Automated Data Ingestion Pipelines, Web Scraping, and API-Driven Telemetry Harvesting: An R-Based Programmatic Data Collection Framework

This project engineered a scalable, programmatic data collection and extraction pipeline using R to automate the ingestion of multi-source structured and unstructured data. Utilizing packages within the tidyverse ecosystem—alongside rvest for HTML DOM parsing, httr/httr2 for RESTful API interactions, and jsonlite for nested payload parsing—the pipeline systematically harvested, transformed, and validated web-based datasets. To overcome dynamic web structures and network bottlenecks, custom error-handling routines using tryCatch, rate-limiting throttles, and automated pagination handling were implemented. Extracted raw data streams were sanitized using pattern matching (stringr), wrangled into tidy relational formats using tidyr and dplyr, and audited for schema integrity before export. The resulting automated workflow established a reproducible foundation for downstream statistical modeling and empirical research.