A review of R for Data Science: key elements and a critical analysis
Bibliographic record
Abstract
A detailed review of a recent data science book by Hadley Wickham and Garrett Grolemund is developed herein. Technical book reviews should provide a guide to the readers, a sense of the appropriate audience, the specifics of the software/language, and identify critical thinking questions that emerge through reading the specifics of these books. This is a pre-print, extended version of a review of 'R for Data Science', and it provides a relatively comprehensive framing of this particular book. The context and background of the authors is introduced, key elements of this book - primarily the workflow proposed and the value of the tidyverse - are summarized, and a critical analysis of the book was done. The following critical questions were addressed in the review. (1) Does this book (or any data science book for that matter) effectively communicate basic versus advanced data science concepts to the reader? (2) Does this book extend or improve upon previous resources particularly for the individual in- terested in using and learning data science to do statistics in R? (3) Can this book be read as a general data science book and by extension how much is this an R versus RStudio book? The importance of reading a book associated with tools one uses in computer science such as R versus rapid, online solution-based reading is very effectively established in 'R for Data Science'. Time spent with a technical book providing the big picture for the tools one uses to solve problems in R is important for deeper learning and insights.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".