This assignment explores HTML (HyperText Markup Language) and JSON (JavaScript Object Notation) as two common formats for organizing and storing data. Using three books on personal finance and financial literacy, the project demonstrates how to create HTML and JSON files, import them into R as data frames using the rvest and jsonlite packages, and compare the results to determine whether both formats contain identical information.

HTML (HyperText Markup Language) and JSON (JavaScript Object Notation) are important technologies for organizing,

The focus of Project 2 is to practice the R packages tidyr and dplyr to prepare three different datasets for downstream analysis. Data Tidying and Transformation, incorporating your existing data-cleaning code and adding two important techniques: 1. Window functions: row_number(), min_rank(), dense_rank(), percent_rank(), lag(), lead(), and cumsum(). 2. Feature engineering: Creating new variables from existing variables to support downstream statistical analysis. The code works with your three datasets: AI Usage, College Majors, and Student Dropout Risk. Because the actual column names have not been provided, the code uses numerical variables that exist in each dataset rather than assuming specific columns are available.

This project analyzes arrival delays for two airlines, Alaska Airlines and AM West, across five destinations: Los Angeles, Phoenix, San Diego, San Francisco, and Seattle.

This is my Week 1 Code Base resubmitted

After watching Carol's presentation, I was able to use ChatGPT to submit this update for the WEEK 2 A assignment.

Week 2B submission

Please disregard Week 2B for now.

This is an update of Week 2B

This is a late submission for Week 2 B

Assignment 3B Window Functions: Code Base Submission

Global Baseline Estimate: Codebase Submission • Work with strings and dates in R • Use regular expressions