Loading and Parsing: Book Publishing Data (CS 573 Assignment #2.1)

100%

Loading and Parsing: Book Publishing Data (CS 573 Assignment #2.1)

✓ Published0🌍 Public

Last edited Sep 30, 2021

Created on Sep 8, 2021

Forked from Loading and Parsing CSV Data

This example displays the loading and parsing of a book publishing dataset, presenting the file size, row count, and column count. It fetches a CSV file from a GitHub gist using the D3.js library, specifically the `d3.csv` function, and computes the file size in kilobytes with `d3.csvFormat`. The resulting information is rendered in a large, green, monospaced text block. The dataset contains over 1000 books and 14 attributes, including publishing year, author, genre, and sales data.

AI-generated description

Book Publishing Dataset

This visualization is a summary of the Book Publishing Dataset. There are over 1000 different books and 14 different attributes in the dataset. This data is being used for my CS 573 Project Proposal.

Orginally Source of Dataset (data.world)

List of Attributes:

Publishing Year: Ordinal
Book Name: Categorical
Author: Categorical
Language: Categorical
Author Rating: Quantitative
Book Average Rating: Quantitative
Book Rating: Quantitative
Genre: Categorical
Grosss Sales: Quantitative
Publisher Revenue: Quantitative
Sales Price: Quantitative
Sales Rank: Ordinal
Publisher: Quantitative
Unit Sold: Quantitative

Questions/Task:

Is there any correlation between an author's rating and the number of units sold?
Are books more likely to be bought if they are published by certain publishers?

(Within some extra data)

Do prestigious literary awards result in larger sales?
Which countries have produced the most influential authors?
How have our cultural taste in literature changed over time?

I would need more data if I were to go with and answer some of these questions/tasks. I have already found a much more extensive book database that would certainly help; however, it is much greater than 5 mBs. I could possibly tried to reduce it down. In the meantime, this database works as a proposal for the project.

MIT Licensed

Similar vizzes

Loading thumbnail…

Loading and Parsing CSV Data - Amazon Bestsellers List Dataset

This example demonstrates loading and parsing a CSV file using D3 v5, displaying the dataset’s size in kilobytes, row count, and column count as large text. It fetches Amazon bestselling books data from a GitHub gist via `d3.csv` and processes it with `d3.csvFormat` to calculate the byte size. The visualization confirms successful data ingestion without rendering charts, focusing solely on the parsing step. The data includes book titles, authors, genres, user scores, prices, years, and review counts from 2009 to 2019.

SSitanshu Rupani

80% match

Loading thumbnail…

Loading And Parsing CSV Data

This example demonstrates the loading and parsing of a CSV file, displaying the data’s size in kilobytes, row count, and column count in large text. It uses D3’s `d3.csv` method to fetch and parse the remote CSV data from a GitHub gist, then formats the output with `d3.csvFormat`. The page is a minimal HTML document with inline CSS and JavaScript, providing a straightforward overview of the dataset’s basic structure.

Kkbrookshire420

77% match

Loading thumbnail…

Loading and parsing CSV data wih D3

This example demonstrates how to load and parse CSV data using D3 v7. It displays the row count, column count, and file size in kilobytes as large text on the page. The code uses `d3.csv()` to fetch and parse a CSV file from a GitHub gist, then processes the resulting data array and columns. It also includes commented-out alternatives using `fetch()` with `async/await` and `d3.csvParse()` to illustrate older parsing approaches.

AAoibhinnRed

77% match

Loading thumbnail…

Loading and Parsing CSV Data with D3

This example demonstrates how to load and parse a CSV file using D3.js, displaying basic dataset statistics. The visualization shows the file size in kilobytes, row count, and column count within a large text element. The code fetches a CSV file containing CSS named colors from a GitHub Gist, uses `d3.csv` to retrieve and parse the data, then `d3.csvFormat` to measure its size, and finally updates the page’s pre-formatted text content. The D3 v5 API handles the asynchronous data loading and parsing.

Bbakstorage00001

77% match

Loading thumbnail…

Loading and Parsing CSV Data with D3

Bbakstorage00001

77% match

Loading thumbnail…

Loading and parsing CSV data

This example demonstrates loading a remote CSV file and parsing it with D3.js, displaying basic dataset statistics as large text. It uses `d3.csv` to fetch and parse the data from a GitHub Gist, then reports the approximate size of the formatted output in kilobytes, the number of rows, and the number of columns. The page also includes commented-out code showing an alternative approach using the `fetch` API with `async`/`await` and `d3.csvParse` for manual text parsing, but the active path relies on D3's built-in CSV handling.

Aadante111

77% match

Loading thumbnail…

Loading Parsing CSV Data

This example demonstrates how to load and parse a CSV file, displaying file size, row count, and column count on a webpage. It uses d3.csv to fetch a remote dataset from a GitHub gist raw URL, then formats the results with d3.csvFormat to calculate kilobytes. The page also includes commented-out code showing an alternative approach using fetch, async/await, and d3.csvParse. A large pre element displays the final statistics as text.

RRaphael Fogha

76% match

Loading thumbnail…

Loading and Parsing CSV Data

This example demonstrates loading and parsing a CSV file with D3, displaying the file size, row count, and column count as large text. It uses d3.csv to fetch the dataset from a GitHub gist of CSS named colors, then relies on d3.csvFormat and data.columns to compute the metrics. The page is a minimal HTML document with a pre element for output, styled with em-based font sizing, and the visualization updates once the asynchronous data loading completes.

CChiedu Egbumokei Jr

76% match