For my Week 1 assignment, I selected the NYSERDA Electric Vehicle Drive Clean Rebate Data: Beginning 2017. The dataset was published by the New York State Energy Research and Development Authority and contains information about vehicle manufacturers, models, electric ranges, rebate amounts, and completed rebate applications in New York State. I selected this dataset because I am an electrical engineer, I am passionate about electric vehicles, and I want to understand how New York’s rebate program supports their purchase or lease.
My main question is: How do vehicle manufacturer, model, and electric range relate to the rebate incentives offered through the Drive Clean Rebate program? I plan to load the data directly from the New York State Open Data website, review its structure, select the variables that are relevant to my question, and give the columns clear names. I will then compare manufacturers, vehicle models, electric ranges, and rebate amounts using summaries and, if useful, a table or visualization.
I anticipate that the dataset may contain missing values, repeated records, inconsistent manufacturer or model names, and columns that are not needed for my analysis. Some vehicles may also have different electric ranges or rebate amounts, which could make direct comparisons more difficult. I will inspect the data before analyzing it, document any cleaning decisions, and make sure that every step can be reproduced using data downloaded from a URL.
After reviewing the available columns, I found that this dataset does not include a direct electric-range variable. Therefore, I adjusted the analysis to focus on vehicle manufacturers, models, EV types, transaction types, and rebate amounts. Electric range could be added in a future analysis by combining this dataset with another reliable vehicle-data source.
The data is downloaded directly from the New York State Open Data website so that the analysis is reproducible.
data_url <- "https://data.ny.gov/api/views/thd2-fu8y/rows.csv?accessType=DOWNLOAD"
ev_raw <- read_csv(
data_url,
col_types = cols(
ZIP = col_character()
)
)
dim(ev_raw)## [1] 241983 11names(ev_raw)## [1] "Data through Date"
## [2] "Submitted Date"
## [3] "Make"
## [4] "Model"
## [5] "County"
## [6] "ZIP"
## [7] "EV Type"
## [8] "Transaction Type"
## [9] "Annual GHG Emissions Reductions (MT CO2e)"
## [10] "Annual Petroleum Reductions (gallons)"
## [11] "Rebate Amount (USD)"OpenAI. (2026). ChatGPT [Large language model]. https://chat.openai.com/. Accessed September 2, 2026.
ChatGPT was used to help interpret the assignment requirements, organize the planned approach, edit the English writing, and provide step-by-step guidance. I reviewed the final text to ensure that it accurately represents my interests and intended analysis.