correction du script d'Hélène

Projet_DataViz_Matrice_Histogramme

This visualization combines a train-station adjacency matrix with a live histogram of journey regularity. It plots TGV and Intercités routes, where each matrix cell’s opacity encodes the percentage of on-time trips, and clicking a cell highlights its row and column. The histogram charts monthly regularity for selected routes using a dynamic bar chart. Built with Vue and D3 v4, the code applies `d3.scaleBand`, `d3.scaleLinear`, and `d3.axisBottom`, while a slider filters data by year. Data comes from CSV files (`trajets_tgv.csv`, `trajets_intercites.csv`) and renders as SVG.

59% match

Projet_DataViz_Matrice_Histogramme

This visualization combines an adjacency matrix with a linked histogram to explore train punctuality. Users select a train type (TGV or Intercité) and click a matrix cell to see monthly regularity data for that route. The matrix uses a color scale to encode weight, while the histogram displays punctuality across months. Built with D3 v4 and Vue, it uses `d3.scaleBand`, `d3.axisTop`, `d3.axisLeft`, and `d3.scaleLinear`, with data loaded via `queue().defer(d3.csv, ...)`. A slider filters the histogram by year, and tooltips show details on hover.

57% match

SNCF_dataviz_corentin

This visualization compares two French railway stations by displaying their available on-site amenities, access modes, and traveler trip purposes side-by-side as small colored rectangles. The code uses D3 v4 (`d3.csv`, selections, and attribute manipulation) to load a dataset of SNCF station statistics, then renders SVG rectangles to encode each categorical variable for both selected stations. Station identifiers are defined in the script, and the tooltip and text elements display station names derived from the underlying CSV data.

CCtuauden

57% match

Projet_DataViz_Matrice_Histogramme3

This block combines a monthly histogram with an adjacency matrix to visualize the punctuality of TGV and Intercité train routes in France. A slider selects the year, and radio buttons switch between train types. The code uses `d3.scaleBand`, `d3.scaleLinear`, and `d3.axisTop` to render the SVG matrix and histograms. Data for stations and routes with punctuality weights is loaded from CSV files, and interacting with the controls re-renders the visualization.

57% 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.

RRCura

Gist e23004e556ab1d1e670720475c9ef0ce

The visualization shows a network of connected nodes, each labeled with a letter, where edges link pairs of nodes. It uses d3.forceSimulation to position nodes, with a drag interaction and a highlighted path between two specific nodes. The rendering relies on SVG elements (circles, lines, and text) updated via d3’s data join. The regex pattern `(?<=\d)\s(?=[A-z])` appears in the code, suggesting it processes input text to extract or filter connections, though the exact source data is not visible.

HHugoberry

Clustered Network

This interactive visualization displays a clustered network using the Les Misérables character co-occurrence data, grouping nodes into colored convex hulls that represent character communities. Each cluster can be expanded or collapsed, with force-directed layout positioning nodes and links between them. The implementation leverages D3 v2’s force layout, SVG rendering, and custom convex hull path generation, with React managing the interface and smooth animations for cluster expansion and collapse.

Ttgu007

Projet_DataViz_Matrice

This example visualizes a network of train connections across France as an adjacency matrix, with each cell shaded to represent the frequency of service between station pairs. The view is built in Vue using SVG, with D3 v4’s `d3.csv` and `queue` loading the node and edge lists. A hover interaction highlights the row and column of a selected station, and the axes use `d3.axisTop` and `d3.axisLeft`.