An end to end R pipeline modeling the relationship between COVID-19 case surges and national diagnostic device shortages (e.g., sodium citrate blood collection tubes). Combines JHU CSSE epidemiological time-series data with FDA medical device shortage data acquired via web scraping (rvest/httr) to overcome bot-blocking. Applies cross correlation (CCF) time-series analysis to identify a 5-day lag between case spikes and official shortage reporting insight informed by my background managing coagulation instrument supply chains in clinical laboratories during the pandemic. Built using R (tidyverse, rvest, httr, lubridate) following a reproducible data pipeline architecture (Obtain → Scrub → Explore → Model → Interpret, OSEMN framework).

HTML and JSON practice in R

DATA 607 Project 2: One of the three wide and untidy data sets in this project formatted into a long and tidy format as part of group work. This tidy data set was then used for exploratory statistical analysis regarding performance between sex, relative lift strength to bodyweight, and max weightlift trend over time.

DATA 607 Assignment 5B Elo expected score calculation using cleaned up chess tournament data set clean from a text file in Project 1

DATA 607 Assignment 5A analysis

Assignment 3B 607

CUNY SPS Assignment 3

Malaria Dataset derived from kaggle. Data set was cleaned and visualized with scatterplot to show relation of subsets.