GGPlot Cleveland Dot Plot

100%

GGPlot Cleveland Dot Plot

✓ Published0🌍 Public

Last edited Mar 31, 2014

Created on Mar 31, 2014

This ggplot2 example visualizes the distribution of words spoken by each character in Shakespeare’s *Much Ado about Nothing*, using a Cleveland dot plot. The chart maps character names on the y-axis (reordered by count) and word frequency on the x-axis, with points colored by gender. The code builds the plot with `ggplot()` and `geom_point()`, applies a black-and-white theme (`theme_bw()`), and customizes grid lines via `theme()` with `element_line()`. The data comes from a `shakes` data frame containing per-speaker word counts and gender attributes.

AI-generated description

Similar vizzes

Loading thumbnail…

A brief R script to generate tag clouds of the primary words used by characters (in the case, Leonato) in Shakespeare's Much Ado about Nothing.

This example visualizes the most frequent words spoken by Leonato in *Much Ado about Nothing* as a tag cloud, highlighting his primary vocabulary through font size and color. The R script reads a CSV file containing character dialogue data, filters for Leonato’s lines, and uses the `wordcloud` library to render the terms, while `RColorBrewer` supplies the accent color palette. The code relies on base R functions like `read.csv` and `subset` to prepare the data, with the `wordcloud` function’s `min.freq` parameter controlling which words appear based on their occurrence count.

CCliffordAnderson

66% match

Loading thumbnail…

Gist fd8a31ea3830d17cacb585fc7313b388

This example visualizes the character relationships and scene structure of Shakespeare’s *Hamlet* as a directed network graph. It shows how characters connect across acts and scenes, with nodes representing acts, scenes, and characters, and edges indicating their narrative co-occurrence. The visualization uses a force-directed layout, likely implemented with `d3.forceSimulation`, to position nodes, while the GraphML data is parsed to build the graph. Scene and act nodes are color-coded and labeled by their act/scene numbers, and character nodes carry names, revealing the play’s structural flow. The rendering relies on a browser-based SVG or Canvas context, with the underlying data sourced from the included `ham-graphml.xml` file.

CCliffordAnderson

60% match

Loading thumbnail…

Produces word frequency list for speakers in Much Ado about Nothing.

This example uses XQuery 3.0 with the TEI namespace to generate a per-speaker word frequency list from Shakespeare’s *Much Ado about Nothing*. It processes the TEI-encoded XML document, iterating over distinct speakers and counting each word’s occurrences within their spoken lines, then outputs a sequence of `<word>` elements with the speaker, type, count, and weighted frequency. The query relies on `fn:distinct-values`, `fn:count`, and an `order by` clause to rank terms.

CCliffordAnderson

60% match

Loading thumbnail…

Gist ea92be31b5382266079042e50d4f7de6

This example visualizes a dataset of comedians stored in a CSV file, plotting each person as a node in a force-directed graph. It shows the relationship between name, city, and email by positioning nodes using `d3.forceSimulation`, with the city serving as a categorical grouping. The rendering relies on D3’s selection and data-binding methods to draw circles and text labels, while the CSV is loaded via `d3.csv`. The code does not include interactive features or additional layout constraints; it simply maps the three fields into a basic spatial arrangement, emphasizing the distribution of comedians across cities.

WWill Carter

54% match

Loading thumbnail…

3D Scatter Plot - Wine

This example visualizes three wine sample groups in a 3D scatter plot, comparing measurements of malic acid, ash, and alcalinity of ash. Each group is represented by a distinct color—red, blue, and green—with semi-transparent markers to reveal overlapping points. The code loads CSV data via `d3.csv` from a GitHub gist and renders the interactive plot using Plotly’s `Plotly.newPlot` function with a `scatter3d` trace type. Axis titles label each dimension, and a legend identifies the wine samples.

Ssenthilrajendran1

54% match

Loading thumbnail…

Gist 9cf3726653a19fb89c2574bdde940323

This example visualizes the character co-occurrence network from *Les Misérables*, grouping nodes by community and bundling edges to reveal hierarchical structure. It adapts an Observable notebook to run directly in a browser via a JavaScript module, loading data with `d3-require`. Using `d3.cluster` for layout and `d3.radialLine` with `d3.curveBundle`, it draws curved paths between characters with a steelblue, semi-transparent stroke and multiply blending. Labels are rotated and positioned radially around the 640-pixel square SVG.

Mmbostock

54% match

Loading thumbnail…

Fork for SDV assn 6 M&K

This scatter plot visualizes the relationship between how often a character is picked and their total professional wins, with each circle representing a data point. The code loads CSV data from a GitHub gist using `d3.csv` and renders the chart as SVG via d3.v7. It uses `d3.scaleLinear` for both axes, appends circles with a fixed radius of 5 pixels, and styles them with a teal fill. Axis labels, a centered title, and rotated y-axis text are added using `svg.append`, with all margins and dimensions explicitly defined.

BBlueT

53% match

Loading thumbnail…

WordCloud Example in R

This example generates a word cloud from a CSV file containing words and their frequencies, filtering to only include terms appearing more than 100 times. The visualization uses the R `wordcloud` library to arrange and scale the text by frequency, while applying an "Accent" color palette from `brewer.pal`. The code reads the data with `read.csv` and subsets it with base R logical indexing before rendering the cloud with `wordcloud()`, displaying the words where size and color are directly mapped to their occurrence counts.

CCliffordAnderson

53% match