In my exploration of handling CSV files, I focused on key parameters like file paths, headers, separators, and handling missing data. I found read.csv in base R convenient for its defaults, but I appreciated the readr package's read_csv for faster performance and better control over data types. The data.table package's fread impressed me with its speed and flexibility, guessing delimiters and variable types automatically. For exporting, I relied on write.csv for simplicity, while write_csv from readr offered efficiency and better formatting. Managing multiple CSV files became streamlined with list.files and lapply, allowing easy combination into a single data frame. Fixed-width files posed unique challenges, but read.fwf in base R and read_fwf from readr helped me handle them effectively by specifying or guessing column widths, enhancing both speed and flexibility. Overall, each tool provided valuable techniques for efficient data manipulation.

ExamNumbers

In this project, I explored the Medicare dataset using R, focusing on the split() function to analyze data by Plan_Type. I identified top patients based on treatment costs and computed correlations between age, treatment costs, and hospital visits. Each step provided valuable insights, revealing patterns and relationships within the data. The process was both challenging and rewarding, as I overcame obstacles and honed my analytical skills. Ultimately, this journey deepened my understanding of data analysis, reinforcing my passion for uncovering meaningful stories through data.