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Graphing data with R : an introduction /

"It's much easier to grasp complex data relationships with a graph than by scanning numbers in a spreadsheet. This introductory guide shows you how to use the R language to create a variety of useful graphs for visualizing and analyzing complex data for science, business, media, and many o...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Hilfiger, John Jay (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, Inc., 2015.
Edición:First edition.
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Hilfiger, John Jay,  |e author. 
245 1 0 |a Graphing data with R :  |b an introduction /  |c John Jay Hilfiger. 
250 |a First edition. 
264 1 |a Sebastopol, CA :  |b O'Reilly Media, Inc.,  |c 2015. 
264 4 |c ©2016 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
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588 0 |a Online resource; title from PDF title page (EBSCO, viewed October 28, 2015). 
500 |a Includes index. 
505 0 |a Preface; Who Is This Book For?; Why R?; How to Use This Book; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments; Part I. Getting Started with R; Chapter 1. R Basics; Downloading the Software; Try Some Simple Tasks; User Interface; Installing a Package: A GUI Interface; Data Structures; Sample Datasets; The Working Directory; Putting Data into R; Typing into a Command Line; Using the Data Editor; Reading from an External File; Sourcing a Script; User-Written Functions; A Taste of Things to Come. 
505 8 |a Exercise 1-4 Chapter 2. An Overview of R Graphics; Exporting a Graph; Exploratory Graphs and Presentation Graphs; Graphics Systems in R; Base Graphics and grid; lattice; ggplot2; Special Applications/Graphs Incorporated into Packages; User-Written Graphic Functions; Part II. Single-Variable Graphs; Chapter 3. Strip Charts; A Simple Graph; Data Can Be Beautiful; Exercise 3-1; Exercise 3-2; Chapter 4. Dot Charts; Basic Dot Chart; Exercise 4-1; Exercise 4-2; Chapter 5. Box Plots; The Box Plot; Nimrod Again; Making the Data Beautiful; Exercise 5-1; Exercise 5-2; Chapter 6. Stem-and-Leaf Plots. 
505 8 |a Basic Stem-and-Leaf PlotExercise 6-1; Exercise 6-2; Chapter 7. Histograms; Simple Histograms; Histograms with a Second Variable; Exercise 7-1; Exercise 7-2; Chapter 8. Kernel Density Plots; Density Estimation; Choosing a Bandwidth; Comparing Two or More Density Plots; A Background That Is Not White; The Cumulative Distribution Function; Exercise 8-1; Exercise 8-2; Chapter 9. Bar Plots (Bar Charts); Basic Bar Plot; Spine Plot; Bar Spacing and Orientation; Exercise 9-1; Exercise 9-2; Chapter 10. Pie Charts; Ordinary Pie Chart; Fan Plot; Exercise 10-1; Exercise 10-2; Chapter 11. Rug Plots. 
505 8 |a The Rug Plot Exercise 11-1; Part III. Two-Variable Graphs; Chapter 12. Scatter Plots and Line Charts; Basic Scatter Plots; Line Charts; Templates; Enhanced Scatter Plots; Exercise 12-1; Exercise 12-2; Chapter 13. High-Density Plots; Working with Large Datasets; Sunflower Plot; Smooth Scatter Plot; Exercise 13-1; Chapter 14. The Bland-Altman Plot; Assessing Measurement Reliability; Exercise 14-1; A Shorter Version of baplot(); Chapter 15. QQ Plots; Comparing Sets of Numbers; Exercise 15-1; Exercise 15-2; Part IV. Multivariable Graphs; Chapter 16. Scatter plot Matrices and Corrgrams. 
505 8 |a Scatter plot MatrixCorrgram; Generalized Pairs Matrix with Mixed Quantitative and Categorical Variables; Exercise 16-1; Chapter 17. Three-Dimensional Plots; 3D Scatter plots; False Color Plots; Bubble Plots; Exercise 17-1; Exercise 17-2; Chapter 18. Coplots (Conditioning Plots); The Coplot; Exercise 18-1; Chapter 19. Clustering: Dendrograms and Heat Maps; Clustering; Heat Maps; Exercise 19-1; Exercise 19-2; Exercise 19-3; Chapter 20. Mosaic Plots; Graphing Categorical Data; Exercise 20-1; Part V. What Now?; Chapter 21. Resources for Extending Your Knowledge of Things Graphical and R Fluency. 
520 |a "It's much easier to grasp complex data relationships with a graph than by scanning numbers in a spreadsheet. This introductory guide shows you how to use the R language to create a variety of useful graphs for visualizing and analyzing complex data for science, business, media, and many other fields. You'll learn methods for highlighting important relationships and trends, reducing data to simpler forms, and emphasizing key numbers at a glance. Anyone who wants to analyze data will find something useful here--even if you don't have a background in mathematics, statistics, or computer programming. If you want to examine data related to your work, this book is the ideal way to start."--  |c Provided by publisher. 
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650 2 |a Data Mining 
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650 6 |a Visualisation de l'information. 
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