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The Statistics and Machine Learning with R Workshop

You're reading from   The Statistics and Machine Learning with R Workshop Unlock the power of efficient data science modeling with this hands-on guide

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803240305
Length 516 pages
Edition 1st Edition
Languages
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Author (1):
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Liu Peng Liu Peng
Author Profile Icon Liu Peng
Liu Peng
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Table of Contents (20) Chapters Close

Preface 1. Part 1:Statistics Essentials
2. Chapter 1: Getting Started with R FREE CHAPTER 3. Chapter 2: Data Processing with dplyr 4. Chapter 3: Intermediate Data Processing 5. Chapter 4: Data Visualization with ggplot2 6. Chapter 5: Exploratory Data Analysis 7. Chapter 6: Effective Reporting with R Markdown 8. Part 2:Fundamentals of Linear Algebra and Calculus in R
9. Chapter 7: Linear Algebra in R 10. Chapter 8: Intermediate Linear Algebra in R 11. Chapter 9: Calculus in R 12. Part 3:Fundamentals of Mathematical Statistics in R
13. Chapter 10: Probability Basics 14. Chapter 11: Statistical Estimation 15. Chapter 12: Linear Regression in R 16. Chapter 13: Logistic Regression in R 17. Chapter 14: Bayesian Statistics 18. Index 19. Other Books You May Enjoy

Geometries in graphics

The previous section mostly covered scatter plots. In this section, we will go over two additional common types of plots: bar charts and line plots. We will discuss different ways to construct these plots, focusing on the geometries that can be used to control layer-specific visual properties of the graph.

Understanding geometry in scatter plots

Let us revisit the scatter plot and zoom in on the geometry layer. The geometry layer determines how the plot actually looks, which is an essential layer in our visual communication. At the time of writing, there are over 50 geometries we can choose from, all of which start with the geom_ keyword.

Some overall guidelines apply when deciding which type of geometry to use. For example, the following list contains the possible kinds of applicable geometries for a typical scatter plot:

  • Point, which visualizes the data as points
  • Jitter, which adds positional jittering to a scatter plot
  • Abline, which...
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