loading_data_NH_2_solar_data

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loading_data_NH_2_solar_data

✓ Published1🌍 Public

Last edited Jan 24, 2023

Created on Jan 23, 2023

This visualization displays daily solar power generation data from multiple sites, comparing metrics like watt hours and expected output across different locations. It loads a CSV file containing site information, dates, power measurements, and geographic coordinates, parsing the data with `d3.csvParse`. The visualization renders the raw data as a JSON-formatted pre element using `d3.select` and `selectAll().join()`, showing the data in its text form. The code demonstrates a simple data-loading state machine that fetches the CSV, parses it into typed numeric and string columns, and displays the structured result.

AI-generated description

address, site, and department are categorical data site_id,date,watt_min,watt_max,watt_avg,watt_hours,watt_hours_expected,latitude, and longitude are quantitative data

Solar data for power generation collected from a few sources, namely an array of solar panels at Bloomington Parks & Rec

how do certain areas compare for solar power generation?

https://www.kaggle.com/datasets/mattop/daily-solar-power-generation?resource=download

MIT Licensed

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