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Hands-On Exploratory Data Analysis with R

You're reading from   Hands-On Exploratory Data Analysis with R Become an expert in exploratory data analysis using R packages

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789804379
Length 266 pages
Edition 1st Edition
Languages
Tools
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Authors (2):
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Radhika Datar Radhika Datar
Author Profile Icon Radhika Datar
Radhika Datar
Harish Garg Harish Garg
Author Profile Icon Harish Garg
Harish Garg
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Setting Up Data Analysis Environment FREE CHAPTER
2. Setting Up Our Data Analysis Environment 3. Importing Diverse Datasets 4. Examining, Cleaning, and Filtering 5. Visualizing Data Graphically with ggplot2 6. Creating Aesthetically Pleasing Reports with knitr and R Markdown 7. Section 2: Univariate, Time Series, and Multivariate Data
8. Univariate and Control Datasets 9. Time Series Datasets 10. Multivariate Datasets 11. Section 3: Multifactor, Optimization, and Regression Data Problems
12. Multi-Factor Datasets 13. Handling Optimization and Regression Data Problems 14. Section 4: Conclusions
15. Next Steps 16. Other Books You May Enjoy

Exploring graphically the dataset

In this section, we will focus on exploring the dataset graphically using a DOE scatter plot, a DOE mean plot, a DOE standard deviation plot, and a contour plot. Let's focus on each of them in turn:

  1. In this step, we will depict the scatter plot in two ways. A scatter plot shows the relationship between wt and mpg as follows:
> plot(Autompg$weight , Autompg$mpg, xlab = 'Weight of Cars', ylab = 'Miles per Gallon', main = 'Scatter Plot for MTCars Weight Vs MPG')

This gives us the following output plot:

The alternative way to depict the scatter plot is with the help of the ggplot2 package or library, which is achieved by executing the following command:

> library(ggplot2)
> ggplot(data=Autompg,aes(x=weight, y=mpg)) + geom_point() + theme_minimal()

This gives us the following output plot:

  1. This step...
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