I prompted ChatGPT to generate datasets containing three tables: Borrowers, Loans, and Market Risks. The goal was to create a realistic data structure for analyzing lending and market risk factors, enabling me to assess relationships between borrower profiles, loan characteristics, and market conditions.

Pipe operators like %>% have revolutionized my data processing in R, making my code cleaner and more intuitive. They enable straightforward, left-to-right chaining of operations, enhancing readability and efficiency. From converting factors to numeric values and handling side effects with %T>%, to seamlessly transitioning between data manipulation and visualization with dplyr and ggplot2, these operators have streamlined my workflow. The use of placeholders, functional sequences, and compound assignment with %<>% has further simplified repetitive tasks, allowing me to focus more on analyzing the data itself.