Skip to main content
SimilarExport ZIP
100%

1. Diabetes Self-Management Data

✓ Published0🌍 Public
Oomniaabouhassan@gmail.com
Last edited Sep 10, 2024
Created on Sep 4, 2024

This example displays the first record of the Diabetes Self-Management dataset as a formatted JSON object in a large monospace font. The code parses a CSV file, converting age and year to numbers and transforming diabetes and heart disease statuses into booleans. It uses the `JSON.stringify` method to serialize the data, which is then inserted into the container's innerHTML within a `<pre>` tag. The visualization provides a raw data preview, highlighting the dataset's structure and initial content.

AI-generated description

The Diabetes Self-Management Data, loaded and parsed as CSV.

This dataset contains responses from participants in Austin, Texas, who completed a diabetes self-management program. It includes various health indicators and socioeconomic factors.

Tasks

  1. Compare Glucose Levels Across Different Age Groups:

    • Visualize and compare the average glucose levels of individuals across different age groups to understand how glucose management varies with age.

  2. Analyze Blood Pressure Trends:

    • Track changes in blood pressure levels over time or across different medical conditions to identify trends or anomalies.

  3. Explore the Distribution of Diabetes Medication Use:

    • Examine how different types of diabetes medications are distributed among individuals to see which medications are most commonly used.

  4. Correlate Physical Activity with Diabetes Management Metrics:

    • Investigate the relationship between physical activity levels and diabetes management metrics like HbA1c levels to determine if increased activity correlates with better diabetes control.

  5. Identify Subgroups with High Risk Factors:

    • Use the dataset to identify subgroups of individuals who exhibit high-risk factors for poor diabetes management, such as high glucose levels or low physical activity.

  6. Investigate the Impact of Socioeconomic Factors on PAID Scores:

    • Analyze how socioeconomic factors, such as income level or insurance type, influence the PAID scores.
MIT Licensed

Similar vizzes

Loading thumbnail…

Diabetes Data

This example presents a scatter plot of the Pima Indians Diabetes Dataset, visualizing diagnostic measurements such as glucose levels, BMI, and age to explore their relationship with diabetes outcomes. The visualization is built using D3.js (d3.v7) and employs the `select` and `csvParse` APIs to load and structure the CSV data, which is then converted from strings to numeric values. The rendering displays the dataset as raw JSON text within a `<pre>` element, offering a straightforward, data-centric view of the 768 patient records without additional charting layers.

Sskasliwal@wpi.edu
77% match
Loading thumbnail…

Diabetes Data

This example loads a CSV dataset on diabetes in adult females from a GitHub gist and compares the number of text bytes, rows, and columns in the file. It reads the data using the `d3.csv` request method, then formats it with `d3.csvFormat` to measure its size in kilobytes, and displays these figures in a large `<pre>` element on the page. The visualization is a simple textual summary, not a chart, highlighting the dataset’s dimensions for comparative analysis of medical predictor variables. The data originates from the Pima Indians Diabetes Database on Kaggle.

AAnurag Desai
76% match
Loading thumbnail…

Fork of Loading Data - Diabetes Data

This example loads a diabetes dataset from a CSV file and displays its raw contents as a JSON-formatted text block. The visualization focuses on the data-loading process, using a state machine to transition from an undefined state to a loading state and finally to the parsed data. The code parses the CSV using D3’s `csvParse` function and converts numeric fields from strings to numbers. The data, sourced from Kaggle, is rendered by fetching the file and displaying the parsed array in a `<pre>` element.

Rrpkupfer@wpi.edu
74% match
Loading thumbnail…

Alzheimer's Disease Patient Data

This visualization displays the first patient record from a dataset containing demographic, lifestyle, medical history, and cognitive assessment information for Alzheimer's disease research. The code loads data from a CSV file, converts all numeric fields (including age, BMI, MMSE scores, and blood pressure readings) from strings to numbers, then renders the resulting data as formatted JSON within a container. The example uses JavaScript's `JSON.stringify` method for formatting, while the `container.innerHTML` assignment directly injects the pre-formatted output into the HTML document. This provides a raw data preview rather than a chart, showing columns like patient ID, age, gender, and diagnosis status.

BBashir Gulistani
74% match
Loading thumbnail…

Fork of diabeties data

This visualization presents survey data on diabetes awareness, showing the first ten rows of the dataset with details on the number of rows, columns, and file size. The page uses D3.js to load a CSV file from a GitHub gist, while the background displays a static JPEG image of a diabetes-related graphic. The code employs D3's `csv` method for data parsing and displays the information as text elements within the SVG, styled with custom CSS. The visualization is built with Vue and rendered using SVG, with D3 version 5.

VVineela Reddy
73% match
Loading thumbnail…

Medical Insurance Data

This example displays a loaded medical insurance dataset as raw JSON in a `<pre>` element, showing each record’s fields such as age, BMI, smoking status, and charges. The visualization uses D3 v7’s `csvParse` to read the `data.csv` file and `select` to bind the parsed array to the page. It also demonstrates a simple state-management pattern with `setState` for asynchronous data loading.

CCyn
73% match
Loading thumbnail…

heart attack

This example displays the first record of a healthcare appointment dataset as a formatted JSON object, revealing patient attributes such as age, blood pressure, cholesterol, and treatment type. The code loads the data from a CSV file using an imported `data.csv` module, converts numeric fields to numbers, and renders the output in a `<pre>` element with a custom font size. The visualization relies on vanilla JavaScript and DOM manipulation rather than external charting libraries.

Aalexwd019@gmail.com
73% match
Loading thumbnail…

COVID-19 Dataset

This example displays the first row of the COVID-19 worldwide dataset as a formatted JSON object in a large monospaced font. It uses the `index.js` module to import CSV data, coerce numeric fields, and render the raw statistics for a single country. The visualization relies on a simple HTML `<pre>` element updated via template literals, with no charting library involved.

JJack Hanlon
73% match