The d3 documentation provides an illustration of how a CSV file can be parsed into an array of objects. Here is a sample CSV file:
Year,Make,Model,Length
1997,Ford,E350,2.34
2000,Mercury,Cougar,2.38
This results in the following JavaScript array:
[
{"Year": "1997", "Make": "Ford", "Model": "E350", "Length": "2.34"},
{"Year": "2000", "Make": "Mercury", "Model": "Cougar", "Length": "2.38"}
]
(source: https://github.com/d3/d3-dsv/blob/master/README.md#dsv_parse)
This means that within your data set, the first element would resemble the following structure:
[
{
"State": "AL",
"Under 5 Years": "310504",
"5 to 13 Years": "552339",
"14 to 17 Years": "259034",
"18 to 24 Years": "450818",
"25 to 44 Years": "1231572",
"45 to 64 Years": "1215966",
"65 Years and Over": "641667"
}
]
An alternative approach could involve converting Bostock's CSV into JSON using a tool like a CSV to JSON converter (such as this one I've used before, and many others are available). This converted JSON data can then be utilized in your local programming code.
If you opt for utilizing D3's CSV loading functions, accessing data from a local file could be complex due to cross-origin request restrictions without a simple server setup.
Regarding running commands in node.js, it may be beneficial to include your JavaScript code and JSON object in a '.js' file and link it to an HTML file, or alternatively embed the JavaScript directly within the HTML if the project is straightforward (similar to Bostock's approach in the linked visualization).