The R data science reading list you will actually bookmark

What it does This repository is a curated index of external R tutorials, packages, documentation, and Stack Overflow threads. It buckets links by theme—learning fundamentals, common dataframe operations, the caret package, time series, multivariate analysis, and text mining—so you do not have to hunt the internet for canonical answers. The maintainer also runs parallel lists for Python and general machine learning, suggesting this is a personal knowledge-management system that happens to be public.

The interesting bit The value is not in the code—there is almost none—but in the editorial judgment of which Stack Overflow threads and Hadley Wickham references are worth remembering. It treats the README as a living syllabus, mixing official cheatsheets with niche packages like freqparcoord.

Key highlights

• Heavy emphasis on practical data manipulation: subsetting, merging, dropping NAs, and factor-level cleanup.
• Dedicated sections for the caret modeling framework and time-series forecasting.
• Curated “Important Questions” that address R-specific footguns: bracket vs. subset, sapply vs. lapply, memory management, and the eternal = vs. <- debate.
• Links to canonical texts: R for Data Science, Advanced R, and the swirl in-R tutorial system.
• Reference slides covering association rules, text mining on Twitter data, and regression/classification workflows.

Caveats

• Most links point to external sites with no archival copies, so rot is a real risk.
• The README is a flat link dump with minimal annotation; you still have to click through to judge quality.
• No stated inclusion criteria, so “curated” here mostly means “collected.”

Verdict Bookmark this if you are learning R and want a veteran practitioner’s trail of breadcrumbs through the ecosystem. Skip it if you need executable code or a structured course; this is a map, not a vehicle.