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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

Table of Contents (17) Chapters

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

Summary

In this chapter, we have focused on the implementation of all libraries of a multi-factor dataset, which is strongly linked to various data type values. The best demonstration is to check various parameters of automobiles, especially with reference to mpg, displacement, and acceleration, including weight parameters. We have listed some of the various packages that are available for reading, in various kinds of attributes, within the dataset specified in R. There are lots of different options, and even the options we have listed have a wide functionality that we are going to cover and use as we go further into this book.

In the next chapter, we will introduce a dataset from the regression problem category and explain how to use exploratory data analysis techniques to analyze this data.

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