This assignment demonstrates how the same dataset can be represented in different formats (HTML, XML, JSON) and how each can be read back into R for comparison. The example uses three personal books to show how structure and purpose vary across formats while maintaining consistent information.

This dataset for this project can be classified as untidy because the columns have multiple variables and the rows have multiple observations. In this project I will tidy this dataset by transforming the data from a wide to a long format that is easier for a data analyst or data scientist to work with.

HTML

Linear transformations

This proposal explores how grocery pricing and store availability differ between low-income and high-income neighborhoods in New York City. Focusing on Brownsville (Brooklyn) and Lower Manhattan, the project investigates whether essential items like eggs and milk are more expensive or less accessible in areas with higher poverty rates. The analysis uses a placeholder dataset for now, with plans to incorporate real data from NYC Open Data, USDA Food Access Research Atlas, and the U.S. Census for the final project.