This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
El Objetivo del paquete aprendeR es facilitar que nuevas personas puedan R moderno con una curva de aprendizaje baja, y evitando que el inglés sea una barrera para que se puedan centrar en el aprendizaje competencial de R. Se incluyen traducciones al castellano de tutoriales presentes en otros paquetes (learnr, tutorial.helpers, r4ds.tutorials, ...), y eventualmente nuevos tutoriales más adelante.
Natercia Valle tells a cautionary tale about the use of learning analytics dashboards to increase student motivation, and the challenges of translating theory into design solutions.
March 20, 2022
T. Junk, and L. Lyons. (2020)cite arxiv:2009.06864Comment: 50 pages, 6 figures. Please see https://hdsr.mitpress.mit.edu/pub/32yz0u49/release/1 for a thoughtful comment by Andrew Fowlie, and https://hdsr.mitpress.mit.edu/pub/57tywz64/release/1 for the authors' response.
S. Andreon, and M. Hurn. (2012)cite arxiv:1210.6232Comment: Invited review on "Statistical Analysis and Data Mining", a referred journal of the American Statistical Association. In press.
M. Sereno. (2015)cite arxiv:1509.05778Comment: 13 pages; LIRA package available from https://cran.r-project.org/web/packages/lira/index.html; further material at http://pico.bo.astro.it/~sereno/; v02: 14 pages, typos corrected, added references to change point analysis. In press on MNRAS.
A. Mantz. (2015)cite arxiv:1509.00908Comment: 11 pages, 5 figures, 2 tables. Code is available on GitHub at https://github.com/abmantz/lrgs and from CRAN.