Skip to main content
SimilarExport ZIP
100%

Forbes Top Colleges Data Summary

✓ Published1🌍 Public
MMartin Blatz
Last edited Sep 24, 2020
Created on Sep 11, 2020

This visualization displays a simple text summary of the Forbes Top Colleges 2019 dataset, reporting the file size, row count, and column count after loading a remote CSV. It uses D3 v5’s `d3.csv` method to fetch and parse the data from a GitHub gist, then formats the numbers with `d3.csvFormat` and updates a `<pre>` element. The code also includes commented-out alternatives using the Fetch API with `async`/`await` and nested promises, illustrating different asynchronous loading patterns. No charts or graphical encodings are rendered.

AI-generated description

A program that loads and parses some CSV data from the Forbes Top Colleges 2019 table collection available on Kaggle

Original work located at Forbes

Some tasks and exploration options:

  • Are there geographic concentrations of top tier or bottom tier colleges?
  • Does the cost of education correlate with the alumni salary?
  • Will going to a more highly ranked school improve your chances of a better salary?
  • Do any of the top schools represent a "value play?"
  • Does student population correlate with the quality or value of education?

Categorical data of interest:

  • location (city, state),
  • public/private

Ordinal data of interest:

  • rank

Quantitative data of interest:

  • student population
  • net price
  • total annual cost
  • alumni salary
MIT Licensed

Similar vizzes

Loading thumbnail…

Forbes Best Colleges Scatter Plot With Menus

A scatter plot maps Forbes’ 2019 Best Colleges data, plotting schools by two user-selectable attributes chosen from dropdown menus for the X and Y axes. The visualization emphasizes correlations between rank, alumni salary, net price, student population, and total annual cost. Built with React and D3 v5, it uses `scaleLinear`, `extent`, and `csv` to parse and scale the data, rendering SVG circles with tooltips via the `Marks` component. Color differentiates private and public institutions.

MMartin Blatz
74% match
Loading thumbnail…

2019 Index of Economic Freedom

This example displays summary statistics for the 2019 Index of Economic Freedom dataset, showing file size in kilobytes, row count, and column count. The code loads a CSV file from a GitHub gist using `d3.csv` and formats the parsed data with `d3.csvFormat` to calculate the size. It then updates the text content of an HTML `<pre>` element to present the metrics. The example demonstrates basic data loading and parsing with D3 v5, with commented alternatives using `fetch`, `async/await`, and `d3.csvParse`.

Hhotdoujiang
72% match
Loading thumbnail…

US school ranking top 150

This example displays basic metadata about the US school ranking top 150 dataset, showing the file size in kilobytes, row count, and column count in large text. It loads a CSV file from a GitHub gist using `d3.csv` and formats the result with `d3.csvFormat` to calculate the byte size. The page also includes commented-out alternatives using the Fetch API with `async`/`await` and `d3.csvParse`, though the active version relies on D3 v5’s promise-based CSV loading.

GGeniXiong
72% match
Loading thumbnail…

Loading and Parsing CSV Data: College Scorecard Data

This example demonstrates loading and parsing a CSV file from the College Scorecard dataset, displaying the file size in kilobytes, the number of rows, and the number of columns as large text. It uses the D3.js library (v5) and its `d3.csv` method to fetch and parse the remote CSV data asynchronously. The code also includes commented-out alternatives using `fetch`, `async`/`await`, and `d3.csvParse`, as well as a pyramid-of-doom nested fetch approach. The data is sourced from a GitHub-hosted CSV of selected Department of Education variables.

JJCarpenter-WPI
71% match
Loading thumbnail…

Grad_Students Data Table Summary

This example presents a terminal-style summary of graduate student earnings and labor force data from FiveThirtyEight’s “Economic Guide to Picking a College Major.” The visualization displays the dataset’s row and column counts, its size in kilobytes, and the first ten rows as formatted JSON. Using d3.csv to load the data and d3.csvFormat to calculate size, it renders the output as ASCII text in a black page with green monospace styling. The code relies on D3 v5’s selection, CSV parsing, and formatting utilities.

YYF
71% match
Loading thumbnail…

D3 CSV

This example displays a text summary of a CSV file's contents, including its size in kilobytes, the number of rows, and the number of columns. The visualization uses D3's `d3.csv` API with a promise to fetch and parse the data from a remote GitHub Gist URL, then formats the results using `d3.csvFormat`. The output is presented as a simple, large-font text message rendered in an HTML `<pre>` element, demonstrating a minimal workflow for loading and summarizing tabular data with D3 v7.

Ddavebell
71% match
Loading thumbnail…

Loading and Parsing CSV Data: College Scorecard Outcomes Data

This example demonstrates loading and parsing a CSV file from the College Scorecard Outcomes dataset. It displays the raw data size in kilobytes, the number of rows, and the number of columns as large text on a blank page. The code uses the `d3.csv` method with a promise callback to fetch and parse the remote CSV, then reports the results via `d3.csvFormat` to calculate the file size. An alternative commented-out approach uses `fetch`, `async`/`await`, and `d3.csvParse`.

JJCarpenter-WPI
71% match
Loading thumbnail…

Data Table Summary

This example reads a CSV file containing the 2017 Fortune 500 company data and displays a concise summary of the dataset. It shows the total number of data rows, the number of columns, and the approximate file size in kilobytes. The first ten rows are then printed as formatted JSON for inspection. The code uses the d3.csv function to parse the data, d3.csvFormat to calculate the data size, and d3.select to append the text elements to the page.

Wwangjonathan
71% match