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Hands-On Exploratory Data Analysis with R

You're reading from  Hands-On Exploratory Data Analysis with R

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789804379
Pages 266 pages
Edition 1st Edition
Languages
Authors (2):
Radhika Datar Radhika Datar
Profile icon Radhika Datar
Harish Garg Harish Garg
Profile icon Harish Garg
View More author details
Toc

Table of Contents (17) Chapters close

Preface 1. Section 1: Setting Up Data Analysis Environment
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

Residual plots

A residual plot is a graph that displays the residuals on the vertical axis specified and the independent variable on the horizontal axis. In the event that the points in a residual plot are dispersed in a random manner around the horizontal axis, it is appropriate to use a linear regression model for the data. If the data is not dispersed, a non-linear model is more appropriate.

We can create and customize the residual plot using the steps mentioned as follows:

  1. Include the library within the R workspace. This is considered a mandatory step:
> 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
  1. Use the required function, such as we did for scatter plots, to get the perpendicular plots arranged in a systematic manner:
  2. ...
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