# ujjwalkarn/DataScienceR

a curated list of R tutorials for Data Science, NLP and Machine Learning

Repository: https://github.com/ujjwalkarn/DataScienceR
Canonical: https://ross.abutalabs.com/products/datasciencer
Language: R
License: MIT
License Family: permissive
Topics: datascience, data-science, r, text-mining
Last push: 2023-03-10T11:06:16+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4347, "days_push": 1272, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2150, forks 878 (observed 2026-08-28T04:06:19.314855+00:00)

## What it is
A curated list of R tutorials and packages for Data Science, NLP, and Machine Learning, serving as a reference guide for common data analysis tasks. It aggregates links to courses, books, blogs, and resources for learning R.

## Use cases
- find tutorials to learn R for data science
- discover R packages for NLP and text mining
- get a reference guide for common data analysis tasks in R
- locate free resources and courses for learning R
- find R resources for machine learning

## When to choose
- you are learning R for data science and want curated starting points
- you need a topic-wise index of R tutorials and resources
- you want links to courses, books, and blogs about R

## When to avoid
- you need executable code or a software library rather than links
- you work primarily in Python or another language
- you need up-to-date or actively maintained content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, data-science, nlp, machine-learning
- domain: data-science, tutorials, machine-learning, awesome-lists
- platform: -
- tags: curated-list, r-language, tutorials, awesome-list, natural-language-processing, r

## Member repositories
- ujjwalkarn/DataScienceR (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:19.314855+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:50:58.371778+00:00, confidence not recorded.
  - readme: https://github.com/ujjwalkarn/DataScienceR (fetched 2026-08-28T04:06:19.314855+00:00, sha 09ea55785e46)
- Data as of 2026-08-30T08:39:29.467469+00:00.