Class 607, Project 2 Data Transformations

Author

Troy Tournat

Published

2026-11-08

Instructions here: https://brightspace.cuny.edu/d2l/le/content/1404182/viewContent/41373795/View

Introduction

Data sources: All datasets originated from students in Discussion #5a.

Dataset 1 from student Patricio Romero: “For this discussion, I selected the Electric Consumption and Cost dataset from NYC Open Data. This dataset contains electrical energy information from New York City Housing Authority (NYCHA) buildings.

Analysis request: compare electricity consumption between NYC boroughs and see how electricity use changes over time.

Data Source: NYC Open Data – Electric Consumption And Cost (2010–Sep 2025)

Dataset Link: https://data.cityofnewyork.us/Housing-Development/Electric-Consumption-And-Cost-2010-Sep-2025-/jr24-e7cr

Dataset 2 from student Renee Watson: “fictional dataset containing monthly patient visits for community health clinics during 2025

Analysis request: I would examine how patient visit volume changes throughout the year and compare these patterns across the five clinics. I could determine which clinics experienced the largest increases or decreases in patient visits and examine whether there are noticeable monthly patterns in patient volume. A visualization could also be used to compare trends across the clinics.”  

Dataset 3 from student Kingsley Kpoku: Fictional NYC housing prices dataset for the five New York Boroughs .The dataset is on 2024 and 2025.

Analysis request: The whole idea is to analyze and study the behavioral patterns of the averages prices across the months and Years and serve as a real estate analyst to advice potential customers on the most expensive areas in NewYork, what people can afford based on salary and when is the best time to move to a borough when the prices drop (based on historicals)

Tackling the problem: In all three datasets I’ll need to extract the wide, messy data in different ways from online (dataset #1), the discussion body (dastaset #2), or downloaded (dataset #3). Then, I’ll import them all in R and begin reshaping and I’ll review the different analysis requests to determine what more is needed for preparation. Finally, I’ll tackle the analysis questions with a summary table, visualization, and short narrative.

Anticipated data challenges: Dataset1 seems like it will be the most challenging because I don’t know anything about how to analyize energy consumption, and the timespan is 15 years, so it requires deduplicating and sifting through a lot of data. I also anticipate all the datacleaning across the three datasets to be challenging.

Citations:

Analysis