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Simulation for Data Science with R

You're reading from   Simulation for Data Science with R Effective Data-driven Decision Making

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781785881169
Length 398 pages
Edition 1st Edition
Languages
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Author (1):
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Matthias Templ Matthias Templ
Author Profile Icon Matthias Templ
Matthias Templ
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction 2. R and High-Performance Computing FREE CHAPTER 3. The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions 4. Simulation of Random Numbers 5. Monte Carlo Methods for Optimization Problems 6. Probability Theory Shown by Simulation 7. Resampling Methods 8. Applications of Resampling Methods and Monte Carlo Tests 9. The EM Algorithm 10. Simulation with Complex Data 11. System Dynamics and Agent-Based Models Index

Visualizing information

In many chapters, results are visualized using the graphics capabilities of R. Thus, we will give a very short introduction to the base graphics package, plus a short introduction to the package ggplot2 (Wickham, 2009).

The reader will learn briefly about the graphical system in R, different output formats for traditional graphics system, customization and fine tuning of standard graphics, and the ggplot2 package.

Tip

Other packages such as ggmap, ggvis, lattice, or grid are not touched on here. Interactive graphics are also beyond the scope of this book (Google Charts, rgl, iplots, JavaScript, and R).

The graphics system in R

Many packages include methods to produce plots. Generally, they either use the functionality of the base R package called graphics or the functionality of the package grid.

For example, the package maptools (Bivand and Lewin-Koh, 2015) includes methods for mapping; with this package one can produce maps. It uses the capabilities of the graphics package...

You have been reading a chapter from
Simulation for Data Science with R
Published in: Jun 2016
Publisher: Packt
ISBN-13: 9781785881169
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