What can 151 Taylor Swift songs reveal about content, timing, and audience strategy? This project blends lyrical text mining, seasonality trends, and YouTube release analytics to uncover surprising patterns—like which albums skew explicit, how often “love” and “midnight” appear, and how long it takes for a song to reach fans via video. With a mix of data wrangling, visual storytelling, and date logic, this analysis showcases the power of combining pop culture with data science.

This report explores two core datasets: one focused on educational attainment and income in the United States, and the other on global annual mean temperatures. Using R, I apply data wrangling, custom function creation, visualization, and classification techniques to uncover trends and insights across both domains.