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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
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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
2. Setting Up Our Data Analysis Environment FREE CHAPTER 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

Data visualization

In this section, we will focus on creating the scatter plots for the given dataset. Creating scatter plots involves new feature analysis with the help of the ggplot2 package:

  1. Include the library in the specified workspace. This involves execution of the following set of commands:
> library('ggplot2')

Attaching package: 'ggplot2'

The following object is masked _by_ '.GlobalEnv':

mpg

Warning message:
package 'ggplot2' was built under R version 3.5.3
> library(readr)
  1. Create the parameters in a systematic way that will help to resize the plots in the way we want:
> options(repr.plot.width = 6, repr.plot.height = 6)
  1. This step involves loading the data in our R workspace. Basically, with this step, we will convert the CSV file into a systematic dataset:
> class(longley)
[1] "data.frame"
"
  1. Once the...
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