Skip to main content
SimilarExport ZIP
100%

Loading and Parsing Healthcare Dataset Stroke CSV Data

✓ Published0🌍 Public
Aaishwarya8615
Last edited Sep 25, 2019
Created on Sep 4, 2019

This example displays a summary of the parsed healthcare stroke dataset, showing its size in kilobytes, row count, and column count as large text. It uses d3.csv to fetch and parse the CSV file from a GitHub raw URL, then computes the file size with d3.csvFormat and reports the data's dimensions. The visualization serves as a preliminary loading step, verifying the data's structure before any further analysis.

AI-generated description

Healthcare Dataset Stroke Data

Data Source: Healthcare Dataset Stroke Data is used. This is the Github Data Link for the Healthcare Dataset Stroke Data

Context: This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, and various diseases and smoking status. I have taken a subset of the original test data using the filtering method for Data Visualization purposes.

Motive: What age group, gender or smoking status are more likely to get stroke and how do they compare with respect to these factors? Is there any correlation between age and stroke?

About the Data: Output/Occurance of stroke is a categorical variable. 2 of the inputs(gender and smoking status) are categorical and ordinal respectively and age is numerical.

Notes: Unknown in Smoking status means the information is unavailable N/A in other input fields imply that it is not applicable.

MIT Licensed

Similar vizzes

Loading thumbnail…

Loading and Parsing diabetes risk CSV Data

This example loads a diabetes risk prediction dataset from UCI’s machine learning repository, sourced from Sylhet Hospital in Bangladesh, and displays its size in kilobytes, row count, and column count. The visualization uses d3.csv to fetch and parse the CSV file, then d3.csvFormat to calculate the file size, with the summary rendered in a large-text <pre> element. It also includes commented-out alternatives using fetch, async/await, and d3.csvParse, demonstrating different ways to handle asynchronous data loading.

Llakalia@wpi.edu
77% match
Loading thumbnail…

Loading and Parsing the CDC Dataset

This example displays a text summary of a CDC health dataset’s size and structure, showing the file’s kilobyte count, row count, and column count in large type. After loading the CSV from a GitHub gist via `d3.csv`, it uses `d3.csvFormat` on the resulting parsed data to calculate its byte length, then writes the formatted message into a `<pre>` element using standard DOM manipulation. The code relies on D3 v7’s promise-based CSV loading and formatting APIs.

RRidhima Saxena
76% match
Loading thumbnail…

Heart Disease

This visualization displays summary statistics for a heart disease dataset sourced from Kaggle.com, showing the file size in kilobytes, row count, and column count in large blue text. The code uses d3.csv to load the CSV file from a GitHub Gist, then d3.csvFormat to calculate the data size and accesses the data's length and columns properties to extract the metrics. It demonstrates both async/await with fetch and a promise-based d3.csv approach, though only the latter is active in the current version.

RRyanYun0017
76% match
Loading thumbnail…

Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data with D3, displaying basic metadata about the dataset. After fetching the CSV file from a GitHub gist, it computes and presents the file size in kilobytes, the total number of rows, and the column count. The visualization uses D3's `d3.csv` method to asynchronously fetch and parse the data, followed by `d3.csvFormat` to measure the raw text length. The resulting statistics are rendered as large text in an HTML `<pre>` element, with alternative commented-out approaches using `fetch`, `async`/`await`, and `d3.csvParse`.

LLei Lei
76% match
Loading thumbnail…

Loading and Parsing the Breast Cancer Dataset

This example demonstrates the initial step of working with the breast cancer dataset by loading and parsing it, then displaying basic dataset statistics. The page shows the file size in kilobytes, the total number of rows, and the number of columns. It uses D3 v7’s `d3.csv` to fetch the CSV data from a GitHub gist URL, and `d3.csvFormat` to calculate the raw data size. The resulting metrics are rendered as plain text inside a `<pre>` element, providing a concise overview of the dataset’s structure.

RRidhima Saxena
75% 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
75% match
Loading thumbnail…

Loading and Parsing CSV Data

This example displays a large text readout showing the size, row count, and column count of a CSV file loaded from a GitHub gist. It uses d3.csv to fetch and parse the remote dataset, then computes the formatted size with d3.csvFormat and inspects the resulting array’s length and columns property. The visualization is purely textual, rendered into a <pre> element, demonstrating basic data loading and parsing workflow in D3 v5.

KKaytheman99
75% match
Loading thumbnail…

Loading and Parsing CSV Data

This example demonstrates how to load and parse CSV data using D3 v5, displaying file size, row count, and column count in a large pre-formatted text element. The code uses `d3.csv()` to fetch and parse the dataset from a GitHub-hosted CSV file containing NYC social media usage data, then formats the statistics with `d3.csvFormat()` to calculate kilobyte size. An alternative commented-out approach uses the Fetch API with `async`/`await` and `d3.csvParse()` for manual parsing. The example concisely shows the core workflow of retrieving and inspecting tabular data in a browser environment.

KKarenNg
75% match