Cotton R. (2013). Learning R: a step-by-step function guide to data analysis. Sebastopol (Etats-Unis) : O'Reilly. 377 p.
Titre : | Learning R: a step-by-step function guide to data analysis |
Auteurs : | R. Cotton |
Type de document : | E-Book |
Editeur : | Sebastopol [Etats-Unis] : O'Reilly, 2013 |
ISBN/ISSN/EAN : | 978-1-4493-5710-8 |
Format : | 377 p. |
Langues : | Anglais |
Langues du résumé : | 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 |
Résumé : |
Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, youll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.
The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what youve learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code |
Note de contenu : |
I. The R Language
1. Introduction 2. A Scientific Calculator 3. Inspecting Variables and Your Workspace 4. Vectors, Matrices, and Arrays 5. Lists and Data Frames 6. Environments and Functions 7. Strings and Factors 8. Flow Control and Loops 9. Advanced Looping 10. Packages 11. Dates and Times II. The Data Analysis Workflow 12. Getting Data 13. Cleaning and Transforming 14. Exploring and Visualizing 15. Distributions and Modeling 16. Programming 17. Making Packages III. Appendixes A. Properties of Variables B. Other Things to Do in R C. Answers to Quizzes D. Solutions to Exercises E. Bibliography |
Cote : | Réservé lecteur CIHEAM |