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R for data science : import, tidy, transform, visualize, and model data /
"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"--
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media,
2016.
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Wickham, Hadley, |e author. | |
| 245 | 1 | 0 | |a R for data science : |b import, tidy, transform, visualize, and model data / |c Hadley Wickham & Garrett Grolemund. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, |c 2016. | |
| 264 | 4 | |c ©2017 | |
| 300 | |a 1 online resource : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed December 22, 2016). | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Copyright; Table of Contents; Preface; What You Will Learn; How This Book Is Organized; What You Won't Learn; Big Data; Python, Julia, and Friends; Nonrectangular Data; Hypothesis Confirmation; Prerequisites; R; RStudio; The Tidyverse; Other Packages; Running R Code; Getting Help and Learning More; Acknowledgments; Online Version; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Part I. Explore; Chapter 1. Data Visualization with ggplot2; Introduction; Prerequisites; First Steps; The mpg Data Frame; Creating a ggplot; A Graphing Template; Exercises. | |
| 505 | 8 | |a Aesthetic MappingsExercises; Common Problems; Facets; Exercises; Geometric Objects; Exercises; Statistical Transformations; Exercises; Position Adjustments; Exercises; Coordinate Systems; Exercises; The Layered Grammar of Graphics; Chapter 2. Workflow: Basics; Coding Basics; What's in a Name?; Calling Functions; Exercises; Chapter 3. Data Transformation with dplyr; Introduction; Prerequisites; nycflights13; dplyr Basics; Filter Rows with filter(); Comparisons; Logical Operators; Missing Values; Exercises; Arrange Rows with arrange(); Exercises; Select Columns with select(); Exercises. | |
| 505 | 8 | |a Add New Variables with mutate()Useful Creation Functions; Exercises; Grouped Summaries with summarize(); Combining Multiple Operations with the Pipe; Missing Values; Counts; Useful Summary Functions; Grouping by Multiple Variables; Ungrouping; Exercises; Grouped Mutates (and Filters); Exercises; Chapter 4. Workflow: Scripts; Running Code; RStudio Diagnostics; Exercises; Chapter 5. Exploratory Data Analysis; Introduction; Prerequisites; Questions; Variation; Visualizing Distributions; Typical Values; Unusual Values; Exercises; Missing Values; Exercises; Covariation. | |
| 505 | 8 | |a A Categorical and Continuous VariableExercises; Two Categorical Variables; Exercises; Two Continuous Variables; Exercises; Patterns and Models; ggplot2 Calls; Learning More; Chapter 6. Workflow: Projects; What Is Real?; Where Does Your Analysis Live?; Paths and Directories; RStudio Projects; Summary; Part II. Wrangle; Chapter 7. Tibbles with tibble; Introduction; Prerequisites; Creating Tibbles; Tibbles Versus data.frame; Printing; Subsetting; Interacting with Older Code; Exercises; Chapter 8. Data Import with readr; Introduction; Prerequisites; Getting Started; Compared to Base R; Exercises. | |
| 505 | 8 | |a Parsing a VectorNumbers; Strings; Factors; Dates, Date-Times, and Times; Exercises; Parsing a File; Strategy; Problems; Other Strategies; Writing to a File; Other Types of Data; Chapter 9. Tidy Data with tidyr; Introduction; Prerequisites; Tidy Data; Exercises; Spreading and Gathering; Gathering; Spreading; Exercises; Separating and Pull; Separate; Unite; Exercises; Missing Values; Exercises; Case Study; Exercises; Nontidy Data; Chapter 10. Relational Data with dplyr; Introduction; Prerequisites; nycflights13; Exercises; Keys; Exercises; Mutating Joins; Understanding Joins; Inner Join. | |
| 520 | |a "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"-- |c Provided by publisher | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Electronic data processing. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 0 | |a Databases. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Data mining. | |
| 650 | 1 | 2 | |a Data Mining |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| 650 | 7 | |a COMPUTERS |x Hardware |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Information Technology. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Machine Theory. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Reference. |2 bisacsh | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946 | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Databases. |2 fast |0 (OCoLC)fst00888065 | |
| 650 | 7 | |a Electronic data processing. |2 fast |0 (OCoLC)fst00906956 | |
| 650 | 7 | |a R (Computer program language) |2 fast |0 (OCoLC)fst01086207 | |
| 650 | 7 | |a R |g Programm |2 gnd | |
| 700 | 1 | |a Grolemund, Garrett, |e author. | |
| 776 | 0 | 8 | |i Print version: |z 9781491910368 |
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