Gist 173470

Gist 6b70052296ccf0ebf338143509429d64

This example simulates a simplified Facebook-like social network in Ruby, using a `User` class to model profiles, friendships, and wall posts. The code defines methods for adding friends and posting content to another user’s wall, checking friendship status before allowing a post, and initializing two user objects with sample data. It uses the `pry` debugging library via `binding.pry` to pause execution and allow interactive inspection of the state, with no graphical output or external visualization library.

JJastor11

59% match

A gist in R showing how to plot twitter data from ScraperWiki as a smoothScatter plot

This example shows the relationship between follower and following counts for a subset of Twitter users, revealing a dense clustering pattern near the origin with sparse outliers at higher values. The R code first retrieves JSON data from a ScraperWiki SQL endpoint, converts it into a data frame using the `rjson` package, and then renders a smoothScatter plot—a base R function that uses hexagonal binning and color density to visualize overlapping points. The plot is restricted to a 0–5000 range on both axes to focus on the majority of users while still exposing the distribution’s shape. The approach highlights typical user behavior rather than individual outliers.

IIanHopkinson

57% match

Gist dfbc4b41961e5b098fffce6af3828f57

This visualization scrapes Instagram profile data to compare follower counts, post engagement, and captions across influencers in different industries. Using Selenium to automate Chrome and BeautifulSoup to parse page JSON, the script extracts post dates, like counts, comments, and partnership flags from each profile’s embedded `_sharedData` blob. It then compiles the results into a pandas DataFrame and exports them to CSV. The example highlights the workflow of collecting social-media metrics at scale, with the final chart likely plotting follower growth or engagement trends per industry.

AArizonaTay

56% match

URL list from Friday, Apr. 17 2015 12:42 PM

This example presents a chronological list of Twitter URLs collected on April 17, 2015, each paired with its author and tweet text. The visualization uses D3.js to render the list as a scrollable feed, highlighting embedded links and media thumbnails. The data comes from a gist authored by TheMapSmith, and the code leverages `d3.select` and `d3.text` to parse the raw markdown file. Tweets are displayed in their original order, with timestamps and usernames formatted via string manipulation. The rendering approach relies on plain HTML elements styled with CSS, avoiding any map or network graphics.

Gist a99d287be62c6d73536521c0e5e74e58

This example scrapes Instagram profile and post data to track follower counts, likes, comments, captions, and post dates for selected users, associating each entry with an industry topic. The script uses Selenium to automate login and search, BeautifulSoup to parse the page source, and the Instagram GraphQL data embedded in `window._sharedData` to extract metrics. It writes the collected records to a CSV via pandas, pausing with random sleeps to avoid detection. The visualization highlights how social-media engagement varies across profiles and industries over time.

AArizonaTay

URL list from Thursday, Jun. 4 2015 11:00 AM

This example visualizes a curated list of URLs shared on Twitter on June 4, 2015, drawn from a gist by TheMapSmith. The display connects each tweet’s text to its linked webpage, using a network layout powered by `d3.forceSimulation` to position nodes. The rendering relies on SVG elements and `d3.geoPath` to draw geographic context, while data is parsed directly from the markdown file. Titles and handles appear as labels, with links encoded as edges between tweet and destination nodes.

URL list from Wednesday, May. 6 2015 12:45 PM

The visualization is a simple text list of URLs shared on Twitter, captured on May 6, 2015. Each line pairs a username and a short tweet excerpt with the corresponding link, rendering the raw content in a plain monospace format. The list uses static HTML with no interactive elements or external libraries, displaying the data as a straightforward, unadorned archive of social-media posts. The source is the gist's raw markup file, which presents each tweet as a hyperlink to its original status.

URL list from Monday, Oct. 5 2015 9:24 AM

This visualization displays a list of URLs shared on Twitter, collected on October 5, 2015. The page presents each link as a textual entry, sorted in reverse chronological order based on their timestamps. The rendering relies on a simple HTML list structure, with each item containing the tweet’s text and its corresponding URL. The visualization uses no external charting libraries; instead, it relies on plain HTML and CSS for layout, with the data sourced directly from a Gist file. The underlying code parses each tweet and displays it as a static, scrollable list.