Introduction

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 completed rebate applications with information about vehicle manufacturers, models, counties, EV types, transaction types, estimated emissions reductions, and rebate amounts. 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.

Planned Approach

My main question is: How do vehicle manufacturer, model, EV type, and transaction type relate to the number and amount of Drive Clean rebates issued in New York State? I downloaded the original data from the New York State Open Data website and stored a fixed copy in my public GitHub repository to make the analysis reproducible. I will review the data structure, select the variables relevant to my question, and assign clear column names. I will then compare the number of completed rebate applications, total rebate dollars, and average rebate amounts across manufacturers, vehicle models, EV types, and transaction types using summary tables and visualizations.

Anticipated Data Challenges

I anticipate that the dataset may contain missing values in some categorical variables and records that appear similar because multiple customers can receive rebates for the same vehicle model. Because the dataset does not include a unique application identifier, I will not automatically remove repeated-looking rows. I will also convert the submission date into a proper date format, document all cleaning decisions, and use a public GitHub URL so that the analysis can be reproduced.

Data Loading and Scope

After reviewing the available columns, I found that the dataset does not include a direct electric-range variable. Therefore, I adjusted the analysis to focus on vehicle manufacturers, models, EV types, transaction types, completed rebate applications, and rebate amounts. Electric range could be added in a future analysis by combining this dataset with another reliable vehicle-data source.

The original CSV was downloaded from the New York State Open Data website and stored as a fixed file in my public GitHub repository. The R code reads the CSV through its GitHub Raw URL so that the analysis can run in another environment without using a local file path.

data_url <- paste0(
  "https://raw.githubusercontent.com/",
  "howtwo388-cyber/DATA607-Week1-Electric-Vehicles/",
  "main/data/ev_drive_clean_rebates.csv"
)

ev_raw <- read_csv(
  data_url,
  col_types = cols(
    ZIP = col_character()
  )
)

dim(ev_raw)
## [1] 241983     11
names(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)"

AI Use

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.