Wickham H., Grolemund G. (2016). R for data science: import, tidy, transform, visualize, and model data. Sebastopol (Etats-Unis) : O'Reilly. 492 p.
https://r4ds.had.co.nz/
https://r4ds.had.co.nz/
Titre : | R for data science: import, tidy, transform, visualize, and model data |
Auteurs : | H. Wickham ; G. Grolemund |
Type de document : | E-Book |
Editeur : | Sebastopol [Etats-Unis] : O'Reilly, 2016 |
ISBN/ISSN/EAN : | 978-1-4919-1039-9 |
Format : | 492 p. |
Langues : | Anglais |
Catégories : |
Catégories principales 12 - EDUCATION. FORMATION. INFORMATION. GESTION DES SAVOIRS ; 12.5 - Formation Continue, Professionnelle. Gestion des CursusThésaurus IAMM LOGICIEL ; DONNEE STATISTIQUE ; ANALYSE DE DONNEES ; TRAITEMENT DES DONNEES ; MODELE ; LANGAGE |
Résumé : |
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way.
Youll learn how to: Wrangletransform your datasets into a form convenient for analysis Programlearn powerful R tools for solving data problems with greater clarity and ease Exploreexamine your data, generate hypotheses, and quickly test them Modelprovide a low-dimensional summary that captures true "signals" in your dataset Communicatelearn R Markdown for integrating prose, code, and results |
Note de contenu : |
I. Explore
1. Data Visualization with ggplot2 2. Workflow: Basics 3. Data Transformation with dplyr 4. Workflow: Scripts 5. Exploratory Data Analysis 6. Workflow: Projects II. Wrangle 7. Tibbles with tibble 8. Data Import with readr 9. Tidy Data with tidyr 10. Relational Data with dplyr 11. Strings with stringr 12. Factors with forcats 13. Dates and Times with lubridate III. Program 14. Pipes with magrittr 15. Functions 16. Vectors 17. Iteration with purrr IV. Model 18. Model Basics with modelr 19. Model Building 20. Many Models with purrr and broom V. Communicate 21. R Markdown 22. Graphics for Communication with ggplot2 23. R Markdown Formats 24. R Markdown Workflow |
Cote : | Réservé lecteur CIHEAM |
URL / DOI : | https://r4ds.had.co.nz/ |
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