, while the simulation drives subtle movement before stabilizing. The code does not include axes, labels, or interaction, focusing solely on the force-directed placement of the given data.

JJan0RodGar

Gist 9587685

This example maps user-uploaded shapefiles by plotting their polygon boundaries in a main panel. It uses R’s `shiny` framework with `server.R` and `ui.R` files, relying on the `maptools` library’s `readShapePoly` function to load the spatial data. A reactive `uploadShpfile` expression handles file renaming and directory switching for multiple uploaded components, while `renderPlot` and `plotOutput` display the resulting map. The interface provides a sidebar `fileInput` for selecting shapefiles and a central plot area for visualization.

Gist 6105613

This example demonstrates a common error-handling scenario in R’s `brew` templating system, where a template is processed into a text connection. The code attempts to render a map template by calling `brew(mapTemplate, output=textConnection("outputObj"))`, but it fails because the output connection is not writeable. The visualization shows the resulting error message, which indicates that `textConnection()` must be opened in write mode. The example uses the `brew` package’s API to highlight how improper connection handling leads to runtime failures in R’s templating workflow.

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.