USA Leading Causes of Death Dataset

100%

USA Leading Causes of Death Dataset

✓ Published0🌍 Public

Last edited Sep 8, 2024

Created on Sep 2, 2024

This example displays the first row of the NCHS Leading Causes of Death dataset as a JSON object in a large monospace font. The code imports the CSV data, converts numeric fields to numbers, and then renders the parsed record using a template literal in an HTML `<pre>` tag. It uses the `data` export and a `main` function to populate the container, though no charting library is employed. The visualization provides a raw, static view of the dataset structure rather than an interactive graphic.

AI-generated description

`The NCHS - Leading Causes of Death: United States.

This data contains the leading causes of death in the US by year, both with the raw number of deaths and adjusted by age.

Note: The CSV data was slightly modified (adding "_"s to field names instead of spaces/dashes) to make parsing easier.

Tasks:

I want to understand how the leading death causes vary by state
I want to understand how the age-adjusted death rate correlates with total deaths geographically
I want to see how the leading death cause has changed over time
I want to see how the age-adjusted death rate correlates to deaths with different death causes
I want to see how the total number of deaths across all regions has changed over time

MIT Licensed

Similar vizzes

Loading thumbnail…

CSV Dataset - Mortality Data

This example displays the first record of the Health Synergy Hospital Mortality CSV dataset as pretty-printed JSON. It shows the parsed data types and date formatting applied to the raw input. The code imports the CSV file, converts numeric fields and reformats the date to a consistent month/day/year string. The visualization simply renders this processed single row inside an HTML `<pre>` element, using JavaScript's `Date` object for parsing.

Nnitinramagiri1@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

72% match

Loading thumbnail…

Annual Deaths Causes in USA

An interactive scatter plot displays annual death counts from numerous causes in the USA, with users selecting any two columns from the dataset to compare via dropdown menus. The visualization uses D3 v5’s `scaleLinear`, `axisBottom`, and `axisLeft` to render animated SVG circles that transition into position. Data is loaded from a CSV file hosted on VizHub and includes causes such as Dementia, Cardiovascular diseases, and Suicide. The code employs a reusable `scatterPlot` module and a custom `dropdownMenu` component to handle user interactions.

CChooo4u

72% match

Loading thumbnail…

Covid Cases and Deaths WorldWide

This example displays the first row of a COVID-19 dataset as a JSON object, showing total cases, deaths, recoveries, active cases, tests, and population for a single country. It imports the data from a CSV file and parses the numeric fields by removing commas. The code uses the native `JSON.stringify` method to format the data and displays it within an HTML `<pre>` element, with the font size controlled by a JavaScript variable. The dataset originates from a Kaggle source on worldwide COVID-19 cases and deaths.

Wwkj0713@gmail.com

69% match

Loading thumbnail…

Annual deaths causes in usa

This scatter plot displays annual deaths from various causes in the USA over time, with the y-axis showing total deaths for a selected cause and the x-axis representing years. The visualization uses D3 v5’s `scaleTime`, `scaleLinear`, `axisBottom`, and `axisLeft` to render the chart as an SVG. A dropdown menu, built with a custom `dropdownMenu` function, is intended to switch between cause-specific datasets, though the selection logic is not implemented. The data is loaded from a CSV file and parsed into numeric death counts for each condition.

CChooo4u

69% match

Loading thumbnail…

Causes of Death

This interactive visualization explores mortality trends in the United States through a linked tree navigator and stacked area chart. The tree navigator organizes causes of death hierarchically, and selecting a node updates the stacked area chart to display the proportional composition of causes over time, with each layer colored distinctly. The visualization uses D3’s stack layout with an expand offset to show relative proportions, while the tree provides an intuitive way to drill into specific causes of death. Data is sourced from the Centers for Disease Control.

CCurran Kelleher

69% match

Loading thumbnail…

1. Diabetes Self-Management Data

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.

Oomniaabouhassan@gmail.com

68% 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

67% match