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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 learned about the benefits that EDA can bring to businesses across various verticals. We introduced the R packages that will be used in this book to teach concepts related to EDA. We also learned how to set up and install these packages using both the Terminal and RStudio.

The next chapter will demonstrate practical, hands-on code examples that show how to handle reading all kinds of data into R for EDA. We will cover how to use advanced options while importing datasets, including delimited data, Excel data, JSON data, and data from web APIs. We will also look at how to scrape and read in data from the web and how to connect to relational databases from R. We will use R packages such as readr, readxl, jsonlite, httr, rvest, and DBI.

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